Cognito - User contributions [en]

User:Vipul/Sandbox

2024-10-07T06:37:15Z

Vipul:

* <math>\sqrt{7 + 2}!! + 3 = 723</math>
* <math>(7 + 2)^{\sqrt{9}} = 729</math>
* <math>5^{1 + 2} = 125</math>
* <math>6^{2 + 1} = 216</math>
* <math>(3 + 4)^3 = 343</math>
* <math>\sqrt{7 + 2}!! + 5 = 725</math>

User:Vipul/Sandbox

2024-10-07T06:17:39Z

Vipul:

* <math>\sqrt{7 + 2}!! + 3 = 723</math>
* <math>(7 + 2)^{\sqrt{9}} = 729</math>
* <math>5^{1 + 2} = 125</math>
* <math>6^{2 + 1} = 216</math>
* <math>(3 + 4)^3 = 343</math>

MediaWiki:Sitenotice

2024-09-05T23:36:14Z

Vipul:

'''This wiki is associated with [http://cognitomentoring.org Cognito Mentoring], an advising service for learners that was run by Jonah Sinick and [http://www.vipulnaik.com Vipul Naik]. The advising service is no longer running and the wiki is not being actively updated; however, we'll try to respond to feedback and suggestions regarding the content.''' '''Please [http://www.cognitomentoring.org/connect connect with us] to offer feedback on the wiki content.'''

MediaWiki:Sitenotice

2024-09-05T23:35:58Z

Vipul:

'''This wiki is associated with [http://cognitomentoring.org Cognito Mentoring], an advising service for learners that was run by [http://www.jonahsinick.com Jonah Sinick] and [http://www.vipulnaik.com Vipul Naik]. The advising service is no longer running and the wiki is not being actively updated; however, we'll try to respond to feedback and suggestions regarding the content.''' '''Please [http://www.cognitomentoring.org/connect connect with us] to offer feedback on the wiki content.'''

User:Vipul/Sandbox

2024-09-05T23:34:58Z

Vipul:

* <math>\sqrt{7 + 2}!! + 3 = 723</math>
* <math>(7 + 2)^{\sqrt{9}} = 729</math>
* <math>5^{1 + 2} = 125</math>
* <math>6^{2 + 1} = 216</math>

User:Vipul/Sandbox

2024-09-05T23:28:36Z

Vipul:

* <math>\sqrt{7 + 2}!! + 3 = 723</math>
* <math>(7 + 2)^{\sqrt{9}} = 729</math>
* <math>5^{1 + 2} = 125</math>

User:Vipul/Sandbox

2024-09-05T23:22:51Z

Vipul:

* <math>\sqrt{7 + 2}!! + 3 = 723</math>
* <math>(7 + 2)^{\sqrt{9}} = 729</math>

MediaWiki:Sitenotice

2024-09-05T23:12:53Z

Vipul:

'''This wiki is in the process of being upgraded. The site may go down intermittently. Please try to avoid editing until this notice has been removed.''' 
'''This wiki is associated with [http://cognitomentoring.org Cognito Mentoring], an advising service for learners that was run by [http://www.jonahsinick.com Jonah Sinick] and [http://www.vipulnaik.com Vipul Naik]. The advising service is no longer running and the wiki is not being actively updated; however, we'll try to respond to feedback and suggestions regarding the content.''' '''Please [http://www.cognitomentoring.org/connect connect with us] to offer feedback on the wiki content.'''

MediaWiki:Sitenotice

2024-07-27T04:47:04Z

Vipul:

User:Vipul/Sandbox

2024-07-22T00:34:45Z

Vipul: Created page with "* <math>\sqrt{7 + 2}!! + 3 = 723</math>"

* <math>\sqrt{7 + 2}!! + 3 = 723</math>

Learn mathematics well

2022-12-27T22:37:22Z

Vipul: /* Online math learning resources */

==Key takeaways==

* If you had sufficient time and resources, a case may be made that you should learn all subjects in the curriculum well. But that's not possible in the real world. We still strongly recommend that you consider learning mathematics well, in addition to the subjects that fascinate you.
* In general, a number of people do not learn math as well as their potential. Not everybody can become a genius at mathematics, but most people (including you) can get a lot better than they currently are. A lot of people mistakenly believe, or affirm, statements of the form "I'm not a math person" and these prevent them from achieving their potential.
* If the resources at your school are not helpful (for instance, you have a bad teacher or peer learning environment) there are still many ways for you to learn math well. Some strategies are described and linked on this page.
* Learning mathematics conceptually is hardest, but it generates greater benefits than learning mathematical techniques through rote memorization and practice. However, ''even the latter'' may be quite helpful to you relative to not learning the subject at all. If you are thorough with the computational processes, it would make it easier for you to learn math at a conceptual level later when you have access to quality teachers and resources.

==Why learn math?==

{{further|[[Learning mathematics: benefits]]}}

The following are some general reasons for learning mathematics well.

# '''Hierarchical''': Later material in the curriculum depends heavily on earlier material (with a few important exceptions, such as many parts of geometry, plus advanced techniques within individual sections).
# '''Unidirectional transfer to many other domains''': Mathematics, at both the computational and conceptual level, gets used extensively in physics, chemistry, economics, and the social sciences. Mathematics, up to and including multivariable calculus and linear algebra, is particularly important for understanding statistics rigorously.
# '''Importance for college gateway examinations (such as SAT and ACT)''': A strong background in mathematics helps with the math (quantitative) part of the SAT, as well as with the SAT Math Subject Test. It also helps with the math part of the ACT if you choose to take that test. The situation is likely similar in many other countries.
# '''General reasoning ability''': Mathematics, if understood well, can help with general reasoning.

==Psychological barriers to learning math well==

Many people experience [http://en.wikipedia.org/wiki/Mathematical_anxiety mathematical anxiety] and believe that they're not well-suited to doing mathematics well, independently of their skill level. This belief can hinder their capacity to develop mathematical mastery.

* It is certainly the case that some people have stronger cognitive skills and can learn mathematics faster. However, this does not mean that other people will be unable to learn math. There are many strategies for people to overcome limitations with working memory or processing speed in order to learn math well.
* People often have the impression that others who seem to grasp a new idea conceptually and deeply somehow do so naturally and effortlessly. However, the people who have a strong mathematical intuition generally spend a lot of time thinking about math in general in order to develop that intuition. You may not want to reach that level yourself if math does not fascinate you enough to put in the effort. But you can reap good rewards by moving somewhat in the direction.

==How can one learn math well?==

There are some general [[good study habits]] that help with learning any subject well. There are also some general [[online mathematics learning resources]]. On this page, we describe some aspects of the strategies and resources that are specific to mathematics.

===Supplementing rote and immediate practice===

In many cases, students have access to straightforward descriptions of the procedure they need to apply to solve a particular class of mathematical problem. They read the process, look at a worked example, then try a few examples of their own. They find that their answers are correct, and conclude that they have learned the subject.

There are several problems with this type of learning:

* The practice is being done immediately after reading the procedure and looking at the worked examples. This means that the student may be learning the material using his/her short term memory rather than long term memory. He/she may well forget the technique and be unable to do problems of the same type just a few days later.
* The textbook from which they are obtaining the problems may have deliberately designed the worked problem, and other problems to match each other very well. So students who learn in this way may be thrown off by similar but slightly different problems that they encounter later .
* The student may not obtain any conceptual understanding of the material, so he/she may be completely unable to use the ideas behind the procedure in different contexts, or reconstruct the procedure in the event that he/she forgets it.

The following approaches can help you avoid these pitfalls:

* Maintain some spacing being doing a worked problem (where you follow indicated steps) and doing practice problems. In some cases, you may prefer to do one practice problem immediately after the worked problem, and a few more after a few days. This is a special case of the more general strategy of [[spaced repetition]].
* When you attempt practice problems a few days after doing the worked problem, pay particular attention to the parts that you are stuck at. These are probably the parts that you have conceptual trouble with. ''Do not'' just open up the worked problem again without making a serious effort.
* Attempt practice problems that are framed somewhat differently from the original worked problem. Most books have large numbers of exercises to choose from, and good teachers generally give a varied mix of problems, but pay conscious attention to this issue if you are studying by yourself, or if your teacher isn't giving you a varied mix.
* Wherever possible, try to acquire the conceptual intuition behind the steps. The texts or resources you use often do offer conceptual explanations, but these may not be highlighted because the authors expect that most students just want to master the algorithms and do not care about the explanations.

===Online mathematics learning resources===

{{further|[[Online mathematics learning resources]]}}

Note that websites often add new features and improve existing ones, so the information here may not be up-to-date.

{| class="sortable" border="1"
! Resource !! Link to website !! Videos explaining the content? !! Text explanations of concepts? || Practice problems sorted by area? !! Dependencies between topics explicitly codified?
|-
| [[ALEKS]] || [http://www.aleks.com www.aleks.com] || No || No || Yes || Yes, done at a considerable level of detail using knowledge spaces.
|-
| [[Khan Academy]] || [http://www.khanacademy.org khanacademy.org] || Yes; comprehensive coverage but not high quality || No || Yes || Yes, but this is a new feature and may not be very thorough.
|-
| [[Brilliant]] || [http://www.brilliant.org brilliant.org] || No || No (with a few exceptions) || Yes || No (though they may be doing this soon)
|-
| [[PatrickJMT]] || [http://www.patrickjmt.com patrickjmt.com] || Yes || No || No || No
|-
| [[Edia]] || [https://edia.app/ edia.app] || Yes (as of January 2021; the videos are sourced from third parties) || Yes (text explanation of each problem) || Yes || No (as of January 2021)
|}

===Math reading recommendations===

See our [[math reading recommendations]] page.

Learn mathematics well

2022-12-27T19:36:23Z

Vipul: /* Online math learning resources */

==Key takeaways==

* If you had sufficient time and resources, a case may be made that you should learn all subjects in the curriculum well. But that's not possible in the real world. We still strongly recommend that you consider learning mathematics well, in addition to the subjects that fascinate you.
* In general, a number of people do not learn math as well as their potential. Not everybody can become a genius at mathematics, but most people (including you) can get a lot better than they currently are. A lot of people mistakenly believe, or affirm, statements of the form "I'm not a math person" and these prevent them from achieving their potential.
* If the resources at your school are not helpful (for instance, you have a bad teacher or peer learning environment) there are still many ways for you to learn math well. Some strategies are described and linked on this page.
* Learning mathematics conceptually is hardest, but it generates greater benefits than learning mathematical techniques through rote memorization and practice. However, ''even the latter'' may be quite helpful to you relative to not learning the subject at all. If you are thorough with the computational processes, it would make it easier for you to learn math at a conceptual level later when you have access to quality teachers and resources.

==Why learn math?==

{{further|[[Learning mathematics: benefits]]}}

The following are some general reasons for learning mathematics well.

# '''Hierarchical''': Later material in the curriculum depends heavily on earlier material (with a few important exceptions, such as many parts of geometry, plus advanced techniques within individual sections).
# '''Unidirectional transfer to many other domains''': Mathematics, at both the computational and conceptual level, gets used extensively in physics, chemistry, economics, and the social sciences. Mathematics, up to and including multivariable calculus and linear algebra, is particularly important for understanding statistics rigorously.
# '''Importance for college gateway examinations (such as SAT and ACT)''': A strong background in mathematics helps with the math (quantitative) part of the SAT, as well as with the SAT Math Subject Test. It also helps with the math part of the ACT if you choose to take that test. The situation is likely similar in many other countries.
# '''General reasoning ability''': Mathematics, if understood well, can help with general reasoning.

==Psychological barriers to learning math well==

Many people experience [http://en.wikipedia.org/wiki/Mathematical_anxiety mathematical anxiety] and believe that they're not well-suited to doing mathematics well, independently of their skill level. This belief can hinder their capacity to develop mathematical mastery.

* It is certainly the case that some people have stronger cognitive skills and can learn mathematics faster. However, this does not mean that other people will be unable to learn math. There are many strategies for people to overcome limitations with working memory or processing speed in order to learn math well.
* People often have the impression that others who seem to grasp a new idea conceptually and deeply somehow do so naturally and effortlessly. However, the people who have a strong mathematical intuition generally spend a lot of time thinking about math in general in order to develop that intuition. You may not want to reach that level yourself if math does not fascinate you enough to put in the effort. But you can reap good rewards by moving somewhat in the direction.

==How can one learn math well?==

There are some general [[good study habits]] that help with learning any subject well. There are also some general [[online mathematics learning resources]]. On this page, we describe some aspects of the strategies and resources that are specific to mathematics.

===Supplementing rote and immediate practice===

In many cases, students have access to straightforward descriptions of the procedure they need to apply to solve a particular class of mathematical problem. They read the process, look at a worked example, then try a few examples of their own. They find that their answers are correct, and conclude that they have learned the subject.

There are several problems with this type of learning:

* The practice is being done immediately after reading the procedure and looking at the worked examples. This means that the student may be learning the material using his/her short term memory rather than long term memory. He/she may well forget the technique and be unable to do problems of the same type just a few days later.
* The textbook from which they are obtaining the problems may have deliberately designed the worked problem, and other problems to match each other very well. So students who learn in this way may be thrown off by similar but slightly different problems that they encounter later .
* The student may not obtain any conceptual understanding of the material, so he/she may be completely unable to use the ideas behind the procedure in different contexts, or reconstruct the procedure in the event that he/she forgets it.

The following approaches can help you avoid these pitfalls:

* Maintain some spacing being doing a worked problem (where you follow indicated steps) and doing practice problems. In some cases, you may prefer to do one practice problem immediately after the worked problem, and a few more after a few days. This is a special case of the more general strategy of [[spaced repetition]].
* When you attempt practice problems a few days after doing the worked problem, pay particular attention to the parts that you are stuck at. These are probably the parts that you have conceptual trouble with. ''Do not'' just open up the worked problem again without making a serious effort.
* Attempt practice problems that are framed somewhat differently from the original worked problem. Most books have large numbers of exercises to choose from, and good teachers generally give a varied mix of problems, but pay conscious attention to this issue if you are studying by yourself, or if your teacher isn't giving you a varied mix.
* Wherever possible, try to acquire the conceptual intuition behind the steps. The texts or resources you use often do offer conceptual explanations, but these may not be highlighted because the authors expect that most students just want to master the algorithms and do not care about the explanations.

===Online math learning resources===

{{further|[[Online math learning resources]]}}

Note that websites often add new features and improve existing ones, so the information here may not be up-to-date.

{| class="sortable" border="1"
! Resource !! Link to website !! Videos explaining the content? !! Text explanations of concepts? || Practice problems sorted by area? !! Dependencies between topics explicitly codified?
|-
| [[ALEKS]] || [http://www.aleks.com www.aleks.com] || No || No || Yes || Yes, done at a considerable level of detail using knowledge spaces.
|-
| [[Khan Academy]] || [http://www.khanacademy.org khanacademy.org] || Yes; comprehensive coverage but not high quality || No || Yes || Yes, but this is a new feature and may not be very thorough.
|-
| [[Brilliant]] || [http://www.brilliant.org brilliant.org] || No || No (with a few exceptions) || Yes || No (though they may be doing this soon)
|-
| [[PatrickJMT]] || [http://www.patrickjmt.com patrickjmt.com] || Yes || No || No || No
|}

===Math reading recommendations===

See our [[math reading recommendations]] page.

Single-variable calculus learning recommendations

2014-11-02T18:06:33Z

Vipul: /* Acquiring greater depth in calculus topics you already learned */

{{subject learning recommendations|single-variable calculus}}

This page lists recommendations specific to learning [[single-variable calculus]] (called ''calculus'' for short here). This includes limits, differential calculus, integral calculus, and some auxiliary topics. This combines the calculus-related items in our [[online mathematics learning resources]] and the calculus-related items in our [[mathematics reading recommendations]] and sorts them based on your educational stage.

==First-time learning==

You're interested in self-studying calculus (roughly at the level of Advanced Placement BC calculus in the US), but with a deeper conceptual understanding and/or better problem-solving skills. The self-study could be undertaken prior to studying the subject at school, in parallel to school, or in place of studying the subject at school. The following are some high-quality resources that can offer you a relatively complete experience. We haven't made an attempt to be comprehensive here, but have listed the best sources.

{| class="sortable" border="1"
! If this description fits you ... !! ... this might be the best recommendation !! Cost
|-
| You have an interest in solving challenging mathematical problems and discovering mathematical ideas through the problem-solving process. || The AoPS calculus text: ''Calculus'' by David Patrick, ISBN 978-1-934124-24-6, [http://www.artofproblemsolving.com/Store/viewitem.php?item=calculus paperback]. You can also enroll in the [https://www.artofproblemsolving.com/School/courseinfo.php?course_id=calculus AoPS online calculus class], though that costs more money. || ~$45 for the text, ~$10 for the solutions manual. Current price for semester-long AoPS calculus class unavailable, but comparable precalculus class costs $425 ($478 with book).
|-
| You prefer to learn from videos that offer conceptual explanations of ideas, and you don't have an aversion to long videos. || The [[Center of Math]] calculus videos ([http://centerofmath.org/videos/index.html here]) and their corresponding textbooks ([http://centerofmath.org/store/index.html store]). These introduce ideas in a concepts-first fashion. || The Worldwide AP Calculus book (that covers all topics) costs $14.95 digital and $39.95 print. Videos require a decent Internet connection, but we expect the Internet connections of most clients to be adequate.
|-
| You just want something to get you started. || Almost any book will do. Here's the [http://ocw.mit.edu/resources/res-18-001-calculus-online-textbook-spring-2005/textbook/ calculus online textbook] by Gilbert Strang that's reasonably good. Stewart's book is also good, but expensive. For video-based learning, consider [[Thinkwell]] if you're willing to pay money. || Strang's book (digital version) is free. Thinkwell costs ~$150 for year-long course access.
|}

==Acquiring greater depth in calculus topics you already learned==

You have already learned calculus at the Advanced Placement level or equivalent, but you feel that your understanding of the subject was inadequate. You want to acquire greater depth of understanding in some areas of the subject.

'''The first-time learner recommendations can be used for relearning'''. As a relearner, however, you have the advantage that you can also rely on resources that are very good for some important parts of the subject, even if they are not comprehensive and/or they are not uniformly good. Some such recommendations are:

{| class="sortable" border="1"
! If this description fits you ... !! ... this might be the best recommendation !! Cost
|-
| You are considering studying higher math and want to relearn calculus in a way that prepares you for higher math. || [http://www.amazon.com/Calculus-4th-Michael-Spivak/dp/0914098918/ Calculus, 4th Edition, by Michael Spivak] is a good choice. This text is used in Honors Calculus classes in many universities. || Expensive, ~$75-100
|-
| You like a nice integrated course experience with professionally done videos, quizzes, and lecture materials. || [https://www.coursera.org/course/calcsing Calculus: Single Variable by Robert Ghrist (University of Pennsylvania) on Coursera] || Free
|-
| You are looking for detailed explanations of a few important topics in single-variable calculus, without necessarily getting comprehensive coverage of all topics || [http://www.youtube.com/user/vipulsclassroom Vipul's Classroom] has video playlists on some calculus-related topics, including limits, many subtopics of integration, differential equations, some subtopics of sequences and series. You might also be interested in [https://docs.google.com/file/d/0B66kEP2FWgm_SExEVmpSUUxyblU these notes by Eus]. || Free
|}

It's also worth remembering that ''many texts have conceptual explanations that may not be emphasized much by the authors'' (because they do not expect the majority of students to be interested in these explanations). If you are sufficiently interested, you can and should read these explanations. Some explanations may be deferred to the appendix.

==Testing and honing your knowledge and skill through practice==

===Practice at basic computation===

* You can use any calculus text. The [http://ocw.mit.edu/resources/res-18-001-calculus-online-textbook-spring-2005/textbook/ calculus online textbook] by Gilbert Strang, available online for free, is a good start. Answers are available to odd-numbered problems at the back of the text.
* For practice with full-length exams, consider the [https://apstudent.collegeboard.org/apcourse/ap-calculus-ab/exam-practice AP exam practice] and [http://www.examswithsolutions.com/Subjects/math_exams.html this page] (many links within the latter page are broken).
* If the problem lists in your calculus text are insufficient, you can also buy [http://www.amazon.com/The-Humongous-Book-Calculus-Problems/dp/1592575129/ The Humongous Book of Calculus Problems] (~$16 paperback) (and [http://www.amazon.com/Schaums-000-Solved-Problems-Calculus/dp/0071635343/ Schaum's 3000 Solved Problems in Calculus] (~$15 digital, ~$23 paperback) to get a ''lot'' of practice with calculus problem-solving. These books cost some money but are not too expensive.
* Although existing problem lists in books are usually more than enough to give you practice and gauge your skill level, you can also in principle vary the numbers to create your own problem variants, then check your solutions against a calculator or [http://www.wolframalpha.com Wolfram Alpha].
* If you have Wolfram Pro, you can try the [http://www.wolframalpha.com/problem-generator/quiz/?category=Calculus Wolfram Problem Generator].

===Practice at solving tricky problems that rely on knowing calculus well and being able to recall it quickly===

Tournaments conducted by universities such as Harvard, MIT, Stanford, and Rice can be a good source of calculus problems. Typically, these problems:

* require you to know calculus well enough to be able to recall relevant facts quickly based on pattern-matching,
* test for general problem-solving skills,
* do not generally require very messy computations, and
* do not test for very deep conceptual understanding.

Particular tournaments:

* Harvard-MIT Math Tournament ([http://web.mit.edu/hmmt/www/february/datafiles/coordinators/2010.shtml 2010] and earlier years)
* [http://www.ruf.rice.edu/~eulers/RMT.html Rice Math Tournament]
* [https://sumo.stanford.edu/old/smt/ Stanford Math Tournament]

===Testing and practice of deeper conceptual understanding without requiring very advanced general problem-solving skills===

* [http://vipulnaik.com/teaching Vipul Naik's teaching]: Vipul Naik has put up quizzes for single-variable calculus courses he taught. These are focused on testing and improving conceptual understanding of specific topics, and they do require somewhat more advanced problem-solving skills than typical calculus classes, but not at the level needed for tournaments.
* Many standard calculus texts, such as Stewart's text and the Salas-Hille-Etgen text, have a number of advanced problems in their exercises. Teachers often omit these problems when assigning homework, but if you have a calculus text, do consider looking for the advanced problems in the text.

==Note on the Khan Academy==

A number of people choose Khan Academy as a source of supplementary learning for all academic subjects, including calculus. We believe our recommended resources are generally better than Khan Academy, regardless of your goals. You might also be interested in reading our discussion of Khan Academy [[Khan Academy|here]].

If, however, our previous suggestions didn't fit your needs well, and you have found Khan Academy to generally work well for you, we recommend considering [[PatrickJMT]] ([http://patrickjmt.com patrickjmt.com]) as a free but somewhat superior alternative to Khan Academy that is very similar in style. Another alternative is [https://www.coursera.org/course/calc1 Calculus One from Coursera]. Both of these have the advantage over Khan Academy that the instructors have relevant mathematical background as well as teaching experience, so they are more likely to make accurate statements that are reflective of the deeper logic of the subject.

==Related recommendations==

* Somewhat lower: [[High school mathematics learning recommendations]], including [[algebra learning recommendations]] and [[precalculus learning recommendations]].
* Somewhat higher: [[Lower division undergraduate mathematics learning recommendations]], including [[multivariable calculus learning recommendations]] and [[linear algebra learning recommendations]]
* [[Mathematics learning recommendations]] (full list)
* [[Online mathematics learning resources]] (full list)
* [[Mathematics reading recommendations]] (for casual mathematics reading)

Programming guide

2014-10-11T17:15:26Z

Vipul:

This page includes a preliminary version of a guide on learning programming. See also our [[programming learning resources]], [[programming learning benefits]], and [[learn programming]] pages.

==#1: Programming for non-programmers==

There are some skills that don't really constitute programming, but:

* require some familiarity with programming-style thinking,
* help make one more computer-savvy, and
* make it a little easier to start learning programming (the differential isn't huge)

Examples are:

* Markup or text formatting languages such as LaTeX, HTML, Markdown, Mediawiki syntax
* Shell commands (such as the Bash shell)
* Computational software (such as Mathematica, Matlab, R (command prompt)). To a lesser extent, this also applies to spreadsheet software such as Excel.

In each of these cases, either the same language supports full-fledged programming, or there are languages built on it that support full-fledged programming. For instance, LaTeX can be used to write complicated functions to control formatting and layout, though the typical use case of LaTeX does not involve programming. Similarly, HTML isn't a programming language, but a bunch of tools built atop it, such as CSS, PHP, Ruby on Rails, and Javascript, support a varied range of programming capabilities. Matlab and R can be used as interactive interfaces where you type in one command at a time, but we can also write full-fledged programs in these languages.

===Using this as a route to learning programming===

Of the people who eventually learn programming, a nontrivial fraction start off with learning one or more of the above and only then transition to a proper programming language. Even so, most of those who transition to a proper programming language do so somewhat independently of their experience with these. But it is possible, if rare, to piggyback on one's experience with markup, shell, and computational languages to learn programming. Here are some examples:

* A person designs his own webpage in HTML. Later, he wants to add more interactivity to the webpage. He adds some Javascript. He wants to make the style uniform across pages. He introduces CSS. He then wants to include some context-dependent displays into the page, so he switches from HTML to PHP to use more general-purpose programming constructs.
* A person uses the shell command "grep" to find relevant snippets of text within large files. Then, he decides that he doesn't just want to find the snippets, he wants to store them in a separate file. The command line syntax for the grep command gets more complicated. So he realizes that it's better to write a shell script for the job. But then he hears of Perl, and decides to switch to writing a Perl program instead of a shell script.
* A person is using a computational software to carry out relevant computations. Then, she notices that she is often doing calculations that share some common aspect. She wonders if there's a faster way of doing it than typing out long commands to do the calculations. She learns that she can define a function that can capture the sequence of operations she wants to do. Thus, she's introduced to the idea of functions. Perhaps her needs become more complicated, and she needs to introduce if-then logic into her functions.

The main advantage is that it allows a more gradual ramp-up in the abstraction level. It does not require the person to rapidly absorb a multitude of ideas related to algorithms and various program control and flow structures. The ramp-up is organic.

The main disadvantage is that this can be a slow process. Moreover, unless the person has the right set of needs, he or she may never get to the point where it's necessary to transition to full-fledged programming.

==#2: Basic structures of imperative programming==

[[Wikipedia:imperative programming|Imperative programming]] (as opposed to declarative programming) is a mode of programming where explicit instructions are provided on what the program should ''do''. In contrast, [[wikipedia:declarative programming|declarative programming]] is a style of programming where the program includes statements on what ''is true'' (functional programming is an example of declarative programming).

Most people begin their journey into programming by learning imperative programming, and more specifically, within that, the sub-paradigm of [[wikipedia:procedural programming|procedural programming]], and within that, [[wikipedia:structured programming|structured programming]]. In other words, they learn how to give explicit instructions to machines in a structured format to accomplish what they want the machines to do. There are plausible arguments for beginning one's journey to programming using structured programming:

* Procedural programming offers the closest correspondence between instructions as they are written in the program source code and how the machine actually executes them. New programmers find that this makes it easy for them to understand how the computer's state is changing as it is executing the program. Functional programming is more opaque (at least to new programmers) in terms of how the instructions are actually executed.
* As a matter of fact, most programming languages, and most of the world's existing codebase, uses the procedural programming, and more specifically, the structured programming, paradigm.

That being said, some people with a mathematical background might find declarative programming styles, particularly functional programming, an easier and more interesting onboarding experience. This is discussed more later in the guide.

===The main components===

* The use of variables as stores of value that can be updated
* The concept of types for variables
* The use of arrays and similar structures
* The use of functions, blocks, and subroutines
* Conditional logic (if-then-else statements)
* Iteration logic (for loops)
* Iteration-cum-conditional logic (do-while loops and while loops)

===How far does this get us in programming knowledge?===

Anecdotal evidence suggests that 50%+ (perhaps close to 80%) of the work programmers do involves only these skills. This is particularly true of people who are only maintaining a codebase and do not need to design a new system from scratch, or for people who are doing very simple, incremental stuff.

This does ''not'', however, mean that somebody who can do only this stuff is almost ready to do coding. Even if time-wise, only 20-50% of coding requires more advanced skills, a programming job will only go to somebody who is able to handle the harder parts along with the easier parts. Since all the components of coding are tightly integrated, it's not efficient to farm out the "easier" parts to people who can't understand the harder parts.

However, this level of skill, while not enough to get a programming-focused job, might be enough for a person to maintain existing codebases for personal or academic use, and reduces the person's reliance on others in this context.

==#3: Combining programming with algorithms and data structures==

Programming guide

2014-10-11T17:09:24Z

Vipul: /* #2: Basic structures of imperative programming */

Programming guide

2014-10-11T17:04:06Z

Vipul:

Programming guide

2014-10-11T16:52:01Z

Vipul: Created page with "This page includes a preliminary version of a guide on learning programming. See also our programming learning resources, programming learning benefits, and learn pr..."

Culture of academia

2014-10-11T16:20:27Z

Vipul: /* The atmosphere is more collegiate, but not dramatically so */

This page describes different aspects of the culture of academia. It complements the pages [[academia as a career option]] and [[social value of academia]], and can help people decide whether academia is a good fit for them.

==Peer group: what sort of people go into academia?==

There are two reasons it is important to care about the sort of people who go into, and ''stay in'', academia. First, it gives you some sense of what sort of people you'd be surrounded by if you stay in academia. Second, by looking at the qualities that these people have, and comparing with yourself, you can better figure out whether you will want to and be able to stay within academia.

===Skills needed in academia===

As in most other professions, intelligence, willingness to work hard, determination, conscientiousness, and willingness to play by the rules of the game all matter for success. Academia is somewhat unusual relative to other professions in that there is less day-to-day external pressure to perform. The pressure is there, but on a longer timescale. Thus, the ability to motivate oneself using long-term goals matters more in academia than in other disciplines, even high-skilled disciplines.

===The main predictor of continuing in academia is whether one picks the right topics and gets in the groove===

For people who enter as graduate students in technical subjects at top-tier universities, the intellectual skills or even the ability to work hard do not differ much between the ones who continue in academia beyond the Ph.D. and those who don't. The best predictor seems to be their Ph.D. experience. In particular:

# Those who picked topics and advisors where the job market has more options are more likely to stay within academia. This is partly a selection effect (people who prize staying within academia are more likely to pick topics and advisors that facilitate this) but there seems to also be a significant causal component. In particular, there are graduate students who pick unfashionable or hard topics because they're passionate about them, and don't consciously trade that off against the difficulty of getting a job later (or underestimate the difficulty thereof).
# Those who were able to get substantive publications in graduate school are more likely to stay within academia. There is again a combination of selection and treatment: selection in that the people who decide to leave graduate school during or immediately after the Ph.D. don't try to get lots of publications -- they try to do the bare minimum to get through, whereas those who want to continue would try harder to publish. However, there's also a causal component: people who publish more (both in terms of quantity and quality) are more likely to get good academic jobs. Even among those who ''do'' stay, the ones who have better publications can get more prestigious post-doctoral options.

(1) and (2) are related: people who pick more job market-friendly options and advisors are also likely to receive better guidance on what areas to concentrate their research in.

===Those who stay within academia may not have higher general intellectual curiosity than those who leave===

Since academia is a good venue for intellectual curiosity, intellectually curious people are more likely to be attracted to academia, and academia has more intellectually curious people than the general population. However, of the intellectually curious people who start within academia, the ones who stay aren't necessarily the ones who are the more intellectually curious. In fact, the correlation may be close to zero.

Here are some possible reasons:

# '''Overtly general intellectual curiosity''': When people start graduate school, they haven't had much exposure both to their academic discipline and the world at large. Thus, to begin with, they may direct most of their intellectual curiosity to problems within the academic discipline. As time passes, and they learn more about the world,their intellectual interests may diversify, and the particular attraction of their academic discipline might diminish. Interestingly, people who are not generically intellectually curious may have more "lock-in" to their academic discipline because they lack the curiosity and interest to learn about other fields.
# '''Overtly specific intellectual curiosity''': Some people who are passionately intellectually curious have very specific interests within their academic discipline. Thus, they may choose unfashionable subdisciplines and work on hard problems. These are both recipes for failure, as discussed above.

Interestingly, reasons (1) and (2) can coexist for some people -- their intellectual curiosity may be quite general, but within their academic discipline, they have a stronger aesthetic attraction to work on some problems than others.

===Constrained optimization===

One way of thinking of the formula for success within academia is: ''constrained optimization''. Namely, people who succeed in academia are the ones who try hard to optimize for success ''within academia''. These aren't necessarily people who are good at, or are even trying to, optimize for success in a holistic sense (for instance, they probably ''aren't'' optimizing for financial success, given that people successful in academia can usually earn much higher incomes outside). In some cases, they can appear to lack basic practical instincts or knowledge that would be necessary in non-academic contexts. However, ''within'' the academic context, they are very practical and have a clear sense.

==Cultural elements==

===Nobody is your boss (note: this doesn't apply to lab work and joint projects)===

One fundamental difference between academia and most professional contexts is that there is no explicit hierarchy: people do not have bosses that they need to report to. They do have advisors (in graduate school) and mentors/supervisors when doing post-doctoral work. However, the student isn't working ''for'' the advisor. Basically, in academia, everybody is working for themselves, and is evaluated by a group of peers and seniors. Advisors may try to help but their own professional standing is not adversely influenced by a student not doing a good job, so they have neither the incentive nor the authority to really enforce standards on the student.

This creates a dynamic where little external structure is enforced. People who want to succeed need to find a good internal rhythm to keep churning out high-quality material.

There are a few exceptions, which might be quantitatively significant:

* For lab work and collaborative projects, people do have bosses, who may be the same as their thesis advisors or mentors. The incentives and constraints in these contexts more closely resemble non-academic high-skilled jobs.
* Academics may have some teaching duties, and they generally have to report to teaching coordinators for their course or for the undergraduate program. Again, the incentives and constraints here come closer to real-world jobs.

===The atmosphere is more collegiate, but not dramatically so===

We can think of academia as an extension of the academic component of undergraduate life. Within academia, it's more customary (compared to other jobs) to just spend time discussing ideas, working out their ramifications, even when they are not directly related to one's research. This isn't necessarily because academics have more free time than people in other jobs, but more because their real work is close enough to general intellectual exploration that they find it easier to engage in such exploration when they have free time (somebody working in software engineering, finance, or consulting, may fall more out of touch with that sort of mindset).

That being said, there is still considerable domain-specificity. If you like to casually discuss algorithmic or programming puzzles, you'd find more of that in a software engineering job than in chemistry graduate school. If you are interested in talking about stock prices, you are more likely to be able to do that in conversation with colleagues in a finance job than in physics graduate school.

==See also==

* [[Social value of academia]]
* [[Academia as a career option]]
* [[Leaving academia after graduate school]]
==Related reading==

* [https://orgtheory.wordpress.com/category/grad-school-rulz/ Grad Skool Rulz] by Fabio Rojas

Culture of academia

2014-10-11T16:17:31Z

Vipul:

This page describes different aspects of the culture of academia. It complements the pages [[academia as a career option]] and [[social value of academia]], and can help people decide whether academia is a good fit for them.

==Peer group: what sort of people go into academia?==

There are two reasons it is important to care about the sort of people who go into, and ''stay in'', academia. First, it gives you some sense of what sort of people you'd be surrounded by if you stay in academia. Second, by looking at the qualities that these people have, and comparing with yourself, you can better figure out whether you will want to and be able to stay within academia.

===Skills needed in academia===

As in most other professions, intelligence, willingness to work hard, determination, conscientiousness, and willingness to play by the rules of the game all matter for success. Academia is somewhat unusual relative to other professions in that there is less day-to-day external pressure to perform. The pressure is there, but on a longer timescale. Thus, the ability to motivate oneself using long-term goals matters more in academia than in other disciplines, even high-skilled disciplines.

===The main predictor of continuing in academia is whether one picks the right topics and gets in the groove===

For people who enter as graduate students in technical subjects at top-tier universities, the intellectual skills or even the ability to work hard do not differ much between the ones who continue in academia beyond the Ph.D. and those who don't. The best predictor seems to be their Ph.D. experience. In particular:

# Those who picked topics and advisors where the job market has more options are more likely to stay within academia. This is partly a selection effect (people who prize staying within academia are more likely to pick topics and advisors that facilitate this) but there seems to also be a significant causal component. In particular, there are graduate students who pick unfashionable or hard topics because they're passionate about them, and don't consciously trade that off against the difficulty of getting a job later (or underestimate the difficulty thereof).
# Those who were able to get substantive publications in graduate school are more likely to stay within academia. There is again a combination of selection and treatment: selection in that the people who decide to leave graduate school during or immediately after the Ph.D. don't try to get lots of publications -- they try to do the bare minimum to get through, whereas those who want to continue would try harder to publish. However, there's also a causal component: people who publish more (both in terms of quantity and quality) are more likely to get good academic jobs. Even among those who ''do'' stay, the ones who have better publications can get more prestigious post-doctoral options.

(1) and (2) are related: people who pick more job market-friendly options and advisors are also likely to receive better guidance on what areas to concentrate their research in.

===Those who stay within academia may not have higher general intellectual curiosity than those who leave===

Since academia is a good venue for intellectual curiosity, intellectually curious people are more likely to be attracted to academia, and academia has more intellectually curious people than the general population. However, of the intellectually curious people who start within academia, the ones who stay aren't necessarily the ones who are the more intellectually curious. In fact, the correlation may be close to zero.

Here are some possible reasons:

# '''Overtly general intellectual curiosity''': When people start graduate school, they haven't had much exposure both to their academic discipline and the world at large. Thus, to begin with, they may direct most of their intellectual curiosity to problems within the academic discipline. As time passes, and they learn more about the world,their intellectual interests may diversify, and the particular attraction of their academic discipline might diminish. Interestingly, people who are not generically intellectually curious may have more "lock-in" to their academic discipline because they lack the curiosity and interest to learn about other fields.
# '''Overtly specific intellectual curiosity''': Some people who are passionately intellectually curious have very specific interests within their academic discipline. Thus, they may choose unfashionable subdisciplines and work on hard problems. These are both recipes for failure, as discussed above.

Interestingly, reasons (1) and (2) can coexist for some people -- their intellectual curiosity may be quite general, but within their academic discipline, they have a stronger aesthetic attraction to work on some problems than others.

===Constrained optimization===

One way of thinking of the formula for success within academia is: ''constrained optimization''. Namely, people who succeed in academia are the ones who try hard to optimize for success ''within academia''. These aren't necessarily people who are good at, or are even trying to, optimize for success in a holistic sense (for instance, they probably ''aren't'' optimizing for financial success, given that people successful in academia can usually earn much higher incomes outside). In some cases, they can appear to lack basic practical instincts or knowledge that would be necessary in non-academic contexts. However, ''within'' the academic context, they are very practical and have a clear sense.

==Cultural elements==

===Nobody is your boss (note: this doesn't apply to lab work and joint projects)===

One fundamental difference between academia and most professional contexts is that there is no explicit hierarchy: people do not have bosses that they need to report to. They do have advisors (in graduate school) and mentors/supervisors when doing post-doctoral work. However, the student isn't working ''for'' the advisor. Basically, in academia, everybody is working for themselves, and is evaluated by a group of peers and seniors. Advisors may try to help but their own professional standing is not adversely influenced by a student not doing a good job, so they have neither the incentive nor the authority to really enforce standards on the student.

This creates a dynamic where little external structure is enforced. People who want to succeed need to find a good internal rhythm to keep churning out high-quality material.

There are a few exceptions, which might be quantitatively significant:

* For lab work and collaborative projects, people do have bosses, who may be the same as their thesis advisors or mentors. The incentives and constraints in these contexts more closely resemble non-academic high-skilled jobs.
* Academics may have some teaching duties, and they generally have to report to teaching coordinators for their course or for the undergraduate program. Again, the incentives and constraints here come closer to real-world jobs.

===The atmosphere is more collegiate, but not dramatically so===

We can think of academia as an extension of the academic component of undergraduate life. Within academia, it's more customary (compared to other jobs) to just spend time discussing ideas, working out their ramifications, even when they are not directly related to one's research. This isn't necessarily because academics have more free time than people in other jobs, but more because their real work is close enough to general intellectual exploration that they find it easier to engage in such exploration when they have free time (somebody working in software engineering, finance, or consulting, may fall more out of touch with that sort of mindset).

==See also==

* [[Social value of academia]]
* [[Academia as a career option]]
* [[Leaving academia after graduate school]]
==Related reading==

* [https://orgtheory.wordpress.com/category/grad-school-rulz/ Grad Skool Rulz] by Fabio Rojas

Culture of academia

2014-10-11T16:04:42Z

Vipul:

Culture of academia

2014-10-11T16:03:27Z

Vipul: Created page with "This page describes different aspects of the culture of academia. It complements the pages academia as a career option and social value of academia, and can help peopl..."

Academia as a career option

2014-10-11T15:34:28Z

Vipul: /* Job satisfaction */

{{career option|academia}}

A number of people who have a high level of intellectual curiosity and like intellectual stimulation are attracted to [[academia]] as a career option. On this page, we list a number of considerations for and against academia as a career option (see our general page on [[career selection]]).

This article is written primarily based on the structure of academia in the United States. The situation differs somewhat in other countries. Specifically, the procedures related to tenure and funding are somewhat different in other countries. However, many of the general points apply.

You might also be interested in our page on [[alternatives to academia]].

==Key takeaways==

{| class="sortable" border="1"
! Factor !! Summary of the answer for academia
|-
| [[Career preparation cost]] -- How much time, money, and effort is needed to prepare for this career? || The standard route to academia is undergraduate studies, followed by graduate school, culminating in a Ph.D., then getting a post-doctoral or tenure track position. Those who cannot land a post-doctoral position may end up as adjunct faculty. The time cost is much larger than for careers like finance, consulting, and law, but comparable to that for medicine. The assessment of financial cost is mixed: people in graduate school are generally exempt from paying tuition, and may even be able to earn enough money to meet their expenses, but have to live frugally and may not be able to save much.
|-
| [[Job security]] -- How likely is it that you will need to leave a particular job, and/or leave the career as a whole? || Academia runs on a pyramid scheme: the number of positions decreases sharply at each successive stage (from undergraduate to graduate school to post-doctoral to tenure track to tenure). This leads to low job security.
|-
| [[Exit flexibility]] -- How ''easy'' is it for you to switch away ''from'' the career? || This depends on the particular academic discipline. In general, switching out is easy as long as one is willing to take a prestige and stage cut (for instance, a person may end up doing an entry-level job in industry after finishing a Ph.D., along with people who have just acquired undergraduate degrees). A few academic disciplines offer credentials and skill that can help acquire high-prestige jobs outside academia. One important point is that it is hard to ''re-enter'' academia after leaving. Therefore, if in doubt, staying a little longer while weighing options makes more sense than leaving prematurely.
|-
| [[Job location options]] -- What options for location will you have, and how much flexibility will you have in selecting the location? || The intense competitiveness of academic applications, combined with the geographic spread of places, makes it very difficult to predict where you'll end up working. In fact, it might be hard to even predict the sort of place you might end up living in (university in a vibrant city versus isolated university town). Further, academic careers involve frequent moves. All these can make life difficult for your spouse and also for raising children.
|-
| [[Job satisfaction]] -- How much will you enjoy your work? || Academia is a good venue for intellectual curiosity. The main drawbacks are that ''publish or perish'', high specialization, and the likelihood of not being at a top place all conspire to make it hard to rely on academia as a fertile environment for intellectual exploration. It might still be better than the majority of other job options.
|-
| [[Earnings]] -- How much money will you earn, after adjusting for things like cost of schooling, taxes, and retirement benefits? || Earnings in academia are low relative to skill levels. Moreover, the delayed start to earning makes the lifetime earnings even lower. Frequent moves can be costly. Unlike medical or law school, there is no or minimal graduate school tuition. However, if you have to repay tuition for undergraduate school, you'd have to wait till you finish graduate school before you can start repaying it, and the debt could be significant.
|-
| [[Work-life balance]] -- How much time and flexibility will your job leave you to pursue other activities? || Subject to the constraint that you are publishing enough (cf. the publish-or-perish syndrome), you have considerable flexibility and latitude in terms of what you choose to work on and how. Once you have tenure, the pressure to publish regularly is also lower. However, the pressure to publish is highest at the time when you are likely to be getting married and raising young children, and can get significantly in the way of childrearing.
|-
| [[Social value of work]] — How much will society benefit from your work? || Social value from research is high either for academics who are exceptional in their field, or for academics working in fields that have considerable social value. Generating social value through teaching does not require exceptional skill, ''but'' there are a number of reasons to believe that most university teaching does not generate as much social value as you could create in other careers.
|}

==Career preparation cost==

Academic research involves extending the frontiers of human knowledge. Being prepared for this therefore requires fairly thorough mastery of a body of human knowledge to the level of the frontier. That requires several years of learning.

The measure of the time taken to prepare for academia depends on whether we include graduate school in the "preparation" time. Undergraduate studies, where one is learning and neither contributing nor earning, lasts 3-4 years. Graduate studies lasts 4-8 years in most disciplines. This time is spent learning, contributing a small amount of original research, and usually earning enough to meet one's short-run expenses but not enough to save for the future or repay student loans. Details may vary based on the subject and the university.

* Directly taking a job outside academia after undergraduate studies typically means greater immediate earning than going to graduate school. The main cost of academia is therefore the substantial delay before one starts earning enough to save significantly or support a family.
* Doctoral programs generally waive tuition costs, and they offer more opportunities to earn money (either based on one's research or through teaching duties), so they require less upfront investment of large sums of money than professional degrees such as law degrees, medical degrees, MBAs, and Masters degree in other subjects (such as the Masters in Financial Mathematics).
* On the other hand, with the exception of medicine, most of these degrees are short and people can start earning huge sums of money almost immediately upon graduation.
* The huge uncertainty of staying in academia (see the [[#Job security|job security section]]) makes the career preparation costs even worse than they would otherwise appear.

==Job security==

===The pyramid scheme of academia===

The structure of academia has been called a ''pyramid scheme''. The following general points are worth noting:

# At every stage of academia, there is a significant narrowing of positions from earlier stages. The number of undergraduate majors significantly exceeds the number of graduate student positions every year, which in turn significantly exceeds the number of new postdoctoral positions every year, which in turn exceeds the number of tenure track positions every year, which in turn exceeds the number of people awarded tenure every year.
# The fraction of this can be explained by the increasing size of academia is nonzero but very small. Explicitly, the number of people from the current batch of people entering graduate school this year who will eventually earn tenure exceeds the number of people who earn tenure this year, but this difference is quite small compared to the difference alluded to in point (1).
# While part of the contraction arises from people voluntarily leaving academia (rather than leaving because it is very difficult to continue in academia), that is unlikely to explain all the narrowing. However, whether or not people leave voluntarily, the fact that most people who ''initially'' intend to stay within academia leave eventually is a point against academia being a good long-term career choice.
# Despite the fact that a large fraction of the people at any given stage in academia are unlikely to proceed to the next, the discourse and incentives are generally set up in a way that gives people the superficial impression that continuing within academia is the natural option. For this reason, many people who would otherwise find it quite easy to transition out of academia harbor the mistaken impression that they are unemployable outside academia.

===The narrowing of positions within academia===

In any hierarchical organization, there are fewer positions at the top than at the bottom. This is for a variety of reasons -- the functioning of the hierarchy and cost considerations being dominant. Thus, the "narrowing" as we move up the academic ladder is not ''a priori'' surprising.

What makes academia different from promotion systems within organizations is its ''up or out'' system. In a non-academic setting, one can remain in a low-level job for one's whole life. In academia, it is not possible to stay at a low level for too long -- one either moves up or leaves. A student can stay a few extra years in graduate school, but not forever. Somebody may do two or three postdocs instead of one (usually at different places) but cannot keep doing postdocs for his or her whole life. Once somebody gets on the tenure track, they either get accepted for tenure or eventually have to leave academia.

===The increasing size of academia does not explain this===

The number of academics in most academic disciplines is increasing, but the increase is very gradual. (Put numbers here). Explicitly, the number of people from the current batch of people entering graduate school this year who will eventually earn tenure exceeds the number of people who earn tenure this year, but this difference is quite small compared to the narrowing.

===Whether or not people leave voluntarily, it's still an argument against academia===

While part of the contraction arises from people voluntarily leaving academia (rather than leaving because it is very difficult to continue in academia), that is unlikely to explain all the narrowing. However, whether or not people leave voluntarily, the fact that most people who ''initially'' intend to stay within academia leave eventually is a point against academia being a good long-term career choice.

===Discourse within academia can obscure the fact that most people will not continue forever in academia===

Academia is an insular system where the general stated convention is to assume that people are planning to continue to do research, even though many people do not end up staying in academia. Thus, somebody in the first few years of a Ph.D. program may fail to explore options outside academia and develop the contacts and skills that might help him or her transition to such a career after completion of the Ph.D., on the mistaken belief that academia is the only place for him or her. In some cases, people are led not only to overestimate the ease of staying within academia, but also underestimate the ease of moving out -- they may believe that they are unemployable outside academia. See, for instance, [http://qz.com/174811/enroll-in-a-phd-program-but-leave-academia-as-soon-as-you-graduate/ this article].

===Exceptions: academia in developing countries and in obscure places within developed countries===

Some developing countries, such as India, have a rapidly expanding academic sector. For these, the growth effect may outweigh the funnel effect. Further, since many people from these countries leave for other countries in academia, this further reduces the competition for top positions.

In addition, a number of lower-ranked universities even within the developed world can have fairly lax system for promotion and tenure.

The flip side of these is that the faculty peer group may not be sufficiently attractive to make staying in academia worthwhile. However, if you simply want to stay in academia and are not too concerned about pay or the immediate peer environment, this option is worth considering.

==Exit flexibility==

===Entry versus exit asymmetry===

In general, it is easy to leave academia for industry, but hard to join academia from industry. Some cases are discussed below.

===Taking a gap before starting graduate studies===

Some students consider applying to graduate school (for a Ph. D. program) after spending a year or more in industry. Keep the following in mind:

* There is no specific penalty for taking years off before applying to graduate school.
* However, preparing one's application and arranging for recommendation letters can be marginally harder, because graduate school admissions generally depend on the strength of the recommendations of teachers and advisors from one's undergraduate studies.
* There are some subjects where achievements in a work environment can provide an asset to the application. For instance, people who work on specific projects related to machine learning or AI in companies might benefit somewhat from this experience when applying for a graduate program in machine learning. Similarly, certain types of experience with social work might be viewed as an asset in graduate schools on social work. However, as a general rule, work experience in and of itself does not constitute an advantage.

If you are a student in your final year of undergraduate studies who is unsure of whether to begin graduate school, consider the following options:

# Apply to graduate schools while in the final year of undergraduate studies, then, after getting an admission, defer for a year where you can work in industry. A number of graduate programs allow students to defer admission.
# Join a graduate program, and re-evaluate the decision to continue after finishing the equivalent of a Masters degree. Most doctoral programs, even though they admit students directly for the doctoral degree, allow students to leave with a Masters degree.
# Take the relevant tests (such as subject GREs) and get recommendation letters drafted from your advisors so that it is easy to apply to graduate school a year or two later.

===Completing a Masters and then later returning to a Ph.D.===

If you finished a Masters, then took some time off to work (or raise children, or travel the world), it would still be possible to apply for doctoral programs. However, most doctoral programs will not recognize your Masters coursework and you will either need to redo the coursework or take the qualifying examinations again. The Masters is unlikely to give you an edge in admissions.

An exception is worth noting: people who do three-year undergraduate programs (such as those in Europe, based on the Bologna process) may be considered either ineligible or at a significant disadvantage directly applying to graduate programs after that. It might be advisable for them to complete a masters program in their home countries and then apply. Details vary.

===Taking time off after the Ph.D.===

In a number of disciplines, particularly the "pure" ones where experience outside academia rarely helps with making academic progress, it is very difficult to re-enter academia if you take time off after your Ph.D. For this reason, students who want to experience life outside academia generally take a leave of absence for up to a year within graduate school. While it's also possible to take leaves of absence later in academia, the ''tenure clock'' starts ticking once one has completed graduate school, so people with their sights set on tenure generally avoid this.

===Moving out of academia===

{{further|[[Leaving academia after graduate school]]}}

It is relatively easy to move ''out'' of academia, but the following points should be noted:

* For a number of jobs, people may find that they do not get much of a ''premium'' for graduate work or later work. So, they may find themselves assigned to similar jobs and earning similar incomes as people who have just completed undergraduate degrees. This can be somewhat disconcerting.
* There do exist some jobs that pay a premium for Ph.Ds but this is mostly because of the signal of intellectual sophistication conferred by a Ph.D. At the object level, people may find that they are using very little of the skills and knowledge base they painstakingly developed in the course of acquiring a Ph.D.
* There are a few rare jobs in industry that draw on skills honed while doing a Ph.D. An example is jobs at Google and Facebook for people who have done their graduate work in machine learning, artificial intelligence, or some forms of graph theory that are directly used at these companies. The same may be true of some forms of engineering and biomedical research that are directly relevant to factories or industry labs. This is more the exception than the rule, and people who want to have such options in the future should choose their subject of graduate work as one of the rare few that have applications outside academia.

[http://qz.com/174811/enroll-in-a-phd-program-but-leave-academia-as-soon-as-you-graduate/ This article] offers some useful information on the subject, and advises people to quit academia after the Ph.D. unless they are sufficiently interested and are able to land a post-doctoral job.

==Job location options==

===The convention of moving to a different place at each academic stage===

In general, academic departments discriminate against people who have finished the previous stage of their academic life in the same place:

* Graduate schools generally discriminate ''against'' undergraduates from the same university in admissions. The idea is to encourage these people to move elsewhere and obtain students from other universities, to facilitate greater circulation. Effectively, this means that people are required to relocate after their undergraduate studies.
* Departments generally discriminate ''against'' hiring Ph.D.'s from the department for post-doctoral fellowships.
* There is generally less explicit discrimination against hiring post-doctoral fellows to continue for tenure track positions. But the number of people who get to that stage is small in any case. It is still generally the case that a significant fraction of people relocate after completing their post-doctoral fellowship in order to start a tenure track job.

===The huge geographic spread of academic universities and the lack of certainty of where one might get admission or a job offer===

There are not too many top universities in any particularly geographic location. The top universities are geographically spread. Some of them are in isolated university towns. Even in cities, there are usually not more than 2 or 3 top universities. For instance, the Bay Area, a center of intellectual innovation, has only two top universities (Stanford and Berkeley) which are quite a distance from one another.

Even people who are highly academically talented and accomplished can rarely guarantee getting admission or a job offer at a ''specific'' university. So, they can rarely make future plans around where they'll be.

Some ways this can be a challenge:

* If you have a spouse or cohabiting partner, then the frequent move can be difficult. If the spouse works in academia, then at every move, you both need to find jobs at the same or nearby universities. If you're working in different departments, then the departments may not coordinate hiring. If the spouse works in a profession where jobs are only available in big cities, then you need to restrict your job search to universities in or near big cities. For more, see the [[Wikipedia:Two-body problem (career)|two-body problem page on Wikipedia]].
* If you have children, frequent moves can be difficult for the children. Anticipating this, you may delay childbearing until it is much later. This could particularly be an issue if you're interested in having a large number of children.

==Job satisfaction==

{{further|[[Culture of academia]]}}

There are many different reasons people enter academia, but the most striking ones are deep interest in a particular subject, intellectual curiosity, and a desire for intellectual stimulation. In terms of providing these, academia is a mixed bag. It is arguably preferable to other ''professions'' in these terms, but may not be as good as choosing to do a high-paying day job and seeking intellectual stimulation in one's free time.

Some of the plus points of academia are:

* A dense concentration of smart and intellectually curious people with whom one can discuss issues, both within one's discipline, and in general.
* A large number of events and venues for such discussion.
* Teaching can also be a venue for feeling satisfied at helping people understand intellectually difficult but beautiful material.

However, the following need to be kept in mind:

* The ''publish or perish'' incentive system in academia means that people are often focused more on delivering publishables than intellectual exploration. Even if you personally resist this, people around you may not, and therefore you may not find much of a peer group for intellectual exploration.
* There is a huge degree of specialization ''within'' academia, making it difficult to communicate insights to colleagues outside your specialty.
* For any given university, the undergraduate student body is less strongly selected than the graduate student body, which in turn is less strongly selected than the faculty. If you like teaching mainly because you expect students who are similar in ability and curiosity as you are, you're likely to be disappointed.

==Earnings==

Academics have higher earnings than the median residents of their countries. However, becoming an academic requires a number of qualities not found in median residents. People in a position to consider academia as a serious option are therefore likely to have many of these qualities. The relevant comparison is between academia and other options available to people with these qualities. The rough takeaways are:

* Academia requires a high level of intelligence (general cognitive ability) as well as conscientiousness (dedication, perseverance, organization, willingness to work hard). People outside academia with similar levels of intelligence and conscientiousness significantly out-earn academics.
* There may be other qualities that are harder to measure that academics possess and non-academics lack, or conversely, that non-academics possess and academics lack. For instance, academics probably have higher intellectual curiosity and intrinsic motivation to learn, since academia offers somewhat greater rewards for these. They are also likely to have lower ability to get through material that is tedious but not intellectually stimulating, though this is not universally true (academia often involves a lot of gruntwork, albeit within a context that is more intellectually stimulating overall).
* For the most part, the subject matter knowledge acquired in academia beyond undergraduate studies does not lead to greater earning power (see the discussion at [[#Moving out of academia|the section of this page on moving out of academia]]).

In particular, if earnings matters to you a lot, academia is at a significant disadvantage.

==Work-life balance==

There are mutually contradictory stories about work-life balance in academia. On the one hand, the small number of hours that one has to spend "reporting for duty" leaves considerable flexibility. On the other hand, the workload can be very demanding. The following are some considerations:

* Low absolute number of hours where one's physical presence at a place at a particular time is necessitated by a job, and many months with no such constraints: Academics may have teaching loads ranging from 3 to 15 hours a week, and a few departmental meetings that rarely take more than 2-3 hours a week. This leaves a large amount of flexible time. Moreover, there are no reporting requirements during summer break.
* Absence of a clean separation between work and non-work: Academics often maintain more odd hours than non-academics, carrying work through the evening and even late at night. This may be a simple result of having a flexible schedule, a hangover from their days as students, a result of the high absolute workload, or a consequence of the fact that academia relies on creative insight that often comes at unexpected moments. It is possible for people to maintain a clean separation, but this needs to be enforced through deliberate self-discipline. The absence of clean separation can be both a positive and a negative depending on the sort of life one is leading.
* Opportunities and expectations of significant amounts of travel: Although the job description does not explicitly require travel, and it is possible to travel very little, advancement within many disciplines relies on frequent travel for conferences and workshops. People may also be expected to organize seminars, conferences, and workshops. The travel expectations are maybe around the 80th percentile of jobs with similar intelligence/conscientiousness benchmarks.
* Publish or perish: People who are interested in getting tenure are judged on a combination of measures that is heavily weighted on publication record. This creates an incentive for people to try to publish as many papers as possible after the Ph.D. completion until the point of receiving tenure. This can contribute both to a heavy workload. It can also lead to academic compromises (discussed in the job satisfaction).
* Tenure clock: The "tenure clock" -- the time period relative to which one's publication record is judged -- starts ticking after the completion of the Ph.D. This tenure clock means that taking time off to have children or do other activities can significantly affect one's tenure chances. There have been proposals to ''pause the tenure clock'' for people who want to take some time off to raise children, but the status of these proposals is unclear.

==Social value of work==

{{further|[[social value of academia]]}}

===Research===

Our general conclusion is that research academia passes the social value test only in one of these two cases:

* You are really good at it, so that you can change the paradigm of thinking.
* The discipline you are picking has high social value, so that every minor contribution there counts for a lot. Some disciplines that might pass the second test are AI-related disciplines (particularly machine learning) and biomedical research.

===Teaching===

Low student interest, curriculum rigidity, low relevance of curriculum content to students' later lives, and other factors conspire to make the social value of teaching low in general.

Academia as a career option

2014-10-11T15:34:16Z

Vipul: /* Job satisfaction */

{{career option|academia}}

A number of people who have a high level of intellectual curiosity and like intellectual stimulation are attracted to [[academia]] as a career option. On this page, we list a number of considerations for and against academia as a career option (see our general page on [[career selection]]).

This article is written primarily based on the structure of academia in the United States. The situation differs somewhat in other countries. Specifically, the procedures related to tenure and funding are somewhat different in other countries. However, many of the general points apply.

You might also be interested in our page on [[alternatives to academia]].

==Key takeaways==

{| class="sortable" border="1"
! Factor !! Summary of the answer for academia
|-
| [[Career preparation cost]] -- How much time, money, and effort is needed to prepare for this career? || The standard route to academia is undergraduate studies, followed by graduate school, culminating in a Ph.D., then getting a post-doctoral or tenure track position. Those who cannot land a post-doctoral position may end up as adjunct faculty. The time cost is much larger than for careers like finance, consulting, and law, but comparable to that for medicine. The assessment of financial cost is mixed: people in graduate school are generally exempt from paying tuition, and may even be able to earn enough money to meet their expenses, but have to live frugally and may not be able to save much.
|-
| [[Job security]] -- How likely is it that you will need to leave a particular job, and/or leave the career as a whole? || Academia runs on a pyramid scheme: the number of positions decreases sharply at each successive stage (from undergraduate to graduate school to post-doctoral to tenure track to tenure). This leads to low job security.
|-
| [[Exit flexibility]] -- How ''easy'' is it for you to switch away ''from'' the career? || This depends on the particular academic discipline. In general, switching out is easy as long as one is willing to take a prestige and stage cut (for instance, a person may end up doing an entry-level job in industry after finishing a Ph.D., along with people who have just acquired undergraduate degrees). A few academic disciplines offer credentials and skill that can help acquire high-prestige jobs outside academia. One important point is that it is hard to ''re-enter'' academia after leaving. Therefore, if in doubt, staying a little longer while weighing options makes more sense than leaving prematurely.
|-
| [[Job location options]] -- What options for location will you have, and how much flexibility will you have in selecting the location? || The intense competitiveness of academic applications, combined with the geographic spread of places, makes it very difficult to predict where you'll end up working. In fact, it might be hard to even predict the sort of place you might end up living in (university in a vibrant city versus isolated university town). Further, academic careers involve frequent moves. All these can make life difficult for your spouse and also for raising children.
|-
| [[Job satisfaction]] -- How much will you enjoy your work? || Academia is a good venue for intellectual curiosity. The main drawbacks are that ''publish or perish'', high specialization, and the likelihood of not being at a top place all conspire to make it hard to rely on academia as a fertile environment for intellectual exploration. It might still be better than the majority of other job options.
|-
| [[Earnings]] -- How much money will you earn, after adjusting for things like cost of schooling, taxes, and retirement benefits? || Earnings in academia are low relative to skill levels. Moreover, the delayed start to earning makes the lifetime earnings even lower. Frequent moves can be costly. Unlike medical or law school, there is no or minimal graduate school tuition. However, if you have to repay tuition for undergraduate school, you'd have to wait till you finish graduate school before you can start repaying it, and the debt could be significant.
|-
| [[Work-life balance]] -- How much time and flexibility will your job leave you to pursue other activities? || Subject to the constraint that you are publishing enough (cf. the publish-or-perish syndrome), you have considerable flexibility and latitude in terms of what you choose to work on and how. Once you have tenure, the pressure to publish regularly is also lower. However, the pressure to publish is highest at the time when you are likely to be getting married and raising young children, and can get significantly in the way of childrearing.
|-
| [[Social value of work]] — How much will society benefit from your work? || Social value from research is high either for academics who are exceptional in their field, or for academics working in fields that have considerable social value. Generating social value through teaching does not require exceptional skill, ''but'' there are a number of reasons to believe that most university teaching does not generate as much social value as you could create in other careers.
|}

==Career preparation cost==

Academic research involves extending the frontiers of human knowledge. Being prepared for this therefore requires fairly thorough mastery of a body of human knowledge to the level of the frontier. That requires several years of learning.

The measure of the time taken to prepare for academia depends on whether we include graduate school in the "preparation" time. Undergraduate studies, where one is learning and neither contributing nor earning, lasts 3-4 years. Graduate studies lasts 4-8 years in most disciplines. This time is spent learning, contributing a small amount of original research, and usually earning enough to meet one's short-run expenses but not enough to save for the future or repay student loans. Details may vary based on the subject and the university.

* Directly taking a job outside academia after undergraduate studies typically means greater immediate earning than going to graduate school. The main cost of academia is therefore the substantial delay before one starts earning enough to save significantly or support a family.
* Doctoral programs generally waive tuition costs, and they offer more opportunities to earn money (either based on one's research or through teaching duties), so they require less upfront investment of large sums of money than professional degrees such as law degrees, medical degrees, MBAs, and Masters degree in other subjects (such as the Masters in Financial Mathematics).
* On the other hand, with the exception of medicine, most of these degrees are short and people can start earning huge sums of money almost immediately upon graduation.
* The huge uncertainty of staying in academia (see the [[#Job security|job security section]]) makes the career preparation costs even worse than they would otherwise appear.

==Job security==

===The pyramid scheme of academia===

The structure of academia has been called a ''pyramid scheme''. The following general points are worth noting:

# At every stage of academia, there is a significant narrowing of positions from earlier stages. The number of undergraduate majors significantly exceeds the number of graduate student positions every year, which in turn significantly exceeds the number of new postdoctoral positions every year, which in turn exceeds the number of tenure track positions every year, which in turn exceeds the number of people awarded tenure every year.
# The fraction of this can be explained by the increasing size of academia is nonzero but very small. Explicitly, the number of people from the current batch of people entering graduate school this year who will eventually earn tenure exceeds the number of people who earn tenure this year, but this difference is quite small compared to the difference alluded to in point (1).
# While part of the contraction arises from people voluntarily leaving academia (rather than leaving because it is very difficult to continue in academia), that is unlikely to explain all the narrowing. However, whether or not people leave voluntarily, the fact that most people who ''initially'' intend to stay within academia leave eventually is a point against academia being a good long-term career choice.
# Despite the fact that a large fraction of the people at any given stage in academia are unlikely to proceed to the next, the discourse and incentives are generally set up in a way that gives people the superficial impression that continuing within academia is the natural option. For this reason, many people who would otherwise find it quite easy to transition out of academia harbor the mistaken impression that they are unemployable outside academia.

===The narrowing of positions within academia===

In any hierarchical organization, there are fewer positions at the top than at the bottom. This is for a variety of reasons -- the functioning of the hierarchy and cost considerations being dominant. Thus, the "narrowing" as we move up the academic ladder is not ''a priori'' surprising.

What makes academia different from promotion systems within organizations is its ''up or out'' system. In a non-academic setting, one can remain in a low-level job for one's whole life. In academia, it is not possible to stay at a low level for too long -- one either moves up or leaves. A student can stay a few extra years in graduate school, but not forever. Somebody may do two or three postdocs instead of one (usually at different places) but cannot keep doing postdocs for his or her whole life. Once somebody gets on the tenure track, they either get accepted for tenure or eventually have to leave academia.

===The increasing size of academia does not explain this===

The number of academics in most academic disciplines is increasing, but the increase is very gradual. (Put numbers here). Explicitly, the number of people from the current batch of people entering graduate school this year who will eventually earn tenure exceeds the number of people who earn tenure this year, but this difference is quite small compared to the narrowing.

===Whether or not people leave voluntarily, it's still an argument against academia===

While part of the contraction arises from people voluntarily leaving academia (rather than leaving because it is very difficult to continue in academia), that is unlikely to explain all the narrowing. However, whether or not people leave voluntarily, the fact that most people who ''initially'' intend to stay within academia leave eventually is a point against academia being a good long-term career choice.

===Discourse within academia can obscure the fact that most people will not continue forever in academia===

Academia is an insular system where the general stated convention is to assume that people are planning to continue to do research, even though many people do not end up staying in academia. Thus, somebody in the first few years of a Ph.D. program may fail to explore options outside academia and develop the contacts and skills that might help him or her transition to such a career after completion of the Ph.D., on the mistaken belief that academia is the only place for him or her. In some cases, people are led not only to overestimate the ease of staying within academia, but also underestimate the ease of moving out -- they may believe that they are unemployable outside academia. See, for instance, [http://qz.com/174811/enroll-in-a-phd-program-but-leave-academia-as-soon-as-you-graduate/ this article].

===Exceptions: academia in developing countries and in obscure places within developed countries===

Some developing countries, such as India, have a rapidly expanding academic sector. For these, the growth effect may outweigh the funnel effect. Further, since many people from these countries leave for other countries in academia, this further reduces the competition for top positions.

In addition, a number of lower-ranked universities even within the developed world can have fairly lax system for promotion and tenure.

The flip side of these is that the faculty peer group may not be sufficiently attractive to make staying in academia worthwhile. However, if you simply want to stay in academia and are not too concerned about pay or the immediate peer environment, this option is worth considering.

==Exit flexibility==

===Entry versus exit asymmetry===

In general, it is easy to leave academia for industry, but hard to join academia from industry. Some cases are discussed below.

===Taking a gap before starting graduate studies===

Some students consider applying to graduate school (for a Ph. D. program) after spending a year or more in industry. Keep the following in mind:

* There is no specific penalty for taking years off before applying to graduate school.
* However, preparing one's application and arranging for recommendation letters can be marginally harder, because graduate school admissions generally depend on the strength of the recommendations of teachers and advisors from one's undergraduate studies.
* There are some subjects where achievements in a work environment can provide an asset to the application. For instance, people who work on specific projects related to machine learning or AI in companies might benefit somewhat from this experience when applying for a graduate program in machine learning. Similarly, certain types of experience with social work might be viewed as an asset in graduate schools on social work. However, as a general rule, work experience in and of itself does not constitute an advantage.

If you are a student in your final year of undergraduate studies who is unsure of whether to begin graduate school, consider the following options:

# Apply to graduate schools while in the final year of undergraduate studies, then, after getting an admission, defer for a year where you can work in industry. A number of graduate programs allow students to defer admission.
# Join a graduate program, and re-evaluate the decision to continue after finishing the equivalent of a Masters degree. Most doctoral programs, even though they admit students directly for the doctoral degree, allow students to leave with a Masters degree.
# Take the relevant tests (such as subject GREs) and get recommendation letters drafted from your advisors so that it is easy to apply to graduate school a year or two later.

===Completing a Masters and then later returning to a Ph.D.===

If you finished a Masters, then took some time off to work (or raise children, or travel the world), it would still be possible to apply for doctoral programs. However, most doctoral programs will not recognize your Masters coursework and you will either need to redo the coursework or take the qualifying examinations again. The Masters is unlikely to give you an edge in admissions.

An exception is worth noting: people who do three-year undergraduate programs (such as those in Europe, based on the Bologna process) may be considered either ineligible or at a significant disadvantage directly applying to graduate programs after that. It might be advisable for them to complete a masters program in their home countries and then apply. Details vary.

===Taking time off after the Ph.D.===

In a number of disciplines, particularly the "pure" ones where experience outside academia rarely helps with making academic progress, it is very difficult to re-enter academia if you take time off after your Ph.D. For this reason, students who want to experience life outside academia generally take a leave of absence for up to a year within graduate school. While it's also possible to take leaves of absence later in academia, the ''tenure clock'' starts ticking once one has completed graduate school, so people with their sights set on tenure generally avoid this.

===Moving out of academia===

{{further|[[Leaving academia after graduate school]]}}

It is relatively easy to move ''out'' of academia, but the following points should be noted:

* For a number of jobs, people may find that they do not get much of a ''premium'' for graduate work or later work. So, they may find themselves assigned to similar jobs and earning similar incomes as people who have just completed undergraduate degrees. This can be somewhat disconcerting.
* There do exist some jobs that pay a premium for Ph.Ds but this is mostly because of the signal of intellectual sophistication conferred by a Ph.D. At the object level, people may find that they are using very little of the skills and knowledge base they painstakingly developed in the course of acquiring a Ph.D.
* There are a few rare jobs in industry that draw on skills honed while doing a Ph.D. An example is jobs at Google and Facebook for people who have done their graduate work in machine learning, artificial intelligence, or some forms of graph theory that are directly used at these companies. The same may be true of some forms of engineering and biomedical research that are directly relevant to factories or industry labs. This is more the exception than the rule, and people who want to have such options in the future should choose their subject of graduate work as one of the rare few that have applications outside academia.

[http://qz.com/174811/enroll-in-a-phd-program-but-leave-academia-as-soon-as-you-graduate/ This article] offers some useful information on the subject, and advises people to quit academia after the Ph.D. unless they are sufficiently interested and are able to land a post-doctoral job.

==Job location options==

===The convention of moving to a different place at each academic stage===

In general, academic departments discriminate against people who have finished the previous stage of their academic life in the same place:

* Graduate schools generally discriminate ''against'' undergraduates from the same university in admissions. The idea is to encourage these people to move elsewhere and obtain students from other universities, to facilitate greater circulation. Effectively, this means that people are required to relocate after their undergraduate studies.
* Departments generally discriminate ''against'' hiring Ph.D.'s from the department for post-doctoral fellowships.
* There is generally less explicit discrimination against hiring post-doctoral fellows to continue for tenure track positions. But the number of people who get to that stage is small in any case. It is still generally the case that a significant fraction of people relocate after completing their post-doctoral fellowship in order to start a tenure track job.

===The huge geographic spread of academic universities and the lack of certainty of where one might get admission or a job offer===

There are not too many top universities in any particularly geographic location. The top universities are geographically spread. Some of them are in isolated university towns. Even in cities, there are usually not more than 2 or 3 top universities. For instance, the Bay Area, a center of intellectual innovation, has only two top universities (Stanford and Berkeley) which are quite a distance from one another.

Even people who are highly academically talented and accomplished can rarely guarantee getting admission or a job offer at a ''specific'' university. So, they can rarely make future plans around where they'll be.

Some ways this can be a challenge:

* If you have a spouse or cohabiting partner, then the frequent move can be difficult. If the spouse works in academia, then at every move, you both need to find jobs at the same or nearby universities. If you're working in different departments, then the departments may not coordinate hiring. If the spouse works in a profession where jobs are only available in big cities, then you need to restrict your job search to universities in or near big cities. For more, see the [[Wikipedia:Two-body problem (career)|two-body problem page on Wikipedia]].
* If you have children, frequent moves can be difficult for the children. Anticipating this, you may delay childbearing until it is much later. This could particularly be an issue if you're interested in having a large number of children.

==Job satisfaction==

[{further|[[Culture of academia]]}}

There are many different reasons people enter academia, but the most striking ones are deep interest in a particular subject, intellectual curiosity, and a desire for intellectual stimulation. In terms of providing these, academia is a mixed bag. It is arguably preferable to other ''professions'' in these terms, but may not be as good as choosing to do a high-paying day job and seeking intellectual stimulation in one's free time.

Some of the plus points of academia are:

* A dense concentration of smart and intellectually curious people with whom one can discuss issues, both within one's discipline, and in general.
* A large number of events and venues for such discussion.
* Teaching can also be a venue for feeling satisfied at helping people understand intellectually difficult but beautiful material.

However, the following need to be kept in mind:

* The ''publish or perish'' incentive system in academia means that people are often focused more on delivering publishables than intellectual exploration. Even if you personally resist this, people around you may not, and therefore you may not find much of a peer group for intellectual exploration.
* There is a huge degree of specialization ''within'' academia, making it difficult to communicate insights to colleagues outside your specialty.
* For any given university, the undergraduate student body is less strongly selected than the graduate student body, which in turn is less strongly selected than the faculty. If you like teaching mainly because you expect students who are similar in ability and curiosity as you are, you're likely to be disappointed.

==Earnings==

Academics have higher earnings than the median residents of their countries. However, becoming an academic requires a number of qualities not found in median residents. People in a position to consider academia as a serious option are therefore likely to have many of these qualities. The relevant comparison is between academia and other options available to people with these qualities. The rough takeaways are:

* Academia requires a high level of intelligence (general cognitive ability) as well as conscientiousness (dedication, perseverance, organization, willingness to work hard). People outside academia with similar levels of intelligence and conscientiousness significantly out-earn academics.
* There may be other qualities that are harder to measure that academics possess and non-academics lack, or conversely, that non-academics possess and academics lack. For instance, academics probably have higher intellectual curiosity and intrinsic motivation to learn, since academia offers somewhat greater rewards for these. They are also likely to have lower ability to get through material that is tedious but not intellectually stimulating, though this is not universally true (academia often involves a lot of gruntwork, albeit within a context that is more intellectually stimulating overall).
* For the most part, the subject matter knowledge acquired in academia beyond undergraduate studies does not lead to greater earning power (see the discussion at [[#Moving out of academia|the section of this page on moving out of academia]]).

In particular, if earnings matters to you a lot, academia is at a significant disadvantage.

==Work-life balance==

There are mutually contradictory stories about work-life balance in academia. On the one hand, the small number of hours that one has to spend "reporting for duty" leaves considerable flexibility. On the other hand, the workload can be very demanding. The following are some considerations:

* Low absolute number of hours where one's physical presence at a place at a particular time is necessitated by a job, and many months with no such constraints: Academics may have teaching loads ranging from 3 to 15 hours a week, and a few departmental meetings that rarely take more than 2-3 hours a week. This leaves a large amount of flexible time. Moreover, there are no reporting requirements during summer break.
* Absence of a clean separation between work and non-work: Academics often maintain more odd hours than non-academics, carrying work through the evening and even late at night. This may be a simple result of having a flexible schedule, a hangover from their days as students, a result of the high absolute workload, or a consequence of the fact that academia relies on creative insight that often comes at unexpected moments. It is possible for people to maintain a clean separation, but this needs to be enforced through deliberate self-discipline. The absence of clean separation can be both a positive and a negative depending on the sort of life one is leading.
* Opportunities and expectations of significant amounts of travel: Although the job description does not explicitly require travel, and it is possible to travel very little, advancement within many disciplines relies on frequent travel for conferences and workshops. People may also be expected to organize seminars, conferences, and workshops. The travel expectations are maybe around the 80th percentile of jobs with similar intelligence/conscientiousness benchmarks.
* Publish or perish: People who are interested in getting tenure are judged on a combination of measures that is heavily weighted on publication record. This creates an incentive for people to try to publish as many papers as possible after the Ph.D. completion until the point of receiving tenure. This can contribute both to a heavy workload. It can also lead to academic compromises (discussed in the job satisfaction).
* Tenure clock: The "tenure clock" -- the time period relative to which one's publication record is judged -- starts ticking after the completion of the Ph.D. This tenure clock means that taking time off to have children or do other activities can significantly affect one's tenure chances. There have been proposals to ''pause the tenure clock'' for people who want to take some time off to raise children, but the status of these proposals is unclear.

==Social value of work==

{{further|[[social value of academia]]}}

===Research===

Our general conclusion is that research academia passes the social value test only in one of these two cases:

* You are really good at it, so that you can change the paradigm of thinking.
* The discipline you are picking has high social value, so that every minor contribution there counts for a lot. Some disciplines that might pass the second test are AI-related disciplines (particularly machine learning) and biomedical research.

===Teaching===

Low student interest, curriculum rigidity, low relevance of curriculum content to students' later lives, and other factors conspire to make the social value of teaching low in general.

Social value of work: income as a proxy

2014-10-10T17:12:47Z

Vipul: /* Why income is a good proxy */

==Key takeaways==

* The income that one can earn doing a job is a fairly good starting point in estimating its [[social value of work|social value]], because it generally factors in most of the relevant considerations that go into estimating the social value.
* There are a few systematic factors that weaken the relationship between income and social value. These are discussed below.
* It is very hard to come up with a robust way of weighing these additional considerations.

==Why income is a good proxy==

A deeper understanding of why this is generally expected to be the case requires an understanding of basic economics. You might wish to check out our [[economics reading recommendations]]. A brief summary is that money is the currency through which people keep track of value. Changes to value that are related to economic transactions are reflected in the associated prices.

===The rough argument: income as a measure of value experienced by customers===

The reason essentially connects to why, in a (largely) free market, one's income represents how much people are willing to pay for one's services, and this is a measure of the value these services provide to those other people. If one produces a product that someone else finds very valuable, that person will tend to be willing to pay a lot for it. If one produces a large quantity of a product that's purchased by many people, the total amount that the customers pay will be large.

===Income takes flow-through effects into account===

One can produce value (positive or negative) not only for one's customers but also for their customers, and their customers' customers, and so on. These effects are sometimes called ''flow-through effects''. Income takes flow-through effects into account as follows: If your customers' customers derive more value, they are willing to pay your customer more. The promise of being able to make more money increases the value of your service to your customer, so you can charge your customer more.

Further, income quantitatively reflects each of the criteria that determine the magnitude of flow-through effects:

* '''Subjective value criterion''': Greater subjective value correlates with greater income as well as greater flow-through effects.
* '''Productivity enhancement criterion''': People are generally willing to pay more for tools that enhance their productivity as opposed to leisure goods, though this is not uniformly the case. Productivity-enhancing value creation also correlates with higher flow-through effects.
* '''Value created by customers criterion''': The greater the value your customers create for their customers, the greater both the income they are willing to pay (for the same improvement in their productivity or subjective experience) and the greater the flow-through effects (for instance, compare providing a high-productivity person and a low-productivity person Internet access, even if they both enjoy it equally).

===Income takes replacability into account===

Some jobs have the property that it's very important that ''somebody'' do them, but that it's relatively easy to find people to do them. (Nowadays, food production is an example.) If you take such a job, you will contribute much less social value than you would if ''nobody else'' could do the job, because if you don't do it, somebody else will. Income partially takes this into account in the following ways:

* Customers tend to pay the lowest price possible, so if multiple suppliers are available, no supplier can charge more than the smallest price at which the other suppliers would be willing to provide the good.
* Income also accounts for what your counterfactual replacement might earn in ''another'' job. If the person who would replace you in a given job ''A'' could generate high value in another job ''B'', then that is an argument in favor of your doing the job ''A'' (so your counterfactual replacement is freed up to do job ''B''). Income takes this dynamic into account: because your replacement has another high-value job ''B'', that bids up his/her asking price for ''A'', and therefore raises the average income for ''A'', making ''A'' more attractive to you.

==Ways in which income can be a bad proxy==

===General problems with any economic proxy===

Irrationality, imperfect information, systems being away from equilibrium, and many other general flaws with the functioning of markets make economic proxies in general somewhat unreliable.

===Externalities===

In some cases, your work may significantly affect other people who are not your customers and do not have contractual relationships with you. The classic example of negative externalities (your work harms others) is the polluting factory that makes the air and water unclean, increasing the incidence of various diseases in the population of the nearby town. An example of positive externalities may be: you paint the front yard of a home, and the neighbors experience aesthetic delight.

The existence of negative externalities is not a decisive argument against doing something, and similarly, the existence of positive externalities is not a decisive argument for doing something. Externalities are important because they point to a possible source of disconnect between income and social value.

====Distinction between externalities and flow-through effects====

Externalities need to be distinguished from a more general concept of ''flow-through effects''. Flow-through effects refers to the fact that people who are not your direct customers benefit from your service, perhaps as customers of your customers. For instance, you may provide technical support to a supermarket, which in turn is better able to serve its customers. Such flow-through effects, that proceed through a chain of market relationships, are ''not'' examples of externalities, and the price mechanism should work as a reasonable proxy, at least in principle (subject to the general problems that all economic proxies face).

===Wealth effects===

A person providing a service to people with little wealth may not be able to make much money even though the customers gain a lot, because of their limited ability to pay. A person providing a similar service to wealthy people may make much more money.

However, it should be noted that providing the same service to wealthier people ''can'' generate more social value than providing the service to poor people, due to flow-through effects. A wealthy person and a poor person may experience the same consumption benefit from Internet access, but the wealthy person is (on average) more likely to be able to productively use the Internet access to create social value. Thus, the measurement distortion created by wealth effects is not as huge as it might seem at first glance.

===Market distortions===

Markets can be distorted, typically by governments, but sometimes also by non-governmental agencies that exert huge power. Examples include:

* '''Credentialism''': Artificially restricting the pool of people who can perform a job to those who acquire certain credentials (even though the credentials are not needed for doing the job) might restrict supply, drive up wages, and also drive up the costs of getting the credentials.
* '''Subsidies and taxes''': Subsidies to certain industries can distort the price mechanism. For instance, if the government subsidizes purchases of a certain good (education, housing, food) then incomes for people supplying that good can rise out of proportion with the value they generate. Arguably, the government subsidy is an attempted corrective for other problems that cause the service providers to be undercompensated for the social value they generate (such as externalities and wealth effects). However, this may not always be the case in practice.

Programming learning resources

2014-08-05T19:07:15Z

Vipul:

There are many resources you can use to learn programming, or coding, online. The links on our page on [[Quora questions about learning programming]] contain a large body of recommendations. We've listed some resources below.

==Website that teach the programming mentality==

{| class="sortable" border="1"
! Resource !! Website !! Description
|-
| [[Code.org]] || [http://learn.code.org learn.code.org] || This focuses on teaching the programming mentality. It begins by using a [[wikipedia:visual programming language|visual programming language]] called Blockly (a simplified web-based version of [[wikipedia:Logo (programming language)|Logo]]) to teach the ideas of programs as sequences of instructions as well as various coding constructs such as loops. Ideal for people of all ages (including young children) to master basic programming constructs.
|-
| CodeHS || [http://www.codehs.com/ www.codehs.com] || Basic free course teaches you how to think as a programmer. More advanced courses require you to pay.
|-
| MIT Scratch || [http://scratch.mit.edu/ scratch.mit.edu] || Dual-purpose: allows kids to program games and animations, and also helps them learn the programming mentality in the process.
|}

==Websites that ''interactively'' teach specific programming languages or web development frameworks==

These are focused more on mastery of syntax and methods for specific programming languages rather than in general.

{| class="sortable" border="1"
! Resource !! Website !! Level !! Focus areas
|-
| [[Codecademy]] || [http://www.codecademy.com www.codecademy.com] || Beginner || Web Fundamentals, PHP, JavaScript, jQuery, Python, Ruby and APIs
|-
| Code Avengers || [http://www.codeavengers.com/ www.codeavengeders.com] || Beginner || HTML, CSS, Javascript; focused on beginners
|-
| Code School || [https://www.codeschool.com/ www.codeschool.com] || Intermediate || Ruby, Javascript, HTML/CSS, iOS development
|-
| Learn Street || [http://www.learnstreet.com/ www.learnstreet.com] || Beginner & intermediate || JavaScript, Python and Ruby (at beginner level)
|}

==Advanced resources==

If you're already somewhat familiar with programming syntax and the general idea of algorithms, you can try these resources for programming practice (more information to be added later):

* [http://www.hackerrank.com HackerRank]
* [http://www.projecteuler.net Project Euler]
* [http://www.topcoder.com TopCoder]

There are also a number of resources with multiple choice quiz questions for specific programming languages. Some of them are listed below:

* [http://www.smarterer.com Smarterer]

==More extensive lists and resource comparisons==

* [http://www.hongkiat.com/blog/sites-to-learn-coding-online/ Top 10 Websites to Learn Coding (Interactively) Online]
* [http://www.quora.com/Learning-to-Program/What-are-the-best-resources-sites-books-or-tutorials-for-learning-programming Quora answer on the best resources, sites, and books for learning programming]

Organizations and resources for gifted children

2014-06-27T22:34:50Z

Vipul: Redirected page to Young children portal

#redirect [[Young children portal]]

Statistics learning recommendations

2014-06-27T22:20:40Z

Vipul: /* =Cartoon books */

Statistics is a fundamental tool in the social sciences, medical sciences, actuarial science and finance. Aside from being useful professionally, some of the basic concepts of statistics are also crucial to quantitative literacy and making sense of the world in day to day life.

You should be warned that statistics expositions are sometimes conceptually misguided. The focus on ''frequentist statistics'' rather than ''Bayesian statistics'' is arguably an example of this, though this has been [http://lesswrong.com/lw/jne/a_fervent_defense_of_frequentist_statistics/ disputed]. It's important to learn the standard perspectives in order to understand and communicate with people who use them. However, you should be critical in your reading about the subject – if some of the material doesn't make sense, it's possible that this is because it doesn't have good justification rather than because you're misunderstanding something.

Statistical intuition isn't the same as technical knowledge of statistical methods like factor analysis. One can have the technical knowledge without the intuition or the intuition without the technical knowledge. If you're planning on going into a line of work where statistics is used, you need to acquire the technical knowledge, but for general quantitative literacy, reading our recommendations for statistical intuition may be sufficient.

== Recommendations ==

=== Statistical intuition ===

These are non-technical books that illustrate the concepts of statistics with real world examples. Because they illustrate the ideas in context, helpful for building statistical intuition.

* [http://www.amazon.com/Naked-Statistics-Stripping-Dread-Data-ebook/dp/B007Q6XLF2/ Naked Statistics: Stripping the Dread from the Data] by Charles Wheelan.

* [http://www.amazon.com/Signal-Noise-Art-Science-Prediction-ebook/dp/B007V65R54/ The Signal and the Noise] by Nate Silver. This book is organized around the theme of predicting the future.

* [http://www.amazon.com/The-Lady-Tasting-Tea-Revolutionized/dp/0805071342 The Lady Tasting Tea: How Statistics Revolutionized Science in the Twentieth Century] by David Salsburg. This provides an early history of statistics.

=== Frequentist approach textbook ===

The most commonly taught approach to statistics is the ''frequentist'' approach.

* [http://www.amazon.com/Statistics-Plain-English-Third-Timothy-ebook/dp/B004RM9VSY/ Statistics in Plain English] by Timothy C. Urdan is a lucid book that covers a lot of material in only 200 pages. It's somewhat terse and some readers may find it to be short on examples.
* [http://www.amazon.com/Statistics-For-Dummies-Deborah-Rumsey/dp/0470911085/ Statistics for Dummies], and [http://www.amazon.com/Statistics-For-Dummies-Deborah-Rumsey/dp/0470911085/ Statistics II for Dummies] offer a more leisurely exposition. [http://www.amazon.com/Statistics-Workbook-Dummies-Deborah-Rumsey/dp/0764584669/ Statistics Workbook for Dummies] offers exercises.

=== Bayesian statistics ===

''Bayesian statistics'' is thought by many people to be a superior alternative to frequentist statistics.

Chapter 8 of [http://www.amazon.com/Signal-Noise-Art-Science-Prediction-ebook/dp/B007V65R54/ The Signal and the Noise] by Nate Silver gives the history of Bayesian statistics and an exposition of the basics of the subject.

Some blog posts that overlap with the content of Silver's book but that the reader may find helpful for slightly different perspectives are:

* [http://lesswrong.com/lw/774/a_history_of_bayes_theorem/ A History of Bayes' Theorem] by Luke Muehlhauser.
* [http://yudkowsky.net/rational/bayes An Intuitive Explanation of Bayes' Theorem] by Eliezer Yudkowsky.
* [http://lesswrong.com/lw/2b0/bayes_theorem_illustrated_my_way/ Bayes' Theorem Illustrated (My Way)] by Komponisto.

For a full technical introduction to Bayesian statistics, [http://www.amazon.com/Introduction-Bayesian-Statistics-William-Bolstad-ebook/dp/B00D8XUJA2 Introduction to Bayesian Statistics] by William Bolstad may be helpful, but the book is very expensive.

==== Programming statistics ====

If you enjoy programming, are looking to learn programming, or will be implementing statistical algorithms in your work, these books may be good choices:

* [http://www.amazon.com/Discovering-Statistics-Using-Andy-Field-ebook/dp/B00HPZ4VVM/ Discovering Statistics Using R] by Andy Field, Jeremy Miles and Zoe Field.
* [http://www.greenteapress.com/thinkbayes/thinkbayes.pdf Think Bayes: Bayesian Statistics Made Simple] by Allen Downey teaches statistics through Python programming.
* [http://www.amazon.com/Doing-Bayesian-Data-Analysis-Tutorial/dp/0123814855/ Doing Bayesian Data Analysis: A Tutorial with R and BUGS] by John Kruschke.

=== Online classes ===

If you find it easier to learn from lectures than from a textbook, we encourage you to check out free online courses. You can try out different lecturers until you find one who you find especially easy to learn from.

* [https://www.coursera.org/ Coursera] offers ~45 statistics courses. Their offerings include [https://www.coursera.org/course/stats1 Statistics One], an introductory course.
* [https://www.edx.org/ edX] offers ~20 statistics courses, including [https://www.edx.org/course/uc-berkeleyx/uc-berkeleyx-stat2-1x-introduction-1138 Introduction to Statistics: Descriptive Statistics], [https://www.edx.org/course/uc-berkeleyx/uc-berkeleyx-stat2-3x-introduction-1533 Introduction to Statistics: Inference] and [https://www.edx.org/course/uc-berkeleyx/uc-berkeleyx-stat2-2x-introduction-1534 Introduction to Statistics: Probability], which are associated with UC Berkeley.
* [https://www.udacity.com/ Udacity] offers [https://www.udacity.com/course/st101 Intro to Statistics] and [https://www.udacity.com/course/st095 Statistics].
* [http://ocw.mit.edu/index.htm MIT OCW] offers an [http://ocw.mit.edu/courses/mathematics/ math courses] that include "Introduction to Probability and Statistics" and "Statistics for Applications."
* Carnegie Mellon University's [http://oli.cmu.edu/ Open Learning Initiative] offers [https://oli.cmu.edu/jcourse/webui/guest/join.do?section=probstat a course on probability and statistics].

Statistics.com offers over 110 [http://www.statistics.com/course-catalog/ statistics courses]. The courses are expensive: $500 for a 4-week long course. We don't have any inside knowledge of the quality of the courses.

===Cartoon books===

The blog post [http://www.r-bloggers.com/the-most-comprehensive-review-of-comic-books-teaching-statistics/ The most comprehensive review of comic books teaching statistics] discusses and reviews a number of cartoon books teaching statistics. These can be ideal for many people who want to learn statistics informally or even for people who want to learn statistics formally and are looking for a way to supplement their main learning source.

Statistics learning recommendations

2014-06-24T14:03:15Z

Vipul: /* Recommendations */

Statistics is a fundamental tool in the social sciences, medical sciences, actuarial science and finance. Aside from being useful professionally, some of the basic concepts of statistics are also crucial to quantitative literacy and making sense of the world in day to day life.

You should be warned that statistics expositions are sometimes conceptually misguided. The focus on ''frequentist statistics'' rather than ''Bayesian statistics'' is arguably an example of this, though this has been [http://lesswrong.com/lw/jne/a_fervent_defense_of_frequentist_statistics/ disputed]. It's important to learn the standard perspectives in order to understand and communicate with people who use them. However, you should be critical in your reading about the subject – if some of the material doesn't make sense, it's possible that this is because it doesn't have good justification rather than because you're misunderstanding something.

Statistical intuition isn't the same as technical knowledge of statistical methods like factor analysis. One can have the technical knowledge without the intuition or the intuition without the technical knowledge. If you're planning on going into a line of work where statistics is used, you need to acquire the technical knowledge, but for general quantitative literacy, reading our recommendations for statistical intuition may be sufficient.

== Recommendations ==

=== Statistical intuition ===

These are non-technical books that illustrate the concepts of statistics with real world examples. Because they illustrate the ideas in context, helpful for building statistical intuition.

* [http://www.amazon.com/Naked-Statistics-Stripping-Dread-Data-ebook/dp/B007Q6XLF2/ Naked Statistics: Stripping the Dread from the Data] by Charles Wheelan.

* [http://www.amazon.com/Signal-Noise-Art-Science-Prediction-ebook/dp/B007V65R54/ The Signal and the Noise] by Nate Silver. This book is organized around the theme of predicting the future.

* [http://www.amazon.com/The-Lady-Tasting-Tea-Revolutionized/dp/0805071342 The Lady Tasting Tea: How Statistics Revolutionized Science in the Twentieth Century] by David Salsburg. This provides an early history of statistics.

=== Frequentist approach textbook ===

The most commonly taught approach to statistics is the ''frequentist'' approach.

* [http://www.amazon.com/Statistics-Plain-English-Third-Timothy-ebook/dp/B004RM9VSY/ Statistics in Plain English] by Timothy C. Urdan is a lucid book that covers a lot of material in only 200 pages. It's somewhat terse and some readers may find it to be short on examples.
* [http://www.amazon.com/Statistics-For-Dummies-Deborah-Rumsey/dp/0470911085/ Statistics for Dummies], and [http://www.amazon.com/Statistics-For-Dummies-Deborah-Rumsey/dp/0470911085/ Statistics II for Dummies] offer a more leisurely exposition. [http://www.amazon.com/Statistics-Workbook-Dummies-Deborah-Rumsey/dp/0764584669/ Statistics Workbook for Dummies] offers exercises.

=== Bayesian statistics ===

''Bayesian statistics'' is thought by many people to be a superior alternative to frequentist statistics.

Chapter 8 of [http://www.amazon.com/Signal-Noise-Art-Science-Prediction-ebook/dp/B007V65R54/ The Signal and the Noise] by Nate Silver gives the history of Bayesian statistics and an exposition of the basics of the subject.

Some blog posts that overlap with the content of Silver's book but that the reader may find helpful for slightly different perspectives are:

* [http://lesswrong.com/lw/774/a_history_of_bayes_theorem/ A History of Bayes' Theorem] by Luke Muehlhauser.
* [http://yudkowsky.net/rational/bayes An Intuitive Explanation of Bayes' Theorem] by Eliezer Yudkowsky.
* [http://lesswrong.com/lw/2b0/bayes_theorem_illustrated_my_way/ Bayes' Theorem Illustrated (My Way)] by Komponisto.

For a full technical introduction to Bayesian statistics, [http://www.amazon.com/Introduction-Bayesian-Statistics-William-Bolstad-ebook/dp/B00D8XUJA2 Introduction to Bayesian Statistics] by William Bolstad may be helpful, but the book is very expensive.

==== Programming statistics ====

If you enjoy programming, are looking to learn programming, or will be implementing statistical algorithms in your work, these books may be good choices:

* [http://www.amazon.com/Discovering-Statistics-Using-Andy-Field-ebook/dp/B00HPZ4VVM/ Discovering Statistics Using R] by Andy Field, Jeremy Miles and Zoe Field.
* [http://www.greenteapress.com/thinkbayes/thinkbayes.pdf Think Bayes: Bayesian Statistics Made Simple] by Allen Downey teaches statistics through Python programming.
* [http://www.amazon.com/Doing-Bayesian-Data-Analysis-Tutorial/dp/0123814855/ Doing Bayesian Data Analysis: A Tutorial with R and BUGS] by John Kruschke.

=== Online classes ===

If you find it easier to learn from lectures than from a textbook, we encourage you to check out free online courses. You can try out different lecturers until you find one who you find especially easy to learn from.

* [https://www.coursera.org/ Coursera] offers ~45 statistics courses. Their offerings include [https://www.coursera.org/course/stats1 Statistics One], an introductory course.
* [https://www.edx.org/ edX] offers ~20 statistics courses, including [https://www.edx.org/course/uc-berkeleyx/uc-berkeleyx-stat2-1x-introduction-1138 Introduction to Statistics: Descriptive Statistics], [https://www.edx.org/course/uc-berkeleyx/uc-berkeleyx-stat2-3x-introduction-1533 Introduction to Statistics: Inference] and [https://www.edx.org/course/uc-berkeleyx/uc-berkeleyx-stat2-2x-introduction-1534 Introduction to Statistics: Probability], which are associated with UC Berkeley.
* [https://www.udacity.com/ Udacity] offers [https://www.udacity.com/course/st101 Intro to Statistics] and [https://www.udacity.com/course/st095 Statistics].
* [http://ocw.mit.edu/index.htm MIT OCW] offers an [http://ocw.mit.edu/courses/mathematics/ math courses] that include "Introduction to Probability and Statistics" and "Statistics for Applications."
* Carnegie Mellon University's [http://oli.cmu.edu/ Open Learning Initiative] offers [https://oli.cmu.edu/jcourse/webui/guest/join.do?section=probstat a course on probability and statistics].

Statistics.com offers over 110 [http://www.statistics.com/course-catalog/ statistics courses]. The courses are expensive: $500 for a 4-week long course. We don't have any inside knowledge of the quality of the courses.

===Cartoon books==

The blog post [http://www.r-bloggers.com/the-most-comprehensive-review-of-comic-books-teaching-statistics/ The most comprehensive review of comic books teaching statistics] discusses and reviews a number of cartoon books teaching statistics. These can be ideal for many people who want to learn statistics informally or even for people who want to learn statistics formally and are looking for a way to supplement their main learning source.

Massive online open courses

2014-06-22T03:46:01Z

Vipul:

'''Massive online open courses''' ('''MOOC'''s) are a recent development in the education space. These are courses with a single professor (assisted by a few graders) who posts video lectures, quizzes, and homeworks, along with a class comprising hundreds or perhaps even thousands of students. For many MOOCs, registering is free but access to course materials requires registration. The professors do not grade student work. Grading is typically done through peer evaluation and automated grading systems.

A large number of people who sign up for MOOCs do so without any intention of taking the course, but rather, to access the course materials for current or later use. Completion rates for MOOCs are about 10%, but completion rate for MOOCs relative to the set of people who turn in the first homework are around 40-50%.

Keep in mind that, unlike [[online high school]]s, MOOCs are not accredited courses, and they do not offer the equivalent of college credit. They do offer certificates of completion (in some cases, they also offer certificates of completion with distinction). It is possible that these may be used to convince potential employers of skills or interests, but we do not have any evidence that they have successfully been used as such. We do not have direct information regarding MOOC participation being used by students.

MOOCs can be thought of as a middle ground between watching video websites like [[Khan Academy]] or other online videos or practice systems, and [[online high school]]s. Since they're in between, they have some of the advantages of both, but they also share the disadvantages.

==External links==

* [http://appliedsciencesfoundation.org/?p=294 40 Useful Tips For Anyone Taking A MOOC]
* [http://gigaom.com/2013/08/09/how-to-pick-the-best-mooc-6-tips-from-a-coursera-junkie/ How to pick the best MOOCs: 6 tips from a Coursera junkie]

Online mathematics learning resources

2014-05-29T21:44:03Z

Vipul: /* Online practice and diagnostic assessment */

This page lists a number of online math learning resources.

==Video lectures==

We list here resources that contain large numbers of video lectures. Note that some of these resources offer videos in other subjects. Comments about quality may not apply to the other subjects.

{| class="sortable" border="1"
! Resource !! Website !! Topics covered in the video lectures !! Duration of the lectures !! Cost !! Comments on quality and links to reviews !! Computational/conceptual spectrum (scale of 1 to 5, 1 for very computational, 5 for very conceptual)
|-
| [[Center of Math]] (only math) || [http://centerofmath.org/videos/index.html Center of Math videos] || Calculus and multivariable calculus (following textbooks published by them) || 50-70 minutes || Free || The videos are very good for in-depth learning. In general, the teachers exhibit considerable conceptual depth. The videos are long, and require patience to get through ||4, maybe 5
|-
| Calculus: Single Variable by Robert Ghrist (University of Pennsylvania) on Coursera || [https://www.coursera.org/course/calcsing coursera.org/course/calcsing] || Most relevant for people taking Advanced Placement Calculus, or who have already taken Advanced Placement Calculus and want to understand calculus somewhat better || 10-20 minutes || Free, but you need to register and enroll to watch the videos || The videos are part of a course with quizzes and course materials. However, some materials may become available only at a particular time of the year because the courses run on the academic year timetable || 4 (?)
|-
| [[Math Doctor Bob]] (only math) || [http://www.mathdoctorbob.org/BasAlg.html Courses (toggle between different course types -- the link goes to basic algebra)] || High school algebra, precalculus, calculus, and multivariable calculus, plus higher math || 5-15 minutes || Free || Reasonably good videos. || 3 (?)
|-
| [[MIT OpenCourseWare]] (includes math and other subjects, math courses relevant for high schoolers include only calculus-related courses) || [http://ocw.mit.edu ocw.mit.edu], most relevant subset: [http://ocw.mit.edu/courses/find-by-topic/#cat=mathematics&subcat=calculus calculus courses] || Most relevant for people taking Advanced Placement Calculus (or equivalent) or more advanced courses || Since these are mostly actual lectures delivered in college classes, they are generally about 50 minutes long. || Free || Reasonably good videos. The [http://ocw.mit.edu/resources/res-18-005-highlights-of-calculus-spring-2010/ Highlights of Calculus] course should be the most accessible to high school students since it provides a big picture overview of calculus. (Add links to reviews?) || 3 (?)
|-

| [[Vipul's Classroom]] (only math) (COI disclosure?) || [http://www.youtube.com/user/vipulsclassroom YouTube channel] || Calculus and multvariable calculus (covers only selected topics) || 5-20 minutes || Free || Similar to Center of Math, but shorter videos often focus on more specific topics rather than on developing an entire thread. No corresponding book, but related pages at [http://calculus.subwiki.org calculus.subwiki.org] || 4 or 5
|-
| [[Art of Problem Solving]] (only math) (COI disclosure?) || [http://www.artofproblemsolving.com/Videos/ homepage for videos] || Middle school math and high school algebra and precalculus. || 5-15 minutes || Free || Focused on computational methods, but considerable emphasis on understanding the computational processes and why they work. The videos are synced with the AoPS books and courses, so you can learn the most from the videos by also buying the books. You can also use the videos to get a sense of whether you would like to try the AoPS courses. || 4 or 5 ''relative to the grade level'' (i.e., not as conceptual as the other resources, but highly conceptual relative to the grade level of the audience).
|-
| [[Dr. Chris Tisdell]] (only math) || [http://www.youtube.com/user/DrChrisTisdell YouTube channel] || calculus (integration onward), differential equations, and more advanced math || 10-25 minutes || Free || Reasonably good videos || 3 (?)
|-
| [[Thinkwell]] || [http://www.thinkwell.com thinkwell.com] || courses in high school math and calculus || 10-25 minutes || Paid (you can pay ~$150 for a year-long subscription to their video-based course, along with many supplementary materials) || Lectures are accompanied by summaries appearing on the side, that help with effective learning. We would recommend this highly if it were not for the cost. || 3, maybe 4
|-
| [[Khan Academy]] (includes math and other subjects) || [http://www.khanacademy.org khanacademy.org] || Entire school math curriculum plus large parts of the introductory college math curriculum || 5-15 minutes || Free || Functional but unremarkable. However, the ability to pause and review video lectures, combined with the comprehensive curriculum coverage and integration with worked examples, may make this worthwhile. See reviews [http://blogs.edweek.org/edweek/edtechresearcher/2012/06/dont_use_khan_academy_without_watching_mmt2k_first.html here], [http://math.stackexchange.com/questions/80202/what-does-khan-academy-have-to-offer-depth-rigor here], and [http://www.huffingtonpost.com/dr-keith-devlin/khan-academy-good-bad-or-_b_1345925.html here]|| 2, but the conceptual explanations are sometimes misguided.
|-
| [[PatrickJMT]] (only math) || [http://www.patrickjmt.com patrickjmt.com] || Large parts of high school math curriculum and beginning college curriculum, including all of calculus || 5-10 minutes || Free, but you can donate to the author || Somewhat better than Khan Academy in terms of the quality of explanation, but no integration with practice exercises. The videos are very computation-oriented and place little emphasis on conceptual understanding. (Write a separate review, link to it? Or find reviews) || 1, but conceptual explanations, when offered, are likely to be correct.
|-
| [[IntegralCALC]] (only math) || [http://integralcalc.pathwright.com/ integralcalc.pathwright.com] || all of calculus || ? || Paid, free trial || Not evaluated || ?
|}

===Our top recommendations===

We recommend the following based on the kind of learner you are:

{| class="sortable" border="1"
! Type of learner and stage !! Our top recommendation !! Supplementary resources
|-
| Middle school or early high school student (grades 6-9) who is reasonably good at school math but has not explored math much beyond school and wants to learn algebra or precalculus. || Start with the '''Art of Problem Solving''' videos (free). If you like them, consider buying the AoPS books and enrolling in the courses (costs money, but not a lot). See our [[algebra learning recommendations]] for more. || Khan Academy and PatrickJMT also cover many of the topics, but less well. We would not recommend these as primary resources, but you can use them to supplement learning for topics where you are unable to find AoPS videos. ThinkWell also has videos, but these are not free.
|-
| High school student (grades 10-12) who wants to learn calculus. || Two relatively complete course sets you can consider: (a) The '''Center of Math''' videos (free) offer a strong conceptual foundation in precalculus and calculus. It may be worth buying the textbook (~$15) to supplement the videos. (b) '''Coursera's "Calculus: Single Variable" course by Robert Ghrist'''. In addition to videos, you also have course materials that you can use to study. See our [[calculus learning recommendations]] for more. || Three other resources to consider for videos on some subtopics are: '''Vipul's Classroom''' (for a more conceptual version), '''Math Doctor Bob''' (for a reasonable mix of concepts and computational techniques), and '''MIT OpenCourseWare'''.
|-
| High school student (grades 10-12) who has already learned Advanced Placement (AP) Calculus or equivalent but wants to understand some of the trickier topics of single variable calculus at the level taught in top colleges. || The majority of the calculus playlists at '''Vipul's Classroom''' are specifically designed for people who have already studied calculus at the AP level (or slightly less) and now want to understand it at the level of a college course. See our [[calculus learning recommendations]] page for more. || We also recommend watching suitable subsets of '''Robert Ghrist's Coursera course''' and the '''Center of Math''' videos.
|-
| High school student (grades 10-12) who has already learned Advanced Placement (AP) Calculus or equivalent and wants to study multivariable calculus. || Two relatively complete course sets you can consider: (a) The '''Center of Math''' videos (free) offer a strong conceptual foundation in multivariable calculus. It may be worth buying the textbook (~$10) to supplement the videos. (b) '''MIT OpenCourseWare''' multivariable calculus course. || '''Math Doctor Bob''' has a set of videos on multivariable calculus. '''Vipul's Classroom''' has playlists dealing with partial derivatives, gradient vectors, directional derivatives, and other select topics of multivariable calculus.
|}

==Online practice and diagnostic assessment==

{| class="sortable" border="1"
! Resource !! Website !! Topics covered for diagnosis and assessment !! Cost !! Dependencies between topics included? !! Detailed solutions provided? !! Can learners keep track of their mastery?
|-
| [[ALEKS]] || [http://www.aleks.com aleks.com] || Most of school math and calculus || up to $20/month, but free 14-day trial || Yes, in a very detailed form using [[wikipedia:knowledge space|knowledge spaces]] || Yes || Yes
|-
| [[Khan Academy]] || [http://www.khanacademy.org khanacademy.org] || Most of school math and calculus || Free || Yes, but very preliminary || Yes, and partial hints also provided || Yes
|-
| [[Brilliant]] || [http://www.brilliant.org brilliant.org] || Middle school and high school math and calculus, may not comprehensively cover all topics || Free || No, but they may be building it soon || Yes (?) || Yes
|-
| [[IXL]] || [http://www.ixl.com ixl.com] || Most of school math || about $20/month, but heavy discounts for use in classrooms, with $200/year per classroom of 30 students || ? || Yes ||
|-
| Varsity Tutors || [http://www.varsitytutors.com varsitytutors.com], practice tests [http://www.varsitytutors.com/practice-tests here] || Many subjects, including mathematics || practice tests are free; there are other resources on the website || || ||
|}

Online mathematics learning resources

2014-05-29T21:42:03Z

Vipul: /* Online practice and diagnostic assessment */

This page lists a number of online math learning resources.

==Video lectures==

We list here resources that contain large numbers of video lectures. Note that some of these resources offer videos in other subjects. Comments about quality may not apply to the other subjects.

{| class="sortable" border="1"
! Resource !! Website !! Topics covered in the video lectures !! Duration of the lectures !! Cost !! Comments on quality and links to reviews !! Computational/conceptual spectrum (scale of 1 to 5, 1 for very computational, 5 for very conceptual)
|-
| [[Center of Math]] (only math) || [http://centerofmath.org/videos/index.html Center of Math videos] || Calculus and multivariable calculus (following textbooks published by them) || 50-70 minutes || Free || The videos are very good for in-depth learning. In general, the teachers exhibit considerable conceptual depth. The videos are long, and require patience to get through ||4, maybe 5
|-
| Calculus: Single Variable by Robert Ghrist (University of Pennsylvania) on Coursera || [https://www.coursera.org/course/calcsing coursera.org/course/calcsing] || Most relevant for people taking Advanced Placement Calculus, or who have already taken Advanced Placement Calculus and want to understand calculus somewhat better || 10-20 minutes || Free, but you need to register and enroll to watch the videos || The videos are part of a course with quizzes and course materials. However, some materials may become available only at a particular time of the year because the courses run on the academic year timetable || 4 (?)
|-
| [[Math Doctor Bob]] (only math) || [http://www.mathdoctorbob.org/BasAlg.html Courses (toggle between different course types -- the link goes to basic algebra)] || High school algebra, precalculus, calculus, and multivariable calculus, plus higher math || 5-15 minutes || Free || Reasonably good videos. || 3 (?)
|-
| [[MIT OpenCourseWare]] (includes math and other subjects, math courses relevant for high schoolers include only calculus-related courses) || [http://ocw.mit.edu ocw.mit.edu], most relevant subset: [http://ocw.mit.edu/courses/find-by-topic/#cat=mathematics&subcat=calculus calculus courses] || Most relevant for people taking Advanced Placement Calculus (or equivalent) or more advanced courses || Since these are mostly actual lectures delivered in college classes, they are generally about 50 minutes long. || Free || Reasonably good videos. The [http://ocw.mit.edu/resources/res-18-005-highlights-of-calculus-spring-2010/ Highlights of Calculus] course should be the most accessible to high school students since it provides a big picture overview of calculus. (Add links to reviews?) || 3 (?)
|-

| [[Vipul's Classroom]] (only math) (COI disclosure?) || [http://www.youtube.com/user/vipulsclassroom YouTube channel] || Calculus and multvariable calculus (covers only selected topics) || 5-20 minutes || Free || Similar to Center of Math, but shorter videos often focus on more specific topics rather than on developing an entire thread. No corresponding book, but related pages at [http://calculus.subwiki.org calculus.subwiki.org] || 4 or 5
|-
| [[Art of Problem Solving]] (only math) (COI disclosure?) || [http://www.artofproblemsolving.com/Videos/ homepage for videos] || Middle school math and high school algebra and precalculus. || 5-15 minutes || Free || Focused on computational methods, but considerable emphasis on understanding the computational processes and why they work. The videos are synced with the AoPS books and courses, so you can learn the most from the videos by also buying the books. You can also use the videos to get a sense of whether you would like to try the AoPS courses. || 4 or 5 ''relative to the grade level'' (i.e., not as conceptual as the other resources, but highly conceptual relative to the grade level of the audience).
|-
| [[Dr. Chris Tisdell]] (only math) || [http://www.youtube.com/user/DrChrisTisdell YouTube channel] || calculus (integration onward), differential equations, and more advanced math || 10-25 minutes || Free || Reasonably good videos || 3 (?)
|-
| [[Thinkwell]] || [http://www.thinkwell.com thinkwell.com] || courses in high school math and calculus || 10-25 minutes || Paid (you can pay ~$150 for a year-long subscription to their video-based course, along with many supplementary materials) || Lectures are accompanied by summaries appearing on the side, that help with effective learning. We would recommend this highly if it were not for the cost. || 3, maybe 4
|-
| [[Khan Academy]] (includes math and other subjects) || [http://www.khanacademy.org khanacademy.org] || Entire school math curriculum plus large parts of the introductory college math curriculum || 5-15 minutes || Free || Functional but unremarkable. However, the ability to pause and review video lectures, combined with the comprehensive curriculum coverage and integration with worked examples, may make this worthwhile. See reviews [http://blogs.edweek.org/edweek/edtechresearcher/2012/06/dont_use_khan_academy_without_watching_mmt2k_first.html here], [http://math.stackexchange.com/questions/80202/what-does-khan-academy-have-to-offer-depth-rigor here], and [http://www.huffingtonpost.com/dr-keith-devlin/khan-academy-good-bad-or-_b_1345925.html here]|| 2, but the conceptual explanations are sometimes misguided.
|-
| [[PatrickJMT]] (only math) || [http://www.patrickjmt.com patrickjmt.com] || Large parts of high school math curriculum and beginning college curriculum, including all of calculus || 5-10 minutes || Free, but you can donate to the author || Somewhat better than Khan Academy in terms of the quality of explanation, but no integration with practice exercises. The videos are very computation-oriented and place little emphasis on conceptual understanding. (Write a separate review, link to it? Or find reviews) || 1, but conceptual explanations, when offered, are likely to be correct.
|-
| [[IntegralCALC]] (only math) || [http://integralcalc.pathwright.com/ integralcalc.pathwright.com] || all of calculus || ? || Paid, free trial || Not evaluated || ?
|}

===Our top recommendations===

We recommend the following based on the kind of learner you are:

{| class="sortable" border="1"
! Type of learner and stage !! Our top recommendation !! Supplementary resources
|-
| Middle school or early high school student (grades 6-9) who is reasonably good at school math but has not explored math much beyond school and wants to learn algebra or precalculus. || Start with the '''Art of Problem Solving''' videos (free). If you like them, consider buying the AoPS books and enrolling in the courses (costs money, but not a lot). See our [[algebra learning recommendations]] for more. || Khan Academy and PatrickJMT also cover many of the topics, but less well. We would not recommend these as primary resources, but you can use them to supplement learning for topics where you are unable to find AoPS videos. ThinkWell also has videos, but these are not free.
|-
| High school student (grades 10-12) who wants to learn calculus. || Two relatively complete course sets you can consider: (a) The '''Center of Math''' videos (free) offer a strong conceptual foundation in precalculus and calculus. It may be worth buying the textbook (~$15) to supplement the videos. (b) '''Coursera's "Calculus: Single Variable" course by Robert Ghrist'''. In addition to videos, you also have course materials that you can use to study. See our [[calculus learning recommendations]] for more. || Three other resources to consider for videos on some subtopics are: '''Vipul's Classroom''' (for a more conceptual version), '''Math Doctor Bob''' (for a reasonable mix of concepts and computational techniques), and '''MIT OpenCourseWare'''.
|-
| High school student (grades 10-12) who has already learned Advanced Placement (AP) Calculus or equivalent but wants to understand some of the trickier topics of single variable calculus at the level taught in top colleges. || The majority of the calculus playlists at '''Vipul's Classroom''' are specifically designed for people who have already studied calculus at the AP level (or slightly less) and now want to understand it at the level of a college course. See our [[calculus learning recommendations]] page for more. || We also recommend watching suitable subsets of '''Robert Ghrist's Coursera course''' and the '''Center of Math''' videos.
|-
| High school student (grades 10-12) who has already learned Advanced Placement (AP) Calculus or equivalent and wants to study multivariable calculus. || Two relatively complete course sets you can consider: (a) The '''Center of Math''' videos (free) offer a strong conceptual foundation in multivariable calculus. It may be worth buying the textbook (~$10) to supplement the videos. (b) '''MIT OpenCourseWare''' multivariable calculus course. || '''Math Doctor Bob''' has a set of videos on multivariable calculus. '''Vipul's Classroom''' has playlists dealing with partial derivatives, gradient vectors, directional derivatives, and other select topics of multivariable calculus.
|}

==Online practice and diagnostic assessment==

{| class="sortable" border="1"
! Resource !! Website !! Topics covered for diagnosis and assessment !! Cost !! Dependencies between topics included? !! Detailed solutions provided? !! Can learners keep track of their mastery?
|-
| [[ALEKS]] || [http://www.aleks.com aleks.com] || Most of school math and calculus || up to $20/month, but free 14-day trial || Yes, in a very detailed form using [[wikipedia:knowledge space|knowledge spaces]] || Yes || Yes
|-
| [[Khan Academy]] || [http://www.khanacademy.org khanacademy.org] || Most of school math and calculus || Free || Yes, but very preliminary || Yes, and partial hints also provided || Yes
|-
| [[Brilliant]] || [http://www.brilliant.org brilliant.org] || Middle school and high school math and calculus, may not comprehensively cover all topics || Free || No, but they may be building it soon || Yes (?) || Yes
|-
| [[IXL]] || [http://www.ixl.com ixl.com] || Most of school math || about $20/month, but heavy discounts for use in classrooms, with $200/year per classroom of 30 students || ? || Yes
|-
| Varsity Tutors || [http://www.varsitytutors.com varsitytutors.com], practice tests [http://www.varsitytutors.com/practice-tests here] || Many subjects, including mathematics || practice tests are free; there are other resources on the website || || ||
|}

Online mathematics learning resources

2014-05-29T21:41:52Z

Vipul: /* Online practice and diagnostic assessment */

This page lists a number of online math learning resources.

==Video lectures==

We list here resources that contain large numbers of video lectures. Note that some of these resources offer videos in other subjects. Comments about quality may not apply to the other subjects.

{| class="sortable" border="1"
! Resource !! Website !! Topics covered in the video lectures !! Duration of the lectures !! Cost !! Comments on quality and links to reviews !! Computational/conceptual spectrum (scale of 1 to 5, 1 for very computational, 5 for very conceptual)
|-
| [[Center of Math]] (only math) || [http://centerofmath.org/videos/index.html Center of Math videos] || Calculus and multivariable calculus (following textbooks published by them) || 50-70 minutes || Free || The videos are very good for in-depth learning. In general, the teachers exhibit considerable conceptual depth. The videos are long, and require patience to get through ||4, maybe 5
|-
| Calculus: Single Variable by Robert Ghrist (University of Pennsylvania) on Coursera || [https://www.coursera.org/course/calcsing coursera.org/course/calcsing] || Most relevant for people taking Advanced Placement Calculus, or who have already taken Advanced Placement Calculus and want to understand calculus somewhat better || 10-20 minutes || Free, but you need to register and enroll to watch the videos || The videos are part of a course with quizzes and course materials. However, some materials may become available only at a particular time of the year because the courses run on the academic year timetable || 4 (?)
|-
| [[Math Doctor Bob]] (only math) || [http://www.mathdoctorbob.org/BasAlg.html Courses (toggle between different course types -- the link goes to basic algebra)] || High school algebra, precalculus, calculus, and multivariable calculus, plus higher math || 5-15 minutes || Free || Reasonably good videos. || 3 (?)
|-
| [[MIT OpenCourseWare]] (includes math and other subjects, math courses relevant for high schoolers include only calculus-related courses) || [http://ocw.mit.edu ocw.mit.edu], most relevant subset: [http://ocw.mit.edu/courses/find-by-topic/#cat=mathematics&subcat=calculus calculus courses] || Most relevant for people taking Advanced Placement Calculus (or equivalent) or more advanced courses || Since these are mostly actual lectures delivered in college classes, they are generally about 50 minutes long. || Free || Reasonably good videos. The [http://ocw.mit.edu/resources/res-18-005-highlights-of-calculus-spring-2010/ Highlights of Calculus] course should be the most accessible to high school students since it provides a big picture overview of calculus. (Add links to reviews?) || 3 (?)
|-

| [[Vipul's Classroom]] (only math) (COI disclosure?) || [http://www.youtube.com/user/vipulsclassroom YouTube channel] || Calculus and multvariable calculus (covers only selected topics) || 5-20 minutes || Free || Similar to Center of Math, but shorter videos often focus on more specific topics rather than on developing an entire thread. No corresponding book, but related pages at [http://calculus.subwiki.org calculus.subwiki.org] || 4 or 5
|-
| [[Art of Problem Solving]] (only math) (COI disclosure?) || [http://www.artofproblemsolving.com/Videos/ homepage for videos] || Middle school math and high school algebra and precalculus. || 5-15 minutes || Free || Focused on computational methods, but considerable emphasis on understanding the computational processes and why they work. The videos are synced with the AoPS books and courses, so you can learn the most from the videos by also buying the books. You can also use the videos to get a sense of whether you would like to try the AoPS courses. || 4 or 5 ''relative to the grade level'' (i.e., not as conceptual as the other resources, but highly conceptual relative to the grade level of the audience).
|-
| [[Dr. Chris Tisdell]] (only math) || [http://www.youtube.com/user/DrChrisTisdell YouTube channel] || calculus (integration onward), differential equations, and more advanced math || 10-25 minutes || Free || Reasonably good videos || 3 (?)
|-
| [[Thinkwell]] || [http://www.thinkwell.com thinkwell.com] || courses in high school math and calculus || 10-25 minutes || Paid (you can pay ~$150 for a year-long subscription to their video-based course, along with many supplementary materials) || Lectures are accompanied by summaries appearing on the side, that help with effective learning. We would recommend this highly if it were not for the cost. || 3, maybe 4
|-
| [[Khan Academy]] (includes math and other subjects) || [http://www.khanacademy.org khanacademy.org] || Entire school math curriculum plus large parts of the introductory college math curriculum || 5-15 minutes || Free || Functional but unremarkable. However, the ability to pause and review video lectures, combined with the comprehensive curriculum coverage and integration with worked examples, may make this worthwhile. See reviews [http://blogs.edweek.org/edweek/edtechresearcher/2012/06/dont_use_khan_academy_without_watching_mmt2k_first.html here], [http://math.stackexchange.com/questions/80202/what-does-khan-academy-have-to-offer-depth-rigor here], and [http://www.huffingtonpost.com/dr-keith-devlin/khan-academy-good-bad-or-_b_1345925.html here]|| 2, but the conceptual explanations are sometimes misguided.
|-
| [[PatrickJMT]] (only math) || [http://www.patrickjmt.com patrickjmt.com] || Large parts of high school math curriculum and beginning college curriculum, including all of calculus || 5-10 minutes || Free, but you can donate to the author || Somewhat better than Khan Academy in terms of the quality of explanation, but no integration with practice exercises. The videos are very computation-oriented and place little emphasis on conceptual understanding. (Write a separate review, link to it? Or find reviews) || 1, but conceptual explanations, when offered, are likely to be correct.
|-
| [[IntegralCALC]] (only math) || [http://integralcalc.pathwright.com/ integralcalc.pathwright.com] || all of calculus || ? || Paid, free trial || Not evaluated || ?
|}

===Our top recommendations===

We recommend the following based on the kind of learner you are:

{| class="sortable" border="1"
! Type of learner and stage !! Our top recommendation !! Supplementary resources
|-
| Middle school or early high school student (grades 6-9) who is reasonably good at school math but has not explored math much beyond school and wants to learn algebra or precalculus. || Start with the '''Art of Problem Solving''' videos (free). If you like them, consider buying the AoPS books and enrolling in the courses (costs money, but not a lot). See our [[algebra learning recommendations]] for more. || Khan Academy and PatrickJMT also cover many of the topics, but less well. We would not recommend these as primary resources, but you can use them to supplement learning for topics where you are unable to find AoPS videos. ThinkWell also has videos, but these are not free.
|-
| High school student (grades 10-12) who wants to learn calculus. || Two relatively complete course sets you can consider: (a) The '''Center of Math''' videos (free) offer a strong conceptual foundation in precalculus and calculus. It may be worth buying the textbook (~$15) to supplement the videos. (b) '''Coursera's "Calculus: Single Variable" course by Robert Ghrist'''. In addition to videos, you also have course materials that you can use to study. See our [[calculus learning recommendations]] for more. || Three other resources to consider for videos on some subtopics are: '''Vipul's Classroom''' (for a more conceptual version), '''Math Doctor Bob''' (for a reasonable mix of concepts and computational techniques), and '''MIT OpenCourseWare'''.
|-
| High school student (grades 10-12) who has already learned Advanced Placement (AP) Calculus or equivalent but wants to understand some of the trickier topics of single variable calculus at the level taught in top colleges. || The majority of the calculus playlists at '''Vipul's Classroom''' are specifically designed for people who have already studied calculus at the AP level (or slightly less) and now want to understand it at the level of a college course. See our [[calculus learning recommendations]] page for more. || We also recommend watching suitable subsets of '''Robert Ghrist's Coursera course''' and the '''Center of Math''' videos.
|-
| High school student (grades 10-12) who has already learned Advanced Placement (AP) Calculus or equivalent and wants to study multivariable calculus. || Two relatively complete course sets you can consider: (a) The '''Center of Math''' videos (free) offer a strong conceptual foundation in multivariable calculus. It may be worth buying the textbook (~$10) to supplement the videos. (b) '''MIT OpenCourseWare''' multivariable calculus course. || '''Math Doctor Bob''' has a set of videos on multivariable calculus. '''Vipul's Classroom''' has playlists dealing with partial derivatives, gradient vectors, directional derivatives, and other select topics of multivariable calculus.
|}

==Online practice and diagnostic assessment==

{| class="sortable" border="1"
! Resource !! Website !! Topics covered for diagnosis and assessment !! Cost !! Dependencies between topics included? !! Detailed solutions provided? !! Can learners keep track of their mastery?
|-
| [[ALEKS]] || [http://www.aleks.com aleks.com] || Most of school math and calculus || up to $20/month, but free 14-day trial || Yes, in a very detailed form using [[wikipedia:knowledge space|knowledge spaces]] || Yes || Yes
|-
| [[Khan Academy]] || [http://www.khanacademy.org khanacademy.org] || Most of school math and calculus || Free || Yes, but very preliminary || Yes, and partial hints also provided || Yes
|-
| [[Brilliant]] || [http://www.brilliant.org brilliant.org] || Middle school and high school math and calculus, may not comprehensively cover all topics || Free || No, but they may be building it soon || Yes (?) || Yes
|-
| [[IXL]] || [http://www.ixl.com ixl.com] || Most of school math || about $20/month, but heavy discounts for use in classrooms, with $200/year per classroom of 30 students || ? || Yes
|-
| Varsity Tutors || [http://www.varsitytutors.com], practice tests [http://www.varsitytutors.com/practice-tests here] || Many subjects, including mathematics || practice tests are free; there are other resources on the website || || ||
|}

Online mathematics learning resources

2014-05-29T21:41:29Z

Vipul: /* Online practice and diagnostic assessment */

This page lists a number of online math learning resources.

==Video lectures==

We list here resources that contain large numbers of video lectures. Note that some of these resources offer videos in other subjects. Comments about quality may not apply to the other subjects.

{| class="sortable" border="1"
! Resource !! Website !! Topics covered in the video lectures !! Duration of the lectures !! Cost !! Comments on quality and links to reviews !! Computational/conceptual spectrum (scale of 1 to 5, 1 for very computational, 5 for very conceptual)
|-
| [[Center of Math]] (only math) || [http://centerofmath.org/videos/index.html Center of Math videos] || Calculus and multivariable calculus (following textbooks published by them) || 50-70 minutes || Free || The videos are very good for in-depth learning. In general, the teachers exhibit considerable conceptual depth. The videos are long, and require patience to get through ||4, maybe 5
|-
| Calculus: Single Variable by Robert Ghrist (University of Pennsylvania) on Coursera || [https://www.coursera.org/course/calcsing coursera.org/course/calcsing] || Most relevant for people taking Advanced Placement Calculus, or who have already taken Advanced Placement Calculus and want to understand calculus somewhat better || 10-20 minutes || Free, but you need to register and enroll to watch the videos || The videos are part of a course with quizzes and course materials. However, some materials may become available only at a particular time of the year because the courses run on the academic year timetable || 4 (?)
|-
| [[Math Doctor Bob]] (only math) || [http://www.mathdoctorbob.org/BasAlg.html Courses (toggle between different course types -- the link goes to basic algebra)] || High school algebra, precalculus, calculus, and multivariable calculus, plus higher math || 5-15 minutes || Free || Reasonably good videos. || 3 (?)
|-
| [[MIT OpenCourseWare]] (includes math and other subjects, math courses relevant for high schoolers include only calculus-related courses) || [http://ocw.mit.edu ocw.mit.edu], most relevant subset: [http://ocw.mit.edu/courses/find-by-topic/#cat=mathematics&subcat=calculus calculus courses] || Most relevant for people taking Advanced Placement Calculus (or equivalent) or more advanced courses || Since these are mostly actual lectures delivered in college classes, they are generally about 50 minutes long. || Free || Reasonably good videos. The [http://ocw.mit.edu/resources/res-18-005-highlights-of-calculus-spring-2010/ Highlights of Calculus] course should be the most accessible to high school students since it provides a big picture overview of calculus. (Add links to reviews?) || 3 (?)
|-

| [[Vipul's Classroom]] (only math) (COI disclosure?) || [http://www.youtube.com/user/vipulsclassroom YouTube channel] || Calculus and multvariable calculus (covers only selected topics) || 5-20 minutes || Free || Similar to Center of Math, but shorter videos often focus on more specific topics rather than on developing an entire thread. No corresponding book, but related pages at [http://calculus.subwiki.org calculus.subwiki.org] || 4 or 5
|-
| [[Art of Problem Solving]] (only math) (COI disclosure?) || [http://www.artofproblemsolving.com/Videos/ homepage for videos] || Middle school math and high school algebra and precalculus. || 5-15 minutes || Free || Focused on computational methods, but considerable emphasis on understanding the computational processes and why they work. The videos are synced with the AoPS books and courses, so you can learn the most from the videos by also buying the books. You can also use the videos to get a sense of whether you would like to try the AoPS courses. || 4 or 5 ''relative to the grade level'' (i.e., not as conceptual as the other resources, but highly conceptual relative to the grade level of the audience).
|-
| [[Dr. Chris Tisdell]] (only math) || [http://www.youtube.com/user/DrChrisTisdell YouTube channel] || calculus (integration onward), differential equations, and more advanced math || 10-25 minutes || Free || Reasonably good videos || 3 (?)
|-
| [[Thinkwell]] || [http://www.thinkwell.com thinkwell.com] || courses in high school math and calculus || 10-25 minutes || Paid (you can pay ~$150 for a year-long subscription to their video-based course, along with many supplementary materials) || Lectures are accompanied by summaries appearing on the side, that help with effective learning. We would recommend this highly if it were not for the cost. || 3, maybe 4
|-
| [[Khan Academy]] (includes math and other subjects) || [http://www.khanacademy.org khanacademy.org] || Entire school math curriculum plus large parts of the introductory college math curriculum || 5-15 minutes || Free || Functional but unremarkable. However, the ability to pause and review video lectures, combined with the comprehensive curriculum coverage and integration with worked examples, may make this worthwhile. See reviews [http://blogs.edweek.org/edweek/edtechresearcher/2012/06/dont_use_khan_academy_without_watching_mmt2k_first.html here], [http://math.stackexchange.com/questions/80202/what-does-khan-academy-have-to-offer-depth-rigor here], and [http://www.huffingtonpost.com/dr-keith-devlin/khan-academy-good-bad-or-_b_1345925.html here]|| 2, but the conceptual explanations are sometimes misguided.
|-
| [[PatrickJMT]] (only math) || [http://www.patrickjmt.com patrickjmt.com] || Large parts of high school math curriculum and beginning college curriculum, including all of calculus || 5-10 minutes || Free, but you can donate to the author || Somewhat better than Khan Academy in terms of the quality of explanation, but no integration with practice exercises. The videos are very computation-oriented and place little emphasis on conceptual understanding. (Write a separate review, link to it? Or find reviews) || 1, but conceptual explanations, when offered, are likely to be correct.
|-
| [[IntegralCALC]] (only math) || [http://integralcalc.pathwright.com/ integralcalc.pathwright.com] || all of calculus || ? || Paid, free trial || Not evaluated || ?
|}

===Our top recommendations===

We recommend the following based on the kind of learner you are:

{| class="sortable" border="1"
! Type of learner and stage !! Our top recommendation !! Supplementary resources
|-
| Middle school or early high school student (grades 6-9) who is reasonably good at school math but has not explored math much beyond school and wants to learn algebra or precalculus. || Start with the '''Art of Problem Solving''' videos (free). If you like them, consider buying the AoPS books and enrolling in the courses (costs money, but not a lot). See our [[algebra learning recommendations]] for more. || Khan Academy and PatrickJMT also cover many of the topics, but less well. We would not recommend these as primary resources, but you can use them to supplement learning for topics where you are unable to find AoPS videos. ThinkWell also has videos, but these are not free.
|-
| High school student (grades 10-12) who wants to learn calculus. || Two relatively complete course sets you can consider: (a) The '''Center of Math''' videos (free) offer a strong conceptual foundation in precalculus and calculus. It may be worth buying the textbook (~$15) to supplement the videos. (b) '''Coursera's "Calculus: Single Variable" course by Robert Ghrist'''. In addition to videos, you also have course materials that you can use to study. See our [[calculus learning recommendations]] for more. || Three other resources to consider for videos on some subtopics are: '''Vipul's Classroom''' (for a more conceptual version), '''Math Doctor Bob''' (for a reasonable mix of concepts and computational techniques), and '''MIT OpenCourseWare'''.
|-
| High school student (grades 10-12) who has already learned Advanced Placement (AP) Calculus or equivalent but wants to understand some of the trickier topics of single variable calculus at the level taught in top colleges. || The majority of the calculus playlists at '''Vipul's Classroom''' are specifically designed for people who have already studied calculus at the AP level (or slightly less) and now want to understand it at the level of a college course. See our [[calculus learning recommendations]] page for more. || We also recommend watching suitable subsets of '''Robert Ghrist's Coursera course''' and the '''Center of Math''' videos.
|-
| High school student (grades 10-12) who has already learned Advanced Placement (AP) Calculus or equivalent and wants to study multivariable calculus. || Two relatively complete course sets you can consider: (a) The '''Center of Math''' videos (free) offer a strong conceptual foundation in multivariable calculus. It may be worth buying the textbook (~$10) to supplement the videos. (b) '''MIT OpenCourseWare''' multivariable calculus course. || '''Math Doctor Bob''' has a set of videos on multivariable calculus. '''Vipul's Classroom''' has playlists dealing with partial derivatives, gradient vectors, directional derivatives, and other select topics of multivariable calculus.
|}

==Online practice and diagnostic assessment==

{| class="sortable" border="1"
! Resource !! Website !! Topics covered for diagnosis and assessment !! Cost !! Dependencies between topics included? !! Detailed solutions provided? !! Can learners keep track of their mastery?
|-
| [[ALEKS]] || [http://www.aleks.com aleks.com] || Most of school math and calculus || up to $20/month, but free 14-day trial || Yes, in a very detailed form using [[wikipedia:knowledge space|knowledge spaces]] || Yes || Yes
|-
| [[Khan Academy]] || [http://www.khanacademy.org khanacademy.org] || Most of school math and calculus || Free || Yes, but very preliminary || Yes, and partial hints also provided || Yes
|-
| [[Brilliant]] || [http://www.brilliant.org brilliant.org] || Middle school and high school math and calculus, may not comprehensively cover all topics || Free || No, but they may be building it soon || Yes (?) || Yes
|-
| [[IXL]] || [http://www.ixl.com ixl.com] || Most of school math || about $20/month, but heavy discounts for use in classrooms, with $200/year per classroom of 30 students || ? || Yes
|-
| Varsity Tutors || [http://www.varsitytutors.com], practice tests [http://www.varsitytutors.com/practice-tests here] || Many subjects, including mathematics || practice tests are free; there are other resources on the website
|}

Effective altruism learning resources

2014-05-20T15:08:01Z

Vipul: /* Courses */

Since the mid-2000s, [[wikipedia:effective altruism|effective altruism]] has grown considerably as a movement. The goal is to choose one's altruistic actions with the goal of doing as much good as possible and as effectively as possible. There are a number of different groups promoting effective altruism, and their websites provide information that can help you get started on understanding the general principles.

==Trello list of starting points==

See [https://trello.com/b/rm3da7HZ/effective-altruist-introductory-resources Effective Altruist Introductory Resources] for a list of books, videos, and blogs intended to introduce people to effective altruist ideas. The list is not created or maintained by us.

==Organizations or group websites interested in effective altruism==

{| class="sortable" border="1"
! Organization/website !! What they do !! Starting point for learners !! Things to follow and read
|-
| [http://www.effective-altruism.com/ Effective Altruism blog] || People directly involved with effective altruism blog their thoughts on the subject. || Blog posts linked on their [http://www.effective-altruism.com/about-2/ about page]: [http://www.effective-altruism.com/what-effective-altruism/ What is Effective Altruism?] by William MacAskill, [http://www.effective-altruism.com/effective-altruism/ Effective Altruism] by Holden Karnofsky, and [http://www.effective-altruism.com/four-focus-areas-effective-altruism/ Four Focus Areas of Effective Altruism] by Luke Muehlhauser. || The blog might be worth following.
|-
| [http://www.givewell.org GiveWell] || Non-profit dedicated to doing the most good possible. Their primary consumer-facing product is recommendations for top charities to donate to, across all causes (rather than providing a top charity separately for each cause). In addition, they evaluate claims about a diverse range of activities (such as scientific research and political advocacy) that people have claimed might be highly effective ways of improving the world. || [http://www.givewell.org/giving101 Giving 101: the basics] is a useful primer that reviews many basic effective altruism ideas, such as differences between standards of living in different parts of the world, [[wikipedia:room for more funding|room for more funding]] and triage (the one-charity argument). || Follow their [http://blog.givewell.org blog] and skim through their detailed reviews of [http://givewell.org/top-charities top charities] as well as their [http://www.givewell.org/shallow shallow cause overviews].
|-
| [http://givingwhatwecan.org Giving What We Can] || Non-profit dedicated to advocating that people donate more money to charity, as well as providing suggestions about top charities to donate to. They rely on GiveWell's research plus some in-house research to make recommendations. || The [http://givingwhatwecan.org/where-to-give/methodology charity evaluation methodology] might be the most educational and informative place to start, but it may be fruitful to check out the [http://givingwhatwecan.org/why-give why give?] and [http://givingwhatwecan.org/where-to-give where to give] pages first to get some background and context. || Follow their [http://givingwhatwecan.org/blog blog]
|}

==Courses==

* [https://www.coursera.org/course/practicalethics Practical Ethics] by Peter Singer on Coursera
* [https://www.edx.org/course/mitx/mitx-jpal101x-evaluating-social-programs-1532 Evaluating social programs] by the Poverty Action Lab on EdX

==Effective altruism in connection with career choice and life trajectory==

{{further|[[Choosing a career with effective altruism as a dominant consideration]]}}
{| class="sortable" border="1"
! Organization !! What they do !! Starting point for learners !! Things to follow and read
|-
| [http://80000hours.org/ 80,000 Hours] || Combine publicly available research as well as personalized career guidance to guide people on career choice, where the people they guide have effective altruism as a primary consideration in career choice. || [http://80000hours.org/research Research page] || Their [http://80000hours.org/blog blog] is worth following if this topic interests you.
|}

==Personal blogs of individuals interested in effective altruism==

* Brian Tomasik is an effective altruist writer who maintains a website [http://www.utilitarian-essays.com/ here].
* Ryan Carey is an effective altruism movement-builder who is interested in guiding young people interested in effective altruism. His website is [http://careyryan.com/ here]
* Peter Hurford blogs regularly about topics closely related to effective altruism and utilitarian ethics [http://www.everydayutilitarian.com/ here].
* Bastian Stern's website [http://utilitarianism.bastianstern.com/ compiles resources on utilitarianism and effective altruism].
* Paul Christiano maintains a [http://rationalaltruist.com/ blog on rational altruism].

==Other useful starting points==

* [http://lesswrong.com/lw/37f/efficient_charity/ Efficient Charity] by multifoliaterose
* [http://lesswrong.com/lw/3gj/efficient_charity_do_unto_others/ Efficient Charity: Do Unto Others...] by Yvain

==Combined feeds==

* [http://www.rssmix.com/u/3890223/rss.xml EA Medley] maintained by Peter Hurford is a RSS feed combining many effective altruism-related blogs.

==Online communities for general discussion==

* [https://www.facebook.com/groups/effective.altruists/ Effective Altruists Facebook group] (read [http://effectivealtruism.info/?page_id=10 here] for the group guidelines)
* The comments sections of some of the blogs linked above.
* [http://www.lesswrong.com LessWrong] often has blog posts on effective altruism and many people in the LessWrong community are interested in effective altruism.
* You might also be interested in forums related to utilitarianism, including [http://www.felicifia.com Felicifia] and many Facebook groups.

==Key terminology==

Links are to Wikipedia pages or other canonical sources:

* [[Wikipedia:Earning to give|Earning to give]]
* [[Wikipedia:Room for more funding|Room for more funding]]

==See also==

* [[Development economics reading recommendations]]
* [[Economics reading recommendations]]
* [[Economics learning recommendations]]

College portal

2014-05-20T01:01:17Z

Vipul: /* General information */

This is a portal page linking to various pages related to college life. There is some overlap between the pages here and those in the [[high school portal]].

==College selection==

* [[College: deciding whether to go]]
* [[College selection: factors to consider]]
* [[College selection: getting reliable information about colleges]]
* [[College selection: deciding based on your intended major]]
* [[Public university versus private university]]
* [[Pros and cons of attending a large college]]

==College applications and admissions==

We're still working on this, but of particular interest may be [[college admissions for homeschooled students]].

We also encourage people interested in learning more about college admissions to check out [[College Confidential]], one of the best sources of information on the subject. Other potential sources of information are [[Quora]], [[About.com]], and StudentAdvisor.

==Between high school and college==

See [[time between the end of high school and the beginning of college]] (partially done).

==College academics==

===General information===

* [[College lecture class formats]]
* [[College course dependencies]]
* [[Auditing courses]]
* [[Office hours]]
* [[Making a positive impression on teachers]]

===Choice of major===

* [[Choosing a college major: factors to consider]]
* See [[:Category:College majors|the college majors category]] for links to pages about different college majors.
* [[Quora answers about college majors]] gives a useful collection of links to Quora answers about a wide range of college majors.

===Course structure and learning recommendations===

* [[Lower division undergraduate mathematics]]: See also [[lower division undergraduate mathematics course structure]] and [[lower division undergraduate mathematics learning recommendations]]
* [[Upper division undergraduate mathematics]]: See also [[upper division undergraduate mathematics course structure]] and [[upper division undergraduate mathematics learning recommendations]]
==Navigating college==

===Typical situations===

* [[Course selection]]
* '''FORTHCOMING''': Understanding and optimizing for the credit, placement, and enrollment structure
* '''FORTHCOMING''': Study abroad programs

===Unusual situations===

* [[Graduating college early]]
* [[Taking a gap year between high school and college]]

===Alternatives===

* [[Alternatives to college]]

==Career planning==

You've got a long time to think about careers, but it's good to get a head start in exploring different careers by reading up about them and observing them in action. See our pages:

* [[Preparing for a career during high school and college]]
* [[Career selection: factors to consider]]
* [[:Category:Career options|The career options category]] lists pages discussing various career options.

==Productivity==

High school is the right period to form good life habits. See our [[personal productivity and life skills portal]] for more. Most relevant to the schooling experience is [[good study habits]].

==Your web presence==

These pages are relevant for everybody, but particularly for high school and college students who are just beginning to build a web presence:

* [[Maintaining your online presence]]
* [[Using Facebook effectively]]
* [[Join Quora]]

Making a positive impression on teachers

2014-05-20T01:00:42Z

Vipul:

As a student, it's important for you to make a positive impression on teachers. This doesn't necessarily mean being ingratiating or flattering to teachers. In most cases, the teachers whose opinion of you matters most are unlikely to be the ones who are easily influenced by flattery. This page describes some aspects of what teachers like and how to make a positive impression on them.

==Requesting accommodations==

* Avoid an attitude of entitlement to accommodations.
* Make accommodation requests as early as possible and explain your reasons clearly.
* Do not lie or exaggerate your situation. Admit if you messed things up rather than invent a story of a disaster or emergency to justify your accommodation request.
* If you have genuine needs (for instance, a physical disability, or a learning disability, such as dyslexia) and need an accommodation for it, you should request your teacher. But you should let the instructor know in advance and explain your situation clearly.
* If instructors refuse an accommodation that you are not entitled to as a matter of policy at your institution, accept that graciously and avoid further protracted arguments with the instructor.

==Requesting personalized assistance==

These suggestions apply in the college setting. Many of them don't quite apply for the school setting,.

* Do not expect the instructor to teach you material that was covered in class from scratch. Use the text, instructor-provided notes, or notes taken by you (or a fellow student) in class first, and simply ask the instructor for assistance with the difficult parts of the notes.
* Be well-prepared with your questions for office hours. Put a clear prior effort and indicate exactly why and where you are confused.
* If the instructor encourages it, use email or time immediately after the lecture to clarify your doubts with the instructor.
* Use the instructor's [[office hours]] (walk-in or by appointment, or online office hours if the instructor offers them). If you anticipate a lengthy interaction, check with the instructor in advance.
* As always, avoid an attitude of entitlement to unlimited assistance. Be adequately deferential, while at the same time not being shy to approach the instructor or ask for help.

==Submissions and deadlines==

* Read the instructor's policy on deadlines and submissions. Some instructors don't allow you to submit late at all. If that is the case, don't ask the instructor at all. If you're not able to complete an assignment, inform the instructor that you weren't able to complete it, and are submitting a partial one, but you'll review the remaining questions without expectation of having them graded. If you have extenuating circumstances, let the instructor know. If the instructor finds your circumstances sufficient to warrant an extension, he/she will offer it.
* In cases where the instructor allows late submissions, make sure you inform the instructor in advance of the lateness of submission. Use the most considerate method possible for submitting the assignment. For instance, if a print copy is expected, try to arrange for a print copy to be delivered to the instructor. If you're unable to send the homework in the required format, send it in a provisional format (such as a scanned copy) and offer to have it submitted in the appropriate format later if needed.

==Using class time==

* In general, class participation is good, and it's good to have your doubts clarified when you're in class. In some cases, a doubt you have may be connected with an error made by the instructor, so voicing it can help the instructor and your fellow students. Doubts expressed by you may be shared by fellow students, so clearing them up in class time may serve them as well.
* Instructors rarely mind students asking questions when they have doubts, even if the questions are stupid. What irritates instructors is ''monopolization'' of class time (to the exclusion of other students and cutting on the instructor's classroom plans) and ''persistence'' with a question after the instructor has resolved it, particularly if that persistence is not well-grounded. The points below address that.
* Determine the convention for class interaction based on the instructor's explicit and implicit policy. Some instructors allow you to simply speak up at any time with questions. Other instructors expect you to raise your hand if you have a question. Similarly, some instructors are okay with anybody shouting out an answer directed at the students, while others expect people to raise their hands if they want to volunteer an answer, and yet other who [[learning:cold calling|cold call]] their students may not want people to volunteer answers at all. If the instructor doesn't make the convention explicit, and it's hard to infer, ask the instructor and/or your fellow students. To the extent possible, stick with the class convention (there may be exceptions).
* Try to determine which of your questions are appropriate for class time and which ones are best asked of the instructor privately. It's okay to err a bit about that occasionally. You can check with the instructor later if a question you asked in class would have been better suited for private interaction, or if a question you asked privately might have been more appropriate to raise in class.
* Avoid protracted one-on-one debate and discussion with the instructor during class on a question that only interests you. If something the instructor says does not make complete sense to you, or you disagree, you could consider deferring further discussion to after class. You could announce this deferral decision or just let the instructor move on.

==Tidiness and punctuality==

* Be on time for classes and attend classes to the extent possible. If you have a conflict that causes you to be late to class (such as a class just before it) let the instructor know.
* If you think the lectures aren't worth attending, this might mean forgoing the opportunity to make a positive impression on the instructor. Nonetheless, some instructors may not mind you missing class ''as long as your performance on tests and homeworks is exemplary''. Missing class makes requesting assistance later to overcome poor performance more painful.
* Submit your homeworks on time, in the indicated format, and in a neat handwriting (or typed and printed if you prefer that). Even if the homeworks are not be graded by your instructor, the impressions of your grader might feed into the impressions of your instructor, and the instructor might occasionally review the homework submissions.

Making a positive impression on teachers

2014-05-20T00:57:02Z

Vipul: u

Making a positive impression on teachers

2014-05-20T00:48:20Z

Vipul: Created page with "As a student, it's important for you to make a positive impression on teachers. This doesn't necessarily mean being ingratiating or flattering to teachers. In most cases, the..."

Office hours

2014-05-20T00:25:40Z

Vipul:

'''Office hours''' refers to a practice, common in colleges and universities, but also found in some other settings, where instructors and teaching assistants allow students to visit them (typically, at their office) to ask questions or discuss topics related to their course.

Office hours could be:

* Walk-in office hours within predetermined time ranges (for instance, an instructor might have office hours 4-5 PM Wednesday and 10-11 AM Friday).
* Office hours by appointment: The student and the instructor or teaching assistant make an appointment (online, by email, or in person before or after class).

In addition to or in place of in-person office hours, some instructors might offer office hours by Skype or text chatting.

==General observations on office hours attendance==

===Fewer students attend office hours than instructors expect or (in many cases) hope for===

Office hours are rarely crowded -- instructors rarely get more than one student per hour for walk-in office hours, and some get less.

The following exceptions have been noted:

* For some courses, particularly writing-related courses, students are expected to use office hours to seek help from the instructor or grader on improving their skills.
* Some teaching-focused liberal arts colleges place considerable emphasis on teacher-student interaction, so students may use office hours more.
* Office hour usage is often high immediately preceding a test. However, in cases where the instructor holds a review session for the test or provides clear review materials, office hour usage before a test need not be high.

For more evidence that students don't attend office hours much, and speculations on the reasons, see these Quora questions:

* [http://www.quora.com/How-frequently-do-college-students-use-their-professors-office-hours-to-request-additional-assistance How frequently do college students use their professors' office hours to request additional assistance?]
* [http://www.quora.com/Office-Hours-1/Why-dont-more-college-students-go-to-office-hours Why don't more college students go to office hours?]

===The reasons students attend office hours are varied, and different motivations can generate different responses from the instructor===

On the Quora question [http://www.quora.com/Office-Hours-1/For-professors-what-does-it-feel-like-to-have-a-student-attend-office-hours For professors, what does it feel like to have a student attend office hours?], Rosina Lippi's [http://www.quora.com/Office-Hours-1/For-professors-what-does-it-feel-like-to-have-a-student-attend-office-hours/answer/Rosina-Lippi answer] offers the following categories (quoted but with bullet points added for clarity):

<blockquote>
* An earnest student with real questions: that's a good feeling, that the person was comfortable enough to come and talk.
* A student wanting a therapy session and life advice: irritating and/or sad. There's not much you can do for a student except point them to a better resource.
* A student looking for a way to get around a bad grade: sometimes amusing, because the narratives can be so convoluted; usually irritating. I had a long, drawn out series of meetings with a football player with professional aspirations who simply refused to come to class, but thought I should pass him anyway. He was hoping to win that war by attrition. He had his mother call me. The coach called. He worked really hard to get me to pass him, as long as the work didn't involve him actually coming to class or doing the work. Every angle he tried just made me angrier, and he did not get what he wanted.
* A belligerent student (and this is not uncommon): tiring. Professors are often the place students focus their anger and unhappiness. It can also be a little frightening, because it's not unknown for students to be physically violent.
* A student with a valid complaint: Relief. At least I'll have a chance to fix the problem. Some students will immediately go over your head (the chair, the dean, the school newspaper) without coming in to say what the problem is.
* Once in a while there's a student who is very professional and hard working and also friendly, who will stop by for five minutes just to say hello, not looking for anything. And that's very nice.
</blockquote>

==Dos and don'ts of office hours==

* Use the instructor's recommended walk-in office hours or make an appointment.
* For simple interactions or questions, including bureaucratic questions, consider using email or cornering the instructor before or after class (if the instructor seems open to these interactions). This minimizes overhead for you as well as the instructor. If the instructor asks you to meet him or her during office hours, of course, you should do so. Email trails are also preferable for initiating discussion of bureaucratic matters, since they are easier to forward to other relevant parties and also provide both sides with evidence in case of future controversy. Another advantage of using email is that it's easier for both sides to stay civil in language (for instance, if you ask for a homework extension and the instructor denies it, it's easier to avoid getting too worked up if you're using an asynchronous medium such as email).
* If you anticipate a lengthy interaction (for instance, you'd like the instructor to answer lots of questions) it may be better to check with the instructor in advance, even if you're using walk-in office hours. The instructor may have some time preferences (for instance, the instructor may ask you to come a little earlier or later so as not to conflict with other students who may use the walk-in office hours). The instructor may also have suggestions for ways you could resolve your doubts yourself.

For more, see [[making a positive impression on teachers]].

==Arguments for going to office hours==

* If you have doubts, questions, or curiosities concerning the material, office hours could be a quick way of resolving them for free.
* Office hours can be a good way of connecting better with your instructors and making positive impressions on them (through, at minimum, the [[wikipedia:mere-exposure effect|mere-exposure effect]], but you can impress them even more by coming prepared and asking perceptive questions). This could give you better future opportunities to interact with your instructors after the course is over, as well as get assistance from them.
* Instructors who see students in office hours may also have more substantive material to write if they're asked to write letters of recommendation for the student.

==Arguments against going to office hours==

* Many instructors hold office hours only grudgingly. While they're willing to help students if needed, they're not enthusiastic about it. If you don't really need help, they may see your coming in as an undue imposition.
* Many instructors don't have very interesting things to say beyond what they're already telling you in the classroom. Their research or other things that they do may not be of interest to you.

College portal

2014-05-20T00:05:47Z

Vipul: /* General information */

This is a portal page linking to various pages related to college life. There is some overlap between the pages here and those in the [[high school portal]].

==College selection==

* [[College: deciding whether to go]]
* [[College selection: factors to consider]]
* [[College selection: getting reliable information about colleges]]
* [[College selection: deciding based on your intended major]]
* [[Public university versus private university]]
* [[Pros and cons of attending a large college]]

==College applications and admissions==

We're still working on this, but of particular interest may be [[college admissions for homeschooled students]].

We also encourage people interested in learning more about college admissions to check out [[College Confidential]], one of the best sources of information on the subject. Other potential sources of information are [[Quora]], [[About.com]], and StudentAdvisor.

==Between high school and college==

See [[time between the end of high school and the beginning of college]] (partially done).

==College academics==

===General information===

* [[College lecture class formats]]
* [[College course dependencies]]
* [[Auditing courses]]
* [[Office hours]]

===Choice of major===

* [[Choosing a college major: factors to consider]]
* See [[:Category:College majors|the college majors category]] for links to pages about different college majors.
* [[Quora answers about college majors]] gives a useful collection of links to Quora answers about a wide range of college majors.

===Course structure and learning recommendations===

* [[Lower division undergraduate mathematics]]: See also [[lower division undergraduate mathematics course structure]] and [[lower division undergraduate mathematics learning recommendations]]
* [[Upper division undergraduate mathematics]]: See also [[upper division undergraduate mathematics course structure]] and [[upper division undergraduate mathematics learning recommendations]]
==Navigating college==

===Typical situations===

* [[Course selection]]
* '''FORTHCOMING''': Understanding and optimizing for the credit, placement, and enrollment structure
* '''FORTHCOMING''': Study abroad programs

===Unusual situations===

* [[Graduating college early]]
* [[Taking a gap year between high school and college]]

===Alternatives===

* [[Alternatives to college]]

==Career planning==

You've got a long time to think about careers, but it's good to get a head start in exploring different careers by reading up about them and observing them in action. See our pages:

* [[Preparing for a career during high school and college]]
* [[Career selection: factors to consider]]
* [[:Category:Career options|The career options category]] lists pages discussing various career options.

==Productivity==

High school is the right period to form good life habits. See our [[personal productivity and life skills portal]] for more. Most relevant to the schooling experience is [[good study habits]].

==Your web presence==

These pages are relevant for everybody, but particularly for high school and college students who are just beginning to build a web presence:

* [[Maintaining your online presence]]
* [[Using Facebook effectively]]
* [[Join Quora]]

Office hours

2014-05-20T00:05:16Z

Vipul: /* General observations on office hours attendance */

Office hours

2014-05-20T00:01:16Z

Vipul: /* General observations on office hours attendance */

Office hours

2014-05-19T23:55:57Z

Vipul:

Office hours

2014-05-19T23:53:23Z

Vipul:

Office hours

2014-05-19T23:49:19Z

Vipul: Created page with "'''Office hours''' refers to a practice, common in colleges and universities, but also found in some other settings, where instructors and teaching assistants allow students t..."

Effective altruism learning resources

2014-05-19T22:48:03Z

Vipul: /* Effective altruism in connection with career choice and life trajectory */

Since the mid-2000s, [[wikipedia:effective altruism|effective altruism]] has grown considerably as a movement. The goal is to choose one's altruistic actions with the goal of doing as much good as possible and as effectively as possible. There are a number of different groups promoting effective altruism, and their websites provide information that can help you get started on understanding the general principles.

==Trello list of starting points==

See [https://trello.com/b/rm3da7HZ/effective-altruist-introductory-resources Effective Altruist Introductory Resources] for a list of books, videos, and blogs intended to introduce people to effective altruist ideas. The list is not created or maintained by us.

==Organizations or group websites interested in effective altruism==

{| class="sortable" border="1"
! Organization/website !! What they do !! Starting point for learners !! Things to follow and read
|-
| [http://www.effective-altruism.com/ Effective Altruism blog] || People directly involved with effective altruism blog their thoughts on the subject. || Blog posts linked on their [http://www.effective-altruism.com/about-2/ about page]: [http://www.effective-altruism.com/what-effective-altruism/ What is Effective Altruism?] by William MacAskill, [http://www.effective-altruism.com/effective-altruism/ Effective Altruism] by Holden Karnofsky, and [http://www.effective-altruism.com/four-focus-areas-effective-altruism/ Four Focus Areas of Effective Altruism] by Luke Muehlhauser. || The blog might be worth following.
|-
| [http://www.givewell.org GiveWell] || Non-profit dedicated to doing the most good possible. Their primary consumer-facing product is recommendations for top charities to donate to, across all causes (rather than providing a top charity separately for each cause). In addition, they evaluate claims about a diverse range of activities (such as scientific research and political advocacy) that people have claimed might be highly effective ways of improving the world. || [http://www.givewell.org/giving101 Giving 101: the basics] is a useful primer that reviews many basic effective altruism ideas, such as differences between standards of living in different parts of the world, [[wikipedia:room for more funding|room for more funding]] and triage (the one-charity argument). || Follow their [http://blog.givewell.org blog] and skim through their detailed reviews of [http://givewell.org/top-charities top charities] as well as their [http://www.givewell.org/shallow shallow cause overviews].
|-
| [http://givingwhatwecan.org Giving What We Can] || Non-profit dedicated to advocating that people donate more money to charity, as well as providing suggestions about top charities to donate to. They rely on GiveWell's research plus some in-house research to make recommendations. || The [http://givingwhatwecan.org/where-to-give/methodology charity evaluation methodology] might be the most educational and informative place to start, but it may be fruitful to check out the [http://givingwhatwecan.org/why-give why give?] and [http://givingwhatwecan.org/where-to-give where to give] pages first to get some background and context. || Follow their [http://givingwhatwecan.org/blog blog]
|}

==Courses==

* [https://www.coursera.org/course/practicalethics Practical Ethics] by Peter Singer on Coursera
==Effective altruism in connection with career choice and life trajectory==

{{further|[[Choosing a career with effective altruism as a dominant consideration]]}}
{| class="sortable" border="1"
! Organization !! What they do !! Starting point for learners !! Things to follow and read
|-
| [http://80000hours.org/ 80,000 Hours] || Combine publicly available research as well as personalized career guidance to guide people on career choice, where the people they guide have effective altruism as a primary consideration in career choice. || [http://80000hours.org/research Research page] || Their [http://80000hours.org/blog blog] is worth following if this topic interests you.
|}

==Personal blogs of individuals interested in effective altruism==

* Brian Tomasik is an effective altruist writer who maintains a website [http://www.utilitarian-essays.com/ here].
* Ryan Carey is an effective altruism movement-builder who is interested in guiding young people interested in effective altruism. His website is [http://careyryan.com/ here]
* Peter Hurford blogs regularly about topics closely related to effective altruism and utilitarian ethics [http://www.everydayutilitarian.com/ here].
* Bastian Stern's website [http://utilitarianism.bastianstern.com/ compiles resources on utilitarianism and effective altruism].
* Paul Christiano maintains a [http://rationalaltruist.com/ blog on rational altruism].

==Other useful starting points==

* [http://lesswrong.com/lw/37f/efficient_charity/ Efficient Charity] by multifoliaterose
* [http://lesswrong.com/lw/3gj/efficient_charity_do_unto_others/ Efficient Charity: Do Unto Others...] by Yvain

==Combined feeds==

* [http://www.rssmix.com/u/3890223/rss.xml EA Medley] maintained by Peter Hurford is a RSS feed combining many effective altruism-related blogs.

==Online communities for general discussion==

* [https://www.facebook.com/groups/effective.altruists/ Effective Altruists Facebook group] (read [http://effectivealtruism.info/?page_id=10 here] for the group guidelines)
* The comments sections of some of the blogs linked above.
* [http://www.lesswrong.com LessWrong] often has blog posts on effective altruism and many people in the LessWrong community are interested in effective altruism.
* You might also be interested in forums related to utilitarianism, including [http://www.felicifia.com Felicifia] and many Facebook groups.

==Key terminology==

Links are to Wikipedia pages or other canonical sources:

* [[Wikipedia:Earning to give|Earning to give]]
* [[Wikipedia:Room for more funding|Room for more funding]]

==See also==

* [[Development economics reading recommendations]]
* [[Economics reading recommendations]]
* [[Economics learning recommendations]]

Social value of academia

2014-05-19T22:46:10Z

Vipul: /* Research */

{{social value|academia}}

This page elaborates on the social value consideration for [[academia as a career option]]. Academics do ''research'' and ''teaching,'' each of which can contribute social value, and we discuss these separately.

==Research==

Your measure of the social value of academia depends to some extent on how you define social value, but the general conclusion is that academia passes the social value test only in one of these two cases:

* You are really good at it, so that you can change the paradigm of thinking.
* The discipline you are picking has high social value, so that every minor contribution there counts for a lot.

See also:

* [[Social value of basic science research]]
* [[Social value of biomedical research]]
* [[Social value of mathematics research]]
* [[Social value of computer science research]]
* [[Social value of physics research]]

Also, this page deals with social value in the sense of impact on the real, existing, social world. Some people view academic work (research and teaching) as a merit good -- expanding the frontier of knowledge is valuable in and of itself, independent of the practical benefits.

The following need to be kept in mind:

* A number of disciplines, including many branches of mathematics, have advanced far, far ahead of anything that might be of practical relevance, and further progress in these is unlikely to be of use. However, a counterpoint is that a number of mathematical techniques that were considered to not have much application have been quite important: differential geometry was useful in relativity, matrices and linear algebra were important in physics, statistics, and eventually all the natural and social sciences, and number theory is critically important to much of modern cryptography. There is considerable debate on whether current work in mathematics will be similarly useful later, but the evidence currently does not seem to be strongly in favor.
* Even for disciplines that are in principle of practical relevance, the theoretical questions considered in academia are often orthogonal to the manner in which those disciplines would be relevant. For instance, many questions asked in philosophy are relevant to practical ethics, but the mode of discourse of philosophy is unlikely to settle these questions. However, this may be more a question of it taking time for the insights to percolate into the real world. Many deep theoretical insights from statistics and economics have percolated into the general intelligentsia from as recently as 30 years ago.
* There are huge differences between academic disciplines in terms of both the expected impact and the variance in impact. For instance, for disciplines such as biomedical research, it can be argued that every bit of research helps at the margin, by investigating and eliminating particular research pathways. In a discipline such as theoretical physics, coming across a fundamental insight comparable to quantum mechanics would revolutionize the subject, but most work is likely to have zero impact.

The following blog posts from the 80000 Hours blog are relevant (we intend to eventually incorporate and discuss these article when we expand this page):

* [http://80000hours.org/blog/103-how-to-do-important-research How to do important research] by Benjamin Todd, October 4, 2012.
* [http://80000hours.org/blog/125-how-to-choose-a-research-topic-an-interview-with-anders-sandberg How to choose a research topic: An interview with Anders Sandberg] by Jess Whittlestone, December 1, 2012.
* [http://80000hours.org/blog/140-how-to-make-a-difference-in-research-an-interview-with-nick-bostrom How to make a difference in research: An interview with Nick Bostrom] by Jess Whittlestone, January 16, 2013

==Teaching==

It's difficult to evaluate the social value of teaching within academia, but we would guess that ''the social value that professors contribute through their teaching is generally lower than the value of what they would be doing in another profession.'' Some relevant considerations are below

===The degree to which most college classes increase human capital appears to be small===

Economist Bryan Caplan makes the following points in [http://econlog.econlib.org/archives/2011/11/the_magic_of_ed.html The Magic of Education]:

* While literacy and numeracy are important for most jobs, subjects such as history, art, music, foreign languages, poetry, and mathematical proofs are not used in the vast majority of occupations.
* There's a strong base of evidence that coursework doesn't teach most students "how to think."
* The degree to which college classes improve students' work habits is smaller than the degree to which job experience improves people's work habits.

College classes do teach important employable skills in some cases, for example:

* Writing is an important skill for many jobs, and some college courses may improve writing ability.
* Students who are pursuing technical professions such as science and engineering may learn relevant background knowledge from their science courses.
* For students who are pursuing academia, undergraduate courses may be good preparation for graduate school.

But the degree to which college courses improve writing ability is unclear, and most people don't become scientists, engineers or academics.

===The consumptive value of college courses is questionable===

Many students attend college because employers require it rather than because they enjoy learning the material in the courses. So the amount that they're willing to pay for the courses doesn't reflect the degree to which they enjoy the courses. Of course, it's arguable that when one takes into account ''all'' of the benefits of college courses, the cost is worth it: the point here is just that for the average student, the consumptive value alone doesn't seem high enough to justify the cost.

===The impact that most individual instructors can have on the quality of courses is questionable===

* Some college courses have rigid syllabi, such that there aren't opportunities to improve students' educational experiences.
* If you're not a substantially better teacher than the person who would otherwise take your job, you won't improve students' educational experience. Few professors have the ability and energy to be much better than their potential replacements. Unless you have reason to believe that you'd be one of them, you shouldn't expect to be able to improve students' educational experiences by a lot.

===The impact of improving the quality of a course on students' experiences is questionable===

Many undergraduate students don't enjoy learning from their classes, independently of the quality of the instructor. Unless you're one of the few professors who's able to get a job at an elite university, you may find that your students are unresponsive to good teaching.

There's an important counterpoint, which is that many students will enjoy being in an entertaining teacher's class even if they don't enjoy learning the material. So if you enjoy and are good at being an entertaining lecturer, you may be able to improve students' experiences (though not necessarily their learning).

===The value of improving grades as a signal may not be high===

In [http://econlog.econlib.org/archives/2011/11/the_magic_of_ed.html The Magic of Education], economist Bryan Caplan argues that the primary value of a student's educational record to employers is as a signal of qualities that employers find desirable, such as intelligence, work ethic and conformity.

Undergraduate grades feed into medical school admissions, law school admissions, business school admissions, and graduate school admissions, and employers sometimes look at undergraduate grades directly.

If you're an instructor, you may be able to add social value by designing your grading system and exams in a way that better measures intelligence, work ethic and conformity.

This may be the primary value that most college instructors can add. We're agnostic as to the magnitude of this value, but would guess that it's not high enough so that the social value that professors contribute through their teaching is higher than the social value that they would be contributing in a different profession.

Entrepreneurship as a career option

2014-05-19T22:35:23Z

Vipul: /* Social value */

{{career option|entrepreneurship}}

Entrepreneurs start businesses.

* In order to be a successful entrepreneur, you need to have unusual personal characteristics.
* The variance in earnings of entrepreneurs is enormous. Some become multibillionaires, and others go bankrupt. Most entrepreneurs could make more money doing something else, but there's a possibility of making far more money than you would be able to in other careers.
* As with earnings, the variance in social value that entrepreneurs produce is enormous. As a group, entrepreneurs contribute a great deal of social value, but most produce very little social value.

If you're interested in the possibility of pursuing entrepreneurship, see our [[entrepreneurship learning recommendations]].

== Relevant personal characteristics ==

Some characteristics relevant to being a successful entrepreneur are:

* '''Determination.''' Famous venture capitalist Paul Graham [http://paulgraham.com/notnot.html says] that this is probably the biggest predictor of success.
* Strong ability to work independently without much guidance or supervision.
* Tolerance of high uncertainty. It's often very unclear whether or not an enterprise will succeed, and whether one is working in the right direction at any given time.
* An idea that you're passionate about. Your chances of success are much better if you care a great deal about the product that you're producing.
* Willingness to accept the risk of not accomplishing anything significant. Most apparently promising ideas don't lead anywhere.

Carl Shulman at the 80,000 Hours blog [http://80000hours.org/blog/23-entrepreneurship-a-game-of-poker-not-roulette lists]:

* Ability to recognize when your product is not promising. Many startup founders are unwilling to acknowledge evidence that their product is not a good one.
* Experience. The chances of success per attempt increase with number of past attempts, even when the past attempts are unsuccessful.
* Intelligence. Your chances of success are much higher if you're at the 99th percentile than they are if you're at the 90th percentile, and they're much higher if you're at the 99.9th percentile than they are if you're at the 99th percentile.

Paul Graham lists factors that he thinks should not play a major role in whether or not somebody should become an entrepreneur in [http://paulgraham.com/notnot.html Why to not not found a startup]. Some of the factors that he lists are factors that we've listed as relevant. Even if some of the characteristics that we list above are not strictly necessary, they still increase one's expected success, and whether one has them should play some role in one's decision.

== Earnings ==

Entrepreneurs' earnings vary by a factor of 10,000+.

Startup genome [http://blog.startupcompass.co/ gives] survey results on salaries, finding that 73% of startup founders make less than $50k/year.

A very small fraction of startups receive venture capital funding. In [http://80000hours.org/blog/12-salary-or-startup-how-do-gooders-can-gain-more-from-risky-careers Salary or startup? How do-gooders can gain more from risky careers] Carl Shulman at the 80,000 Hours gives figures for the amount of money that entrepreneurs who received venture capital ended up making over the span of ~ 4 years. Of the entrepreneurs studied, 67.4% made less than $1 million, 19.7% made between $1 million and $10 million, and about 10% made more than $10 million.

At the high end, people like Sergey Brin and Larry Page (founders of Google) Mark Zuckerberg (founder of Facebook) and Jeff Bezos (founder of Amazon) earned ~$20 billion over the course of their careers. However, they constitute a negligible fraction of entrepreneurs.

== Social value ==

{{further|[[social value of entrepreneurship]]}}

* In general, [[Social value of work: income as a proxy|earnings are correlated with social value produced]]. The fact that entrepreneurs' earnings are highly variable reflects high variance in social value produced.

* The average earnings of entrepreneurs of a given quality are higher than the earnings of the average entrepreneur of the same quality. So your [http://en.wikipedia.org/wiki/Expected_value expected earnings] (in the technical sense) if you do entrepreneurship are higher than your ''probable earnings'' if you do entrepreneurship. Because most people care more about their probable earnings than their expected earnings (on account of [http://en.wikipedia.org/wiki/Risk_aversion risk aversion]), this is a consideration in favor of entrepreneurship having higher expected social value than other careers do. If you're equally good at entrepreneurship and another career, you can probably contribute more expected social value (as measured by earnings) as an entrepreneur than if you pursue the other career.

* If your expected earnings are higher as an entrepreneur than they are in other careers, then the expected amount that you can donate to charity is higher than it is if you pursue other careers. If you donate a large fraction of your earnings to highly effective charities such as those recommended by [http://www.givewell.org/ GiveWell], the expected social value that you're able to contribute as a result of pursuing entrepreneurship is enhanced substantially.

* There's high variance in social value produced beyond what's captured by income. For example, Google doesn't earn income from those who use its products, and so produces social value out of proportion with its earnings. In the other direction, game developer Zynga arguably [http://www.thewire.com/business/2012/04/your-stupid-game-addiction-earned-zynga-321-million-last-quarter/51628/ does harm] on account of fostering addictive behavior amongst clients. If you're an entrepreneur, you can make a product that produces [[wikipedia:Externality#Positive|positive externalities]] while minimizing [[Wikipedia:Externality#Negative|negative externalities]], thereby contributing social value out of proportion with your earnings.

See also

* [http://80000hours.org/blog/12-salary-or-startup-how-do-gooders-can-gain-more-from-risky-careers Salary or startup? How do-gooders can gain more from risky careers]
* [http://80000hours.org/blog/256-should-more-altruists-consider-entrepreneurship Should more altruists consider entrepreneurship?]

at the 80,000 Hours blog.

Social value of finance

2014-05-19T22:32:32Z

Vipul: /* Causing financial crises */

{{social value|finance}}

If you're considering working in finance, you may wonder about the social value that working in finance produces. Actors in finance produce both social value and social disvalue, and it seems difficult to make a general statement about whether the typical worker at an investment bank (for example) does more good or harm. The situation probably varies from sector to sector of finance. We give some relevant considerations below.

==Earning to give: the primary consideration in favor of finance==

Because the earnings are high in finance, finance has been highlighted as a promising career track for people who want to engage in [[wikipedia:earning to give|earning to give]], i.e., people who want to choose a career with the explicit goal of being able to donate larger sums of money to charity. In fact, earning to give seems to be the dominant reason for finance even being ''considered'' as an option for people interested in doing the most good.

The [http://80000hours.org/blog?category=12 80,000 Hours blog category "finance"] mostly discusses finance in the context of earning to give, but some of the posts also discuss other potential pluses and minuses of finance to see the extent to which they alter the calculation of social value. Some posts worth highlighting:

* [http://80000hours.org/blog/239-show-me-the-harm Show me the harm] by Benjamin Todd, 80,000 Hours, July 31, 2013, argues that the harm one might do in finance is small relative to the good that one can do by donating 50+% of one's income to highly effective charities.
* [http://80000hours.org/blog/304-case-study-can-i-earn-more-in-software-or-finance Case study: can I earn more in software or finance?] by Benjamin Todd, 80,000 Hours, January 30, 2014, discusses the case of Jessica, a Google engineer who consulted 80,000 Hours for advice on whether to switch to finance to be able to engage in earning to give more effectively.

==How finance helps: the basic model==

[[Social value of work: income as a proxy |In general]], there's a correlation between income and social value contributed. The fact that the earnings of people who work in finance are high raises the possibility that workers in finance contribute high social value.

People and organizations sometimes have a temporary need for money to accomplish their goals, and people and organizations are sometimes willing to lend money for a fee. Actors in finance who enable these transactions benefit both the borrower and the lender, and are paid accordingly. Actors in finance who can enable a larger quantity of transactions, or enable transactions that generate more value, get paid proportionally more. Similarly, actors in finance who lend money themselves benefit the borrowers and are paid accordingly.

The proportion of activity in finance that fits this basic model is unclear. Many of the transactions in finance are many steps removed from the basic activity of enabling borrowers and lenders to connect. Some of these transactions indirectly enable borrowers and lenders to connect, and others don't. It can be very difficult to tell which are which from the outside.

Relevant discussions:

* [http://80000hours.org/blog/272-case-study-working-in-the-financial-sector-to-promote-a-flourishing-long-term-future Case study: Working in the financial sector to promote a flourishing long-term future] by Benjamin Todd, 80,000 Hours blog, November 13, 2013.

== Unproductively increasing the efficiency of the market ==

If a company is looking for an investor and nobody is willing to invest, this is bad for the company. If the company is deserving of an investment, you spot this, and nobody is willing to invest, then you can benefit the company and make a profit by investing.

But suppose there are actors who are willing to invest in the company, and you invest in the company a tiny bit faster than the other actors. The company doesn't benefit much from this, because it would have gotten an investment anyway. The other people who would have invested are harmed by this, because they can't make a profit. So the social value that you contribute is much smaller than it would have been if nobody had been willing to invest within the same rough timeframe.

Some activity in finance takes this form. High frequency trading is a candidate for a sector of finance that makes money through buying and selling stocks a little bit faster than others, without contributing much social value. The transactions that high frequency trading firms make occur on a time scale of a fraction of a second, and it's unclear that enabling people to buy or sell a stock a fraction of a second faster helps them to an appreciable degree, even after taking into account the number of people involved.

==Pushing off tail risk onto the government==

Some firms in finance are [[wikipedia:too big to fail|"too big to fail"]] in the sense that if they were to go bankrupt, the whole economy would suffer enormously, because of their interconnectedness. When they're in danger of bankruptcy, the government will often lend or give them money to keep them afloat. Because the firms are aware that they'll likely be supported by the government in the event that they make bad investments, they'll sometimes make very risky investments, that have high upside to them if they pan out well, with the expectation that if they pan out poorly, the government will cover their losses. Such actors effectively make their money at the expense of the taxpayers, thereby contributing negative social value.

Not all actors in finance behave in this way.

== Causing financial crises ==

As above, sometimes "too big to fail" firms will take risks that they're not able to handle, with the expectation that the government will cover their losses. If they're in danger of bankruptcy and the government ''doesn't'' cover their losses, this can precipitate a financial crisis. In particular, the collapse of [[wikipedia:Lehman Brothers|Lehman Brothers]] is thought to have played a major role in the 2008 financial crisis. In this way, actors in finance may be able to cause damage far out of proportion with their earnings.

As above, not all actors in finance behave in this way.

[http://business.time.com/2012/08/08/high-frequency-trading-wall-streets-doomsday-machine/ Some people] have raised the possibility that high-frequency trading could cause a financial crisis on account of increasing the stock market's volatility, but others have disputed it, or even claimed that high-frequency trading reduces the stock market's volatility.

Social value of finance

2014-05-19T22:31:45Z

Vipul: /* Pushing off tail risk onto the government */

{{social value|finance}}

If you're considering working in finance, you may wonder about the social value that working in finance produces. Actors in finance produce both social value and social disvalue, and it seems difficult to make a general statement about whether the typical worker at an investment bank (for example) does more good or harm. The situation probably varies from sector to sector of finance. We give some relevant considerations below.

==Earning to give: the primary consideration in favor of finance==

Because the earnings are high in finance, finance has been highlighted as a promising career track for people who want to engage in [[wikipedia:earning to give|earning to give]], i.e., people who want to choose a career with the explicit goal of being able to donate larger sums of money to charity. In fact, earning to give seems to be the dominant reason for finance even being ''considered'' as an option for people interested in doing the most good.

The [http://80000hours.org/blog?category=12 80,000 Hours blog category "finance"] mostly discusses finance in the context of earning to give, but some of the posts also discuss other potential pluses and minuses of finance to see the extent to which they alter the calculation of social value. Some posts worth highlighting:

* [http://80000hours.org/blog/239-show-me-the-harm Show me the harm] by Benjamin Todd, 80,000 Hours, July 31, 2013, argues that the harm one might do in finance is small relative to the good that one can do by donating 50+% of one's income to highly effective charities.
* [http://80000hours.org/blog/304-case-study-can-i-earn-more-in-software-or-finance Case study: can I earn more in software or finance?] by Benjamin Todd, 80,000 Hours, January 30, 2014, discusses the case of Jessica, a Google engineer who consulted 80,000 Hours for advice on whether to switch to finance to be able to engage in earning to give more effectively.

==How finance helps: the basic model==

[[Social value of work: income as a proxy |In general]], there's a correlation between income and social value contributed. The fact that the earnings of people who work in finance are high raises the possibility that workers in finance contribute high social value.

People and organizations sometimes have a temporary need for money to accomplish their goals, and people and organizations are sometimes willing to lend money for a fee. Actors in finance who enable these transactions benefit both the borrower and the lender, and are paid accordingly. Actors in finance who can enable a larger quantity of transactions, or enable transactions that generate more value, get paid proportionally more. Similarly, actors in finance who lend money themselves benefit the borrowers and are paid accordingly.

The proportion of activity in finance that fits this basic model is unclear. Many of the transactions in finance are many steps removed from the basic activity of enabling borrowers and lenders to connect. Some of these transactions indirectly enable borrowers and lenders to connect, and others don't. It can be very difficult to tell which are which from the outside.

Relevant discussions:

* [http://80000hours.org/blog/272-case-study-working-in-the-financial-sector-to-promote-a-flourishing-long-term-future Case study: Working in the financial sector to promote a flourishing long-term future] by Benjamin Todd, 80,000 Hours blog, November 13, 2013.

== Unproductively increasing the efficiency of the market ==

If a company is looking for an investor and nobody is willing to invest, this is bad for the company. If the company is deserving of an investment, you spot this, and nobody is willing to invest, then you can benefit the company and make a profit by investing.

But suppose there are actors who are willing to invest in the company, and you invest in the company a tiny bit faster than the other actors. The company doesn't benefit much from this, because it would have gotten an investment anyway. The other people who would have invested are harmed by this, because they can't make a profit. So the social value that you contribute is much smaller than it would have been if nobody had been willing to invest within the same rough timeframe.

Some activity in finance takes this form. High frequency trading is a candidate for a sector of finance that makes money through buying and selling stocks a little bit faster than others, without contributing much social value. The transactions that high frequency trading firms make occur on a time scale of a fraction of a second, and it's unclear that enabling people to buy or sell a stock a fraction of a second faster helps them to an appreciable degree, even after taking into account the number of people involved.

==Pushing off tail risk onto the government==

Some firms in finance are [[wikipedia:too big to fail|"too big to fail"]] in the sense that if they were to go bankrupt, the whole economy would suffer enormously, because of their interconnectedness. When they're in danger of bankruptcy, the government will often lend or give them money to keep them afloat. Because the firms are aware that they'll likely be supported by the government in the event that they make bad investments, they'll sometimes make very risky investments, that have high upside to them if they pan out well, with the expectation that if they pan out poorly, the government will cover their losses. Such actors effectively make their money at the expense of the taxpayers, thereby contributing negative social value.

Not all actors in finance behave in this way.

== Causing financial crises ==

As above, sometimes "too big to fail" firms will take risks that they're not able to handle, with the expectation that the government will cover their losses. If they're in danger of bankruptcy and the government ''doesn't'' cover their losses, this can precipitate a financial crisis. In particular, the collapse of [http://en.wikipedia.org/wiki/Lehman_Brothers Lehman Brothers] is thought to have played a major role in the 2008 financial crisis. In this way, actors in finance may be able to cause damage far out of proportion with their earnings.

As above, not all actors in finance behave in this way.

[http://business.time.com/2012/08/08/high-frequency-trading-wall-streets-doomsday-machine/ Some people] have raised the possibility that high-frequency trading could cause a financial crisis on account of increasing the stock market's volatility, but others have disputed it, or even claimed that high-frequency trading reduces the stock market's volatility.