First, the standard answer: Bryan Caplan’s The Case Against Education. Short version: education is about signalling to future employers how smart/diligent/willing-to-jump-through-hoops/etc you are. Skill acquisition is mostly irrelevant. This is basically true for most people most of the time.
That said… I personally have gotten a lot of value out of things I learned in courses. This is not something that happens by default; the vast majority of my classmates did not get nearly as much value out of courses as I did. I’ll list a few things I did differently which may help.
Avoid nontechnical classes: this one is kind of a “well duh” thing, but there are some subtleties. “Technical” should be interpreted in a broad sense—things like e.g. law or languages aren’t technical in the sense of STEM, but they’re technical in the sense that the things they teach are intended to be directly useful. By contrast, subjects which are primarily about aesthetics or history or critical theory are not really intended to be directly useful.
Decreasing marginal returns: the first course in any particular field/subfield is far more valuable than the second course, the second course is more valuable than the third, etc. This suggests going for breadth over depth. In particular, I recommend taking one or two courses in many different fields so that you can talk to specialists in those fields without being completely lost. You don’t need to become an expert yourself; much of the value is in being able to quickly and easily work with specialists in many different fields. You can translate jargon and act as a human interface, and you can easily jump into many different areas.
General-purpose tools: focus on fields which provide tools applicable to many different domains. Most of applied math qualifies, as well as computer science, economics, and law. Ideally, you take one or two courses in some general-purpose subject, then run into applications of that subject while sampling other fields. By seeing it come up in different contexts, you’re more likely to remember and use it.
Summary of this point and previous: go for depth in general purpose tools, and practice those tools while gaining breadth in other areas.
Use available resources: I’ve covered about as much material in open courseware as I have in in-person classes. I’ve watched online lectures, I’ve read textbooks, and I’ve audited courses. (I’ve even audited classes at universities where I’m not registered—professors are usually happy with people just showing up.) In college, I’d often watch a semester’s worth of online lectures on a subject before taking a class on the subject; material is a lot easier to follow when you already have a general idea of where things are headed and how it all slots together..
Have a stock of problems: as you learn new tools, it’s useful to have a handful of problems to try them out on. Hard open algorithmic problems like integer factorization or P vs NP or graph isomorphism are great for testing out all sorts of applied math/CS tricks. “How could I use this to start a company and make a gazillion dollars?” is a problem which applies to practically anything. The problems should be things you’re interested in and enjoy thinking about, so that you’ll find it worthwhile to try out new tools on them even though most of the tools don’t actually yield breakthroughs on most of the problems.
This point and the previous one help a lot with actually remembering things and being able to apply them in-the-wild.
Optimize: at my college (Harvey Mudd), it was very easy to tell who had actually tried to optimize their course choices—it was the people who used the “build your own major” option. We only had half a dozen majors, and the course requirements always included things which weren’t really what any particular person was interested in. If you wanted to cram in more courses you were actually interested in, while avoiding irrelevant courses, a build-your-own major was the way to go.
More generally, you’ll get more out of classes if you read through the whole course catalog, mark the classes which sound interesting, and then optimize your schedule to focus on those classes. Sounds obvious, yet most people don’t do it.
Be comprehensive: you’re not going to know everything, but you can learn enough that nothing is very far from the things you do know. You can learn enough that you at least have some idea of which things you don’t know. You can learn enough that, even if you don’t know something, you’ve probably heard of it, and you have some idea of where to learn about it if you need to. The key is to aim for comprehensive knowledge of the world—you don’t need to know every little detail, but at least get the broad strokes of the big things. Anytime you don’t have a clue about something, pay attention to it, and look for the most general subject which would give you some background about that thing.
Math, physics and economics are particularly useful for comprehensive foundations—they give you the tools to solve practically anything in principle, though you’ll often need more specialized tools in practice.
Caplan puts the signalling share of the college income premium at 50%-80%, leaving (say) 20% for the human capital share. So your sentence calling HC “mostly irrelevant” is technically true, but I wouldn’t use the word ‘irrelevant’ for a feature explaining ~ a fifth of the variance.
Thanks. That’s a really nice list.I have not seen a lot of these ideas previously.Especially general purpose tool-idea and stock of problems-idea is very good.These ideas are really nice to ensure in-built spaced repetition.
But can you give me some ideas about the second question I asked.I cant do this because I am still undergrad.So pick a topic that you learned about say 4-5 years ago(or any time-frame for that matter),make sure that you haven’t used that particular knowledge for the past 4-5 years,try to get back to the same knowledge-level that you had acquired when you first learned the topic(or some % of it) and measure the amount of effort/time that you took.Then calculate the ratio of (this time or effort)/(time or effort when you first read that particular topic).
I graduated 7 years ago. During that time, I’ve actually used most of the subjects I studied in college—partly at work (as a data scientist), partly in my own research, and partly just when they happen to come up in conversation or day-to-day life. On the occasions when I’ve needed to return to a topic I haven’t used in a while, it’s typically been very fast.
But the question “how long does it take to get back up to speed on something I learned a while ago?” kind of misses the point. Most of the value doesn’t come from being able to quickly get back up to speed on fluid mechanics or materials science or inorganic chemistry. Rather, the value comes knowing which pieces I actually need to get back up to speed on. What matters is remembering what questions to ask, how to formulate them, and what the important pieces usually are. Details are easy to find on wikipedia or in papers if you’re familiar with the high-level structure.
To put it differently: you want to already have an idea of what kinds of things are usually important for problems in some field, and what kinds of things usually aren’t important. If you have that, then it’s fast and easy to look up the parts which are important for any particular problem, and double-check that you’re not missing anything crucial.
First, the standard answer: Bryan Caplan’s The Case Against Education. Short version: education is about signalling to future employers how smart/diligent/willing-to-jump-through-hoops/etc you are. Skill acquisition is mostly irrelevant. This is basically true for most people most of the time.
That said… I personally have gotten a lot of value out of things I learned in courses. This is not something that happens by default; the vast majority of my classmates did not get nearly as much value out of courses as I did. I’ll list a few things I did differently which may help.
Avoid nontechnical classes: this one is kind of a “well duh” thing, but there are some subtleties. “Technical” should be interpreted in a broad sense—things like e.g. law or languages aren’t technical in the sense of STEM, but they’re technical in the sense that the things they teach are intended to be directly useful. By contrast, subjects which are primarily about aesthetics or history or critical theory are not really intended to be directly useful.
Decreasing marginal returns: the first course in any particular field/subfield is far more valuable than the second course, the second course is more valuable than the third, etc. This suggests going for breadth over depth. In particular, I recommend taking one or two courses in many different fields so that you can talk to specialists in those fields without being completely lost. You don’t need to become an expert yourself; much of the value is in being able to quickly and easily work with specialists in many different fields. You can translate jargon and act as a human interface, and you can easily jump into many different areas.
General-purpose tools: focus on fields which provide tools applicable to many different domains. Most of applied math qualifies, as well as computer science, economics, and law. Ideally, you take one or two courses in some general-purpose subject, then run into applications of that subject while sampling other fields. By seeing it come up in different contexts, you’re more likely to remember and use it.
Summary of this point and previous: go for depth in general purpose tools, and practice those tools while gaining breadth in other areas.
Use available resources: I’ve covered about as much material in open courseware as I have in in-person classes. I’ve watched online lectures, I’ve read textbooks, and I’ve audited courses. (I’ve even audited classes at universities where I’m not registered—professors are usually happy with people just showing up.) In college, I’d often watch a semester’s worth of online lectures on a subject before taking a class on the subject; material is a lot easier to follow when you already have a general idea of where things are headed and how it all slots together..
Have a stock of problems: as you learn new tools, it’s useful to have a handful of problems to try them out on. Hard open algorithmic problems like integer factorization or P vs NP or graph isomorphism are great for testing out all sorts of applied math/CS tricks. “How could I use this to start a company and make a gazillion dollars?” is a problem which applies to practically anything. The problems should be things you’re interested in and enjoy thinking about, so that you’ll find it worthwhile to try out new tools on them even though most of the tools don’t actually yield breakthroughs on most of the problems.
This point and the previous one help a lot with actually remembering things and being able to apply them in-the-wild.
Optimize: at my college (Harvey Mudd), it was very easy to tell who had actually tried to optimize their course choices—it was the people who used the “build your own major” option. We only had half a dozen majors, and the course requirements always included things which weren’t really what any particular person was interested in. If you wanted to cram in more courses you were actually interested in, while avoiding irrelevant courses, a build-your-own major was the way to go.
More generally, you’ll get more out of classes if you read through the whole course catalog, mark the classes which sound interesting, and then optimize your schedule to focus on those classes. Sounds obvious, yet most people don’t do it.
Be comprehensive: you’re not going to know everything, but you can learn enough that nothing is very far from the things you do know. You can learn enough that you at least have some idea of which things you don’t know. You can learn enough that, even if you don’t know something, you’ve probably heard of it, and you have some idea of where to learn about it if you need to. The key is to aim for comprehensive knowledge of the world—you don’t need to know every little detail, but at least get the broad strokes of the big things. Anytime you don’t have a clue about something, pay attention to it, and look for the most general subject which would give you some background about that thing.
Math, physics and economics are particularly useful for comprehensive foundations—they give you the tools to solve practically anything in principle, though you’ll often need more specialized tools in practice.
Caplan puts the signalling share of the college income premium at 50%-80%, leaving (say) 20% for the human capital share. So your sentence calling HC “mostly irrelevant” is technically true, but I wouldn’t use the word ‘irrelevant’ for a feature explaining ~ a fifth of the variance.
Thanks. That’s a really nice list.I have not seen a lot of these ideas previously.Especially general purpose tool-idea and stock of problems-idea is very good.These ideas are really nice to ensure in-built spaced repetition.
But can you give me some ideas about the second question I asked.I cant do this because I am still undergrad.So pick a topic that you learned about say 4-5 years ago(or any time-frame for that matter),make sure that you haven’t used that particular knowledge for the past 4-5 years,try to get back to the same knowledge-level that you had acquired when you first learned the topic(or some % of it) and measure the amount of effort/time that you took.Then calculate the ratio of (this time or effort)/(time or effort when you first read that particular topic).
I graduated 7 years ago. During that time, I’ve actually used most of the subjects I studied in college—partly at work (as a data scientist), partly in my own research, and partly just when they happen to come up in conversation or day-to-day life. On the occasions when I’ve needed to return to a topic I haven’t used in a while, it’s typically been very fast.
But the question “how long does it take to get back up to speed on something I learned a while ago?” kind of misses the point. Most of the value doesn’t come from being able to quickly get back up to speed on fluid mechanics or materials science or inorganic chemistry. Rather, the value comes knowing which pieces I actually need to get back up to speed on. What matters is remembering what questions to ask, how to formulate them, and what the important pieces usually are. Details are easy to find on wikipedia or in papers if you’re familiar with the high-level structure.
To put it differently: you want to already have an idea of what kinds of things are usually important for problems in some field, and what kinds of things usually aren’t important. If you have that, then it’s fast and easy to look up the parts which are important for any particular problem, and double-check that you’re not missing anything crucial.