10 ms·

Teach Yourself Computer Science

334 points by rspivak 8y ago

_t94r 8y ago
How does it compare to OSSU (https://github.com/ossu/computer-science/blob/dev/README.md https://github.com/ossu/computer-science/blob/dev/README.md) ?
- gruglife 8y ago
  At the bottom of the website, the authors compare the two and freeCodeCamp. How does this compare to Open Source Society or freeCodeCamp curricula? The OSS guide has too many subjects, suggests inferior resources for many of them, and provides no rationale or guidance around why or what aspects of particular courses are valuable. We strove to limit our list of courses to those which you really should know as a software engineer, irrespective of your specialty, and to help you understand why each course is included. freeCodeCamp is focused mostly on programming, not computer science. For why you might want to learn computer science, see above.
  - karimdag 8y ago
    I was on mobile so didn't get to scroll to the bottom of it. Thanks!
- Kagerjay 8y ago
  I quickly audited all the courses a few months ago, I find OSSU to be much easier to grasp than teachyourselfcs.com because it doesn't always use modern day MOOC's which makes learning things way quicker You can learn things by a book sure. But if you go on a udemy or coursea or whatever course, there's always Q/A comments with other people working on the same course and that insight is extremely invaluable so this is why I prefer OSSU over teachyourselfcs.com. I prefer MOOC's because there's also some level of accountability as well, you can go for the certificates too but I don't really care too much about them. OSSU does have way too many topics just pick 80% of them and you should be okay, depending on what your weaknesses are. Some course overlap in both so you should focus on those first and foremost. These include things like nand2tetris, 3blue1brown's linear algebra, data structures & algorithms (various choices, sedgewick, skienna, etc). Do your homework on reddit threads as well before picking one or the other. I like OSSU because it also tells you what the prereqs are clearly as well. Currently doing nand2tetris because I have a lack of hardware knowledge and the course is fantastic. Also doing a lot of math courses so I can do mathmatical proofs much easier to understand why one algorithm is better than another, or how to derive it based on whoever invented it etc. I prefer a traditional book for math though, but everything else I prefer MOOC's.
lainon 8y ago
previous discussion, 237 comments: https://news.ycombinator.com/item?id=13862284 https://news.ycombinator.com/item?id=13862284
mlpinit 8y ago
This seems like a thought out list of recommendations. I have enjoyed going through some of those books myself. However, the persuasive argument seems rather biased and somewhat toxic. There are many software developers out there, without a strong CS background, who write useful tools used by other developers. Does anybody here use Homebrew? The author admits that Homebrew is not the perfect software, but I think we can all agree that it's highly successful and helps many developers every day. The field of computer science can be rather punishing if you don't grow up with a STEM mentality. I don't think there's a need to put any more pressure on developers and tell them they are not on a path to make enough money, that they will not work on interesting problems and that they have to spend another 900 to 1800 hours before they are worthy. We should instead foster a mentality of inclusion and promote learning by getting people excited about spending 100 hours on a particular subject and not persuade people to learn based on the fear of missing out. And we're not addressing the really big elephant in the room. So many bright people spend 900 to 1800 hours developing their CS skills and end up working on trivial things that could just as easily be tackled by somebody with a bootcamp background. https://www.quora.com/Whats-the-logic-behind-Google-rejecting-Max-Howell-the-author-of-Homebrew-for-not-being-able-to-invert-a-binary-tree https://www.quora.com/Whats-the-logic-behind-Google-rejectin...
yters 8y ago
I have many degrees in computer science, and have used almost none of the knowledge in programming jobs. So, the content is fairly useless, practically speaking. I've also found the more I focus on theoretical elements while at work, the more useless I become. The actually useful elements are big picture, and can probably be taught in one or two classes. Much more useful than technical knowledge are the abilities to reason about systems and solve problems. In my opinion, the CS classes seem to be almost purposefully obtuse.
- agumonkey 8y ago
  Anyone else concur with yters experience ?
  - guhidalg 8y ago
    I concur. The way I think about it is that what you learn in class is valuable for understanding the abstractions that you will use in industry. If you’re working in C# or Java, most of the time you don’t care about the cost of memory allocation, method calls, reflection voodoo, file system access, etc... BUT, in those rare situations where the abstraction causes a performance or correctness issue, then all that academic knowledge becomes valuable. I find that the instances of these problems are very rare but when they occur you have an opportunity to deliver a lot of value. For example, at my job we have a rather slow build. Looking at the logs, it’s because we spend a lot of time doing I/O. Someone had the idea to use symlinks instead of file copies. Badabing, badaboom, we got something like a 3x speed up from doing that.
    - newbie912 8y ago
      Surprisingly one of the areas I use my comp sci knowledge the most is in one of the most widely applied areas - relational databases. Every programmer spends lots of time reading and writing data, and understanding a lot of the low level operations, plus linear algebra, can really help with performance and assessing alternatives.
  - skylark 8y ago
bogardon 8y ago
My personal experience is that I got bored with frontend and pushing pixels, and when I tried to switch into infra work I found my incomplete picture of CS made it hard for me to grasp concepts other seem to take for granted. Maybe you don't have to have a CS degree, but even having a survey knowledge of major CS topics will at least give you more confidence.
- Waterluvian 8y ago
  I'd love to know what kinds of concepts you struggled with. I do maybe 30% front end, then a whole bunch of everything else and often wonder what entire categories I'm blind of.
  - bogardon 8y ago
    Just like OS concepts like sockets/files/pipes and how clang/ld/dyld actually works.
Waterluvian 8y ago
There aren't two types of software engineers. It's a spectrum, possibly multidimensional. The attraction to declaring two types of any kind of person is that you can bin all the desirable and undesirable traits together.
squarefoot 8y ago
My theoretical knowledge of computer science can be approximated to zero, as is my knowledge of math, but for some time I worked as a programmer with good results; in my best days I could write about two hundred lines of C or Pascal in a few hours, then go home without testing them because the machine wasn't available and be complimented the following day because they compiled and worked without any errors. I still felt I was lacking something so I tried to improve reading some books but all of them required high math skills I never had, so I quickly gave up. (Niklaus Wirth algorithms book probably is the only exception here) I can perfectly figure in my head registers loading data and shifting them around, logic gates changing and combining values, stacks pushing and popping words, electrons flowing through components etc, and for example can diagnose problems in analog circuits due to say bad decoupling, ground loops etc. Figuring them is easy from experience but the math behind that is way harder; anything beyond simple equations is like alien language to me, and the very few times I grasped something beyond that I quickly forgot for lack of continuous exercise. Did anyone else encounter the same discouraging level of difficulty? So my question would rather be: are there any good algorithms book for nearly complete math illiterates like me that would help to learn both in the process (and possibly not hate them :)?
- senthil_rajasek 8y ago
  Algorithms ( sorting, for instance) don't require knowledge of advanced math. Analysing the computational cost requires some math or math like thinking. Advanced math is linked with computing mostly because the roots of computing lie in mathematical logic. So you can become a master programmer without advanced math knowledge. In short, most algorithms 101 books should be accessible to someone who considers themselves math illiterate.
  - Daishiman 8y ago
    Yeah, but a substantial amount of problems that are dealt with through computers _do_ require mathematical tools beyond that. Statistics, for example, appears in any non-trivial computer science problem, like network congestion, analyzing proper parameters for algorithms, probabilities of getting lock contention, distribution of dispatched instructions, audio psychoacoustics, etc. Computer vision and graphics programming requires a solid foundation of linear algebra and geometry. In short, those tools are pretty necessary if you want to approach computer science as a science and as a tool for engineering beyond the very basics.
dan-robertson 8y ago
I don’t really like the recommendation for SICP. The book expects a certain level of mathematical maturity from the reader and I think this level is significantly greater than the level that might be expected from the targets of this website. Mathematical maturity in this case can be broken into two parts: 1. Understanding what a proof is, what is required to prove some proposition, how to figure out and write a proof, how to cope with struggling to prove something, and how to carefully read definitions. 2. Just knowing what “basic” things are. In the case of SICP these include calculus in one variable, lots of elementary algebra, an understanding of polynomials and exponentials, vectors and linear transformations (roughly the level of geometry required for basic classical mechanics (c.f. SICM although this is a slightly more advanced way of doing mechanics)) It is easy to see this requirement by looking at the exercises from the book. They start with calculus themed exercises (e.g. Newton’s method), move on towards more algebra flavoured problems and then vectors/geometry. Later exercises are not on mathematics but require one to apply a reasonable amount of mathematical sophistication to get the answer. Throughout are exercises which rely on the reader carefully applying their careful reading of carefully written definitions for things like models of evaluation. I have another issue which is with the following: CS is basically a runaway branch of applied math, so learning math will give you a competitive advantage. I basically disagree with this entirely. I would say that computer science is basically a branch of pure mathematics with applications to engineering practice with real-life computers. I’m not sure I would describe it as runaway but some branches of computer science (eg operating systems) are, I think, not really branches of mathematics.
- westoncb 8y ago
  That kind of annoyed me when I delved into a it a while ago, too. I have decent mathematical sophistication (I do a lot of linear algebra and computational geometry for work, read graphics papers and math history), though I'm largely self-taught, and had bad gaps in very basic things at the time I was attempting to read SICP. So when I came across all the math-related stuff in it, I'd have to stop reading and go learn about some things that were basically orthogonal to the subject matter I was actually interested in from the book (e.g. principles for effectively managing complexity with functional constructs). It's one thing to say CS and math have similar intrinsic structure—but that doesn't mean the content of the two fields need to overlap, and we shouldn't assume the content of one in the other. Imagine if you were reading a book on real analysis but they kept bringing in examples that required you to know CS stuff.
jokoon 8y ago
Of course if you read every available online course, buy books, you can teach yourself CS. I'm always more interested in reading quality material, that teaches things quickly and efficiently, with practice and not just long theory, without going into dark details. Those material are more rare. It's also often more interesting to learn about subjects you're interested into, instead of just "learning CS". I have an allergy to academic materials, and reading books that teach theory without explaining their application. Math is important, but it's a mean to an end, and to me it's better to teach CS with code. You cannot teach the proper math to everybody. The finality of computers is to use them by writing code and algorithms that perform well or better. Analyzing computability and other theory seems like applied math, so it's not really applied to computers science in my mind. Being able to pick up machine learning is great, but there won't be many individuals using that sweet math to do CS research. I used to believe programming was always a field of research in a way. It's not. You don't see random developers building ground breaking algorithms that change the world. That doesn't happen. Programming is about engineering and tinkering. You don't need to teach yourself computer science, you need to teach yourself more math, or learn programming, or learn electronics.
hackermailman 8y ago
I didn't really get algorithm analysis, beyond the standard sequential analysis of this simple operation maps to X complexity, until I did some complexity theory then it all sort of came together https://functionalcs.github.io/curriculum/#sec-12-1 https://functionalcs.github.io/curriculum/#sec-12-1
l39 8y ago
I think adding a concrete language (most probably C) and a course for OOP to the list might prove useful.
- furo 8y ago
  When you say "concrete language (most probably C)" are you referring to using one language throughout each subject?
  - l39 8y ago
    Not one language per subject. A separate subject to have basics strong. I feel learning C as a subject and getting concepts like pointers right might prove useful while learning datastructures and algorithms. The article assumes learning a language is very easy. But for a self taught programmer its easy to fall into the trap of straightaway going to high level language like dynamically typed languages. It would be more appropriate to learn C followed by datastructures followed by compiler design in that order.
    - furo 8y ago
      Late reply, but looks like C is covered in the Computer Architecture section. How deep does it go into C? That I don't know.
noetic_techy 8y ago
Does this even matter given that Apple is developing a new screen technology? https://www.bloomberg.com/news/articles/2018-03-19/apple-is-said-to-develop-displays-to-replace-samsung-screens https://www.bloomberg.com/news/articles/2018-03-19/apple-is-...
blackrock 8y ago
I think the problem with Computer Science is that the focus has been on the C and C++ programming languages. It goes back to the 1970s. C was created to have all these cute little tricks that you could do to manipulate your data in memory. This led to pointers and all the fancy pointer arithmetics, that allowed your program to run a few clock cycles more efficiently. But, the cost is that you shoot yourself in the foot, once in a while. Fast forward to now, and squeezing a few extra clock cycles here and there, and memory usage conservancy is largely irrelevant, except for a few edge cases. C is just a bad programming language. And C++ just inherited all its defects, just to bring in Objects. But the objects were a poorly designed idea, and was terribly executed. In C and C++, instead of just focusing on your problem, you have to manage the language, and its built in defects. Then came Java. And it was the opposite reaction to C++. No more pointer arithmetics, everything is now a reference. Ok, good. But, it created another problem. The framework monstrosity. Learning the language itself is simple enough. But having to work with someone else's poorly designed framework, that makes no logical sense, is just unbearable. In Java, you now have to manage the framework. I like Python for its brevity and conciseness. And especially for its flexibility with functions. It allows you to program in a pure functional style. It's a breath of fresh air. I can build out my code like lego blocks, stress test each function, and then connect it all together. And the results work flawlessly. Except for one catch, it runs a little slower. I'm hoping this move into Functional Programming will be the next true wave to come into the computer industry.

334 points by rspivak 8y ago

How does it compare to OSSU (https://github.com/ossu/computer-science/blob/dev/README.md https://github.com/ossu/computer-science/blob/dev/README.md) ?

gruglife 8y ago
At the bottom of the website, the authors compare the two and freeCodeCamp. How does this compare to Open Source Society or freeCodeCamp curricula? The OSS guide has too many subjects, suggests inferior resources for many of them, and provides no rationale or guidance around why or what aspects of particular courses are valuable. We strove to limit our list of courses to those which you really should know as a software engineer, irrespective of your specialty, and to help you understand why each course is included. freeCodeCamp is focused mostly on programming, not computer science. For why you might want to learn computer science, see above.
- karimdag 8y ago
  I was on mobile so didn't get to scroll to the bottom of it. Thanks!
Kagerjay 8y ago
I quickly audited all the courses a few months ago, I find OSSU to be much easier to grasp than teachyourselfcs.com because it doesn't always use modern day MOOC's which makes learning things way quicker You can learn things by a book sure. But if you go on a udemy or coursea or whatever course, there's always Q/A comments with other people working on the same course and that insight is extremely invaluable so this is why I prefer OSSU over teachyourselfcs.com. I prefer MOOC's because there's also some level of accountability as well, you can go for the certificates too but I don't really care too much about them. OSSU does have way too many topics just pick 80% of them and you should be okay, depending on what your weaknesses are. Some course overlap in both so you should focus on those first and foremost. These include things like nand2tetris, 3blue1brown's linear algebra, data structures & algorithms (various choices, sedgewick, skienna, etc). Do your homework on reddit threads as well before picking one or the other. I like OSSU because it also tells you what the prereqs are clearly as well. Currently doing nand2tetris because I have a lack of hardware knowledge and the course is fantastic. Also doing a lot of math courses so I can do mathmatical proofs much easier to understand why one algorithm is better than another, or how to derive it based on whoever invented it etc. I prefer a traditional book for math though, but everything else I prefer MOOC's.

lainon 8y ago

previous discussion, 237 comments: https://news.ycombinator.com/item?id=13862284 https://news.ycombinator.com/item?id=13862284

mlpinit 8y ago

This seems like a thought out list of recommendations. I have enjoyed going through some of those books myself. However, the persuasive argument seems rather biased and somewhat toxic. There are many software developers out there, without a strong CS background, who write useful tools used by other developers. Does anybody here use Homebrew? The author admits that Homebrew is not the perfect software, but I think we can all agree that it's highly successful and helps many developers every day. The field of computer science can be rather punishing if you don't grow up with a STEM mentality. I don't think there's a need to put any more pressure on developers and tell them they are not on a path to make enough money, that they will not work on interesting problems and that they have to spend another 900 to 1800 hours before they are worthy. We should instead foster a mentality of inclusion and promote learning by getting people excited about spending 100 hours on a particular subject and not persuade people to learn based on the fear of missing out. And we're not addressing the really big elephant in the room. So many bright people spend 900 to 1800 hours developing their CS skills and end up working on trivial things that could just as easily be tackled by somebody with a bootcamp background. https://www.quora.com/Whats-the-logic-behind-Google-rejecting-Max-Howell-the-author-of-Homebrew-for-not-being-able-to-invert-a-binary-tree https://www.quora.com/Whats-the-logic-behind-Google-rejectin...

yters 8y ago

I have many degrees in computer science, and have used almost none of the knowledge in programming jobs. So, the content is fairly useless, practically speaking. I've also found the more I focus on theoretical elements while at work, the more useless I become. The actually useful elements are big picture, and can probably be taught in one or two classes. Much more useful than technical knowledge are the abilities to reason about systems and solve problems. In my opinion, the CS classes seem to be almost purposefully obtuse.

agumonkey 8y ago
Anyone else concur with yters experience ?
- guhidalg 8y ago
  I concur. The way I think about it is that what you learn in class is valuable for understanding the abstractions that you will use in industry. If you’re working in C# or Java, most of the time you don’t care about the cost of memory allocation, method calls, reflection voodoo, file system access, etc... BUT, in those rare situations where the abstraction causes a performance or correctness issue, then all that academic knowledge becomes valuable. I find that the instances of these problems are very rare but when they occur you have an opportunity to deliver a lot of value. For example, at my job we have a rather slow build. Looking at the logs, it’s because we spend a lot of time doing I/O. Someone had the idea to use symlinks instead of file copies. Badabing, badaboom, we got something like a 3x speed up from doing that.
  - newbie912 8y ago
    Surprisingly one of the areas I use my comp sci knowledge the most is in one of the most widely applied areas - relational databases. Every programmer spends lots of time reading and writing data, and understanding a lot of the low level operations, plus linear algebra, can really help with performance and assessing alternatives.
- skylark 8y ago

bogardon 8y ago

My personal experience is that I got bored with frontend and pushing pixels, and when I tried to switch into infra work I found my incomplete picture of CS made it hard for me to grasp concepts other seem to take for granted. Maybe you don't have to have a CS degree, but even having a survey knowledge of major CS topics will at least give you more confidence.

Waterluvian 8y ago
I'd love to know what kinds of concepts you struggled with. I do maybe 30% front end, then a whole bunch of everything else and often wonder what entire categories I'm blind of.
- bogardon 8y ago
  Just like OS concepts like sockets/files/pipes and how clang/ld/dyld actually works.

Waterluvian 8y ago

There aren't two types of software engineers. It's a spectrum, possibly multidimensional. The attraction to declaring two types of any kind of person is that you can bin all the desirable and undesirable traits together.

squarefoot 8y ago

My theoretical knowledge of computer science can be approximated to zero, as is my knowledge of math, but for some time I worked as a programmer with good results; in my best days I could write about two hundred lines of C or Pascal in a few hours, then go home without testing them because the machine wasn't available and be complimented the following day because they compiled and worked without any errors. I still felt I was lacking something so I tried to improve reading some books but all of them required high math skills I never had, so I quickly gave up. (Niklaus Wirth algorithms book probably is the only exception here) I can perfectly figure in my head registers loading data and shifting them around, logic gates changing and combining values, stacks pushing and popping words, electrons flowing through components etc, and for example can diagnose problems in analog circuits due to say bad decoupling, ground loops etc. Figuring them is easy from experience but the math behind that is way harder; anything beyond simple equations is like alien language to me, and the very few times I grasped something beyond that I quickly forgot for lack of continuous exercise. Did anyone else encounter the same discouraging level of difficulty? So my question would rather be: are there any good algorithms book for nearly complete math illiterates like me that would help to learn both in the process (and possibly not hate them :)?

senthil_rajasek 8y ago
Algorithms ( sorting, for instance) don't require knowledge of advanced math. Analysing the computational cost requires some math or math like thinking. Advanced math is linked with computing mostly because the roots of computing lie in mathematical logic. So you can become a master programmer without advanced math knowledge. In short, most algorithms 101 books should be accessible to someone who considers themselves math illiterate.
- Daishiman 8y ago
  Yeah, but a substantial amount of problems that are dealt with through computers _do_ require mathematical tools beyond that. Statistics, for example, appears in any non-trivial computer science problem, like network congestion, analyzing proper parameters for algorithms, probabilities of getting lock contention, distribution of dispatched instructions, audio psychoacoustics, etc. Computer vision and graphics programming requires a solid foundation of linear algebra and geometry. In short, those tools are pretty necessary if you want to approach computer science as a science and as a tool for engineering beyond the very basics.

dan-robertson 8y ago

I don’t really like the recommendation for SICP. The book expects a certain level of mathematical maturity from the reader and I think this level is significantly greater than the level that might be expected from the targets of this website. Mathematical maturity in this case can be broken into two parts: 1. Understanding what a proof is, what is required to prove some proposition, how to figure out and write a proof, how to cope with struggling to prove something, and how to carefully read definitions. 2. Just knowing what “basic” things are. In the case of SICP these include calculus in one variable, lots of elementary algebra, an understanding of polynomials and exponentials, vectors and linear transformations (roughly the level of geometry required for basic classical mechanics (c.f. SICM although this is a slightly more advanced way of doing mechanics)) It is easy to see this requirement by looking at the exercises from the book. They start with calculus themed exercises (e.g. Newton’s method), move on towards more algebra flavoured problems and then vectors/geometry. Later exercises are not on mathematics but require one to apply a reasonable amount of mathematical sophistication to get the answer. Throughout are exercises which rely on the reader carefully applying their careful reading of carefully written definitions for things like models of evaluation. I have another issue which is with the following: CS is basically a runaway branch of applied math, so learning math will give you a competitive advantage. I basically disagree with this entirely. I would say that computer science is basically a branch of pure mathematics with applications to engineering practice with real-life computers. I’m not sure I would describe it as runaway but some branches of computer science (eg operating systems) are, I think, not really branches of mathematics.

westoncb 8y ago
That kind of annoyed me when I delved into a it a while ago, too. I have decent mathematical sophistication (I do a lot of linear algebra and computational geometry for work, read graphics papers and math history), though I'm largely self-taught, and had bad gaps in very basic things at the time I was attempting to read SICP. So when I came across all the math-related stuff in it, I'd have to stop reading and go learn about some things that were basically orthogonal to the subject matter I was actually interested in from the book (e.g. principles for effectively managing complexity with functional constructs). It's one thing to say CS and math have similar intrinsic structure—but that doesn't mean the content of the two fields need to overlap, and we shouldn't assume the content of one in the other. Imagine if you were reading a book on real analysis but they kept bringing in examples that required you to know CS stuff.

jokoon 8y ago

Of course if you read every available online course, buy books, you can teach yourself CS. I'm always more interested in reading quality material, that teaches things quickly and efficiently, with practice and not just long theory, without going into dark details. Those material are more rare. It's also often more interesting to learn about subjects you're interested into, instead of just "learning CS". I have an allergy to academic materials, and reading books that teach theory without explaining their application. Math is important, but it's a mean to an end, and to me it's better to teach CS with code. You cannot teach the proper math to everybody. The finality of computers is to use them by writing code and algorithms that perform well or better. Analyzing computability and other theory seems like applied math, so it's not really applied to computers science in my mind. Being able to pick up machine learning is great, but there won't be many individuals using that sweet math to do CS research. I used to believe programming was always a field of research in a way. It's not. You don't see random developers building ground breaking algorithms that change the world. That doesn't happen. Programming is about engineering and tinkering. You don't need to teach yourself computer science, you need to teach yourself more math, or learn programming, or learn electronics.

hackermailman 8y ago

I didn't really get algorithm analysis, beyond the standard sequential analysis of this simple operation maps to X complexity, until I did some complexity theory then it all sort of came together https://functionalcs.github.io/curriculum/#sec-12-1 https://functionalcs.github.io/curriculum/#sec-12-1

l39 8y ago

I think adding a concrete language (most probably C) and a course for OOP to the list might prove useful.

furo 8y ago
When you say "concrete language (most probably C)" are you referring to using one language throughout each subject?
- l39 8y ago
  Not one language per subject. A separate subject to have basics strong. I feel learning C as a subject and getting concepts like pointers right might prove useful while learning datastructures and algorithms. The article assumes learning a language is very easy. But for a self taught programmer its easy to fall into the trap of straightaway going to high level language like dynamically typed languages. It would be more appropriate to learn C followed by datastructures followed by compiler design in that order.
  - furo 8y ago
    Late reply, but looks like C is covered in the Computer Architecture section. How deep does it go into C? That I don't know.

noetic_techy 8y ago

Does this even matter given that Apple is developing a new screen technology? https://www.bloomberg.com/news/articles/2018-03-19/apple-is-said-to-develop-displays-to-replace-samsung-screens https://www.bloomberg.com/news/articles/2018-03-19/apple-is-...

blackrock 8y ago

I think the problem with Computer Science is that the focus has been on the C and C++ programming languages. It goes back to the 1970s. C was created to have all these cute little tricks that you could do to manipulate your data in memory. This led to pointers and all the fancy pointer arithmetics, that allowed your program to run a few clock cycles more efficiently. But, the cost is that you shoot yourself in the foot, once in a while. Fast forward to now, and squeezing a few extra clock cycles here and there, and memory usage conservancy is largely irrelevant, except for a few edge cases. C is just a bad programming language. And C++ just inherited all its defects, just to bring in Objects. But the objects were a poorly designed idea, and was terribly executed. In C and C++, instead of just focusing on your problem, you have to manage the language, and its built in defects. Then came Java. And it was the opposite reaction to C++. No more pointer arithmetics, everything is now a reference. Ok, good. But, it created another problem. The framework monstrosity. Learning the language itself is simple enough. But having to work with someone else's poorly designed framework, that makes no logical sense, is just unbearable. In Java, you now have to manage the framework. I like Python for its brevity and conciseness. And especially for its flexibility with functions. It allows you to program in a pure functional style. It's a breath of fresh air. I can build out my code like lego blocks, stress test each function, and then connect it all together. And the results work flawlessly. Except for one catch, it runs a little slower. I'm hoping this move into Functional Programming will be the next true wave to come into the computer industry.