Ben Halpern
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What computer science concepts should devs without a CS background prioritize learning?
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Top comments (64)
My favorite non-CS computer science algorithms book is: Grokking Algorithms: An illustrated guide for programmers and other curious people.
It stays at a pretty high level, touches the major algorithms, and explains big O very well!
Grokking Algorithms
Most self taught colleagues I've worked with, and others as well, are missing skills in writing good clean code. If you write good code everything else will be much easier to fix later e.g. performance issues, bugs.
So I'd suggest reading "Clean Code" by Robert Bob Martin, he's not only fun to read and watch holding talks, but is good at explaining things so that they make sense.
Depending on what kind of dev you are focused on, I really appreciated some courses on computer architecture - circuit design, machine language. Helped give a better conception of how computers actually work, and iron out misconceptions. Also OS design. Maybe not a first priority tough.
Computer architecture is great because it's fundamental but also not too abstract
Some developers come from a Computer Science background. Others, like me, never studied Computer Science in an academic environment.
In my opinion, the three main things for developers without a Computer Science background to study are Data Structures, Algorithms, and Lambda Calculus. Why these three?
Data Structures. These are the building blocks of data inside of a program. There are many different types of data structures, both simple and complex, and all of them have advantages and disadvantages (in terms of reading, writing, and space complexities). When a dev has to model a problem domain, he or she will first reach for the a data structure. It's good to know the pros and cons of different types of data structures and what they can offer in order to make the best choice for the problem domain. Understanding data structures will help a developer to solve problems quickly and efficiently using the right tools; in some cases, it will also give a better understanding of how data is mapped to computer memory.
Algorithms. Computers are fast - very fast - but solving problems still takes time. Devs should understand algorithmic complexity, know the advantages and disadvantages of specific approaches, and cultivate the foresight to make algorithmic choices based on input and time. More importantly, devs should be able to identify problems which can be solved (trivially or non-trivially) versus problems that cannot be solved at all within a reasonable time period. Algorithms, in conjunction with Data Structures, are the meat and potatoes of our programs.
Lambda Calculus. Modern languages have many design patterns - OOP, functional, declarative, and so on - but Lambda Calculus is a basic, simple model of computing that lends itself very well to any developer who wants to improve the way that they read, write, and understand code. Gaining an understanding of Lambda Calculus can allow a developer to think in terms of inputs and outputs, which lead to further inputs and further outputs. Understanding the compositional, functional style of Lambda Calc can make many problems much more accessible. With Data Structures and Algorithms, Lambda Calculus can elevate a developer to the next level.
Non-CS-major here. Got any suggested readings on algorithms or lambda calc?
This one is very good coursera.org/learn/algorithms-part1 although a bit hard.
I really liked the introduction to Lambda Calculus in Haskell: Programming from First Principles: haskellbook.com/
Besides data structures and algorithms, there is some math stuff that is surprising helpful. Calculus is fundamental to model a lot of real problems and to think in terms of velocity and aggregation. Linear algebra is pure computing: once you get to think in terms of matrices, a lot of very, very complex stuff becomes way simpler. Number Theory is fundamental for a lot of disciplines. But the most important math subject IMHO is Probability and Statistics: it permeates everything.
Besides that, the basic of networking and cryptography are very useful as well.
+1. The one thing I do regret most not paying attention to in my CS degree (which might be a good way to think about the question above) was probability and statistics. With ML this is becoming ever more important. Personally, I still don't feel 100% confident about my maths.
Regarding data structures and algorithms: I find most lists out there not really useful as they tend to cover data structures either not really in use anymore (e.g. linked lists) or so frequently used that a CS degree doesn't add detail (e.g. hash tables). The biggest advantage of a university course is that it should teach you concepts and ideas you might one day find useful (e.g. bloom filters) - instead of tools that make you productive right now. As such, I believe graph algorithms, binary search, finite state machines, lambda calculus and yes, complexity analysis, are the most useful ones (all mentioned below) - alongside various Patters which are also mentioned below.
Big O notation, an understanding of what algorithms are "expensive," and an understanding of what happens with "expensive" algorithms at large scales.
I second Big O notation and this whole idea. I didn't do an entire computer science degree, but I did some, and these concepts are what have stuck with me as considerations that come up a lot.
Time to write my Big O practical applications essay. Still need to fully understand it though. I read a great book about CS concepts written by someone who didn't have a CS degree. Not an ad but a good book.
bigmachine.io/products/the-imposte...
Ah, someone beat me to it I see :)
I couldn't agree more about this recommendation.
For some reason at my CS degree we hardly touched on Big O. Does anyone have some great resources for gaining a better understanding?
I know it is expensive and massive but I really think every developer should have a copy of CLRS. It is well organized, well written, and extremely thorough. In fact I was just paging through it today to review a few graph algorithms.
How to structure systems to support concurrency.
We are entering a major shift in industry where concurrency is going to become mandatory. Programming with shared data and interwoven state is going to cause massive loss of time and energy in the future.
The sooner we begin the shift toward a more stateless asynchronous programming methodology the better we will all be.
If you are a web developer: Databases. I took an intro to databases course in college and even at that pretty beginner level, I still use what I learned in that class every day. Technology stacks may change, but data isn't going anywhere.
Yes I strongly agree. I had worked with new self-taught web developers and database skills was the most glaring thing they needed to learn. Knowing how to design a database to write SQL queries that has good enough read and write performance, knowing ACID concepts, knowing when transactions are needed, knowing the differences between SQL and no-SQL databases and to pick the best one to use rather than the easiest to use. Database is one of the most important things a web developer should know.
Easily the most fundamental skill to have besides algorithms and data structures.
I'll toss a do-not in here: Sorting.
Sorting can be fun, and can produce some interesting visualizations, but the best for most situations is already built into your language, probably, and it likely goes by the name
sort().There are a lot of things to learn, But the most important IMHO should be Data Structures and algorithms.
With some basic knowledge about both, Writing code becomes less about "Let's get this to run / work." and more about "how efficient is this? / could i do this is a more efficient way?"
Naturally the search for efficient ways to do stuff will lead to some other important concepts like concurrency, caching etc
Stand-alone code.
At my current employer, I am one of the few people, if not the only person, with a computer science background. Yet almost everyone is a "coder". Some are writing Stata, some R, some Python, and I've heard rumors of a couple of web and db developers. They know far more math than I do and can Google the rest.
But what what they can't do is write stand-alone, automated code. One of the skills someone with a CS background must learn is how to write code that compiles and runs on someone else's machine. CS students have to submit source code that the professor will compile and run. The "but it works on my machine" refrain doesn't cut it in a CS class. Code has to run and run correctly under someone else's account, on someone else's machine, and maybe with different input data. That is a skill you can only get from a CS class, not Google, and not Coursera.
Hi John. Could you please elaborate a bit more on this? A bit more of explanation would be very appreciated. Thanks!
Most of the responses here are technical issues, like Big O notation or lambda calculus. Anyone can figure that stuff out on their own. I am talking about engineering issues that are important when building something. The differences between being a coder and a software engineer are not new. These engineering issues can be learned too, but someone learning on their own might never be aware of their existence to begin with.
I saw this in action at my (now former) employer. They knew about Big O notation, math of all kinds, and git. But they couldn't make a program run the same way twice, or on a different machine. There was no knowledge of UNIX, environments, dynamic loading, databases, information theory, text encoding, testing, validation, chain of reference, etc. Yet, by the only metric that mattered, securing more funding from wealthy donors, they were wildly successful.
I also see this in the Apple Developer forums. To get an app in the App Store, all you have to do is get it to launch once, for about 30 seconds, and maybe change a screen, on an Apple reviewers device. That is a challenge. Those indie developers, whether working alone or with a roomful of people just like them, don't understand the issues necessary to make an app run on someone else's device. They can figure it out eventually of course. But it is much more frustrating than having worked through those issues in a 2nd year data structures course in University.
I understand your point, but the question was specific to CS, not Software Engineering as a topic. But I'm sharing your opinion here - CS is not that hard to learn on your own, while I notice that self taught devs lack the Software Engineering skills and practices, and don't even know about the existence of them.
I'd guess it's like that because CS topics are very hot and trending buzz-words that you can easily hear about and google random course/book. But Software engineering topics are a bit more specific, and you have to know about them before searching for materials.
the most important one - how to use Google effectively to find whatever you need for CS learning.
I guess that very much depends on what you are working on.
For example when I first started working with LINQ in C# I loved it. How on earth did they make that work?? So I watched some channel 9 videos (eg. Bart de Smet on 'Ana, Bind and Cata'). I did a course on functional programming @ edX. It no longer seems like magic to me. Not sure if that makes me a better developer, guess I just don't like to rely on magic :o)
If you write many sql queries (either directly or via an ORM), learn about b-trees and indexing. It will help you understand how to make efficient use of your database.
So to wrap it up, prioritize what's relevant to your part of the puzzle so you can reap the benefits.
Things which proved helpful
I finished a Bachelor degree in Applied Computer Science about three years ago and am working as software developer since then. Those are the things which have proved most helpful from my studies:
Things I still want/need to learn
Follow this link . This will be a good learning path for non-CS programmers
(I am also non-CS coder!!!)
Google's Technical Development Guide