SICP still deserves to be on such lists.

I also love Concrete Mathematics.

I prefer the Tanenbaum OS books over Stallings. In particular the design and implementation book, although it is more than a decade old now.

Just to add to this, I think John Levine's Linkers and Loaders is also a great reference.

When I need a refresher on the basics of Python, I refer to Python Distilled, and when I want a deep dive, I turn to Fluent Python. Reading these books makes me feel like I'm sitting next to an experienced, witty colleague.

I will take a look at Python Crash Course.

I agree that five books won't ever cover every discipline withing Computer Science. Just providing an introductory book, a university-level textbook, and an expert/graduate-level reference for each discipline turns into a long list.

See if this blog post helps out with sorting through the various CS subjects: https://tolerablecoder.blogspot.com/2022/03/a-short-list-of-...

> * Kurose, Ross - Computer Networking - A Top Down Approach

Over TCP/IP Illustrated?

Given unlimited time, read all of them, learn all the languages. It will all help make you a better programmer in your preferred language. With limited time (as a normal being), start with the top 100 books. Any of them. The next will be simpler than the first...

I have an M.Sc. in Comp.Sci. Flicking through books like these, all the chapter titles resonate with courses, exams, and problems we solved. It also makes me realise I have probably forgotten more than I like to think.

On the other hand, bashing my head against graph theory and logic, has made me a much better programmer. Similarly, the hours spent in Van Roy and Haridi's fairly abstract and technically language-agnostic "Concepts, Techniques and Models of Computer Programming" made me primed to learn a lot of languages fast - because I had the primitives mastered.

Projects are essential, but I've found there is a huge problem with your advice: you have no clue about the possible solution surface.

My advice to learners has been "try to learn as much about a topic as someone who has taken the subject in college and forgotten about it".

For example consider calculus: Someone who took calc 20 years ago and hasn't used it since will probably forget exactly how to compute most derivatives and integrals. But if someone mentions an optimization problem "we need to know when this curve peaks" or asks something involving finding the area under a curve, alarm bells should start ringing. They'll know this can be done, and likely go grab a calc book to refresh.

Another example I run across all the time, which is the opposite scenario: Survival analysis. I have been on countless teams where somebody needs to understand something like churn or the impact of a offering a discount that hasn't expired yet, etc. These are all classic survival analysis problems, yet most people are ignorant that this field of study even exists! Because of this I've seen so many times where people complain that "we'll have to wait months or years to see if these changes impact customer lifetime!" (note: if anyone out there is doing Churn or LTV analysis and aren't familiar with survival analysis, you are most certainly approaching it incorrectly).

I've seen a lot of people get frustrated with self study because they try to learn the material too well. If you aren't going to be using survival analysis soon, it's not really worth remembering all the details of how to implement a Kaplan Meier curve. But if you even have a vague sense of what problem this solves, when you encounter that problem in a project, you know where to go back to. Then you typically walk away with a much stronger sense of the subject then if you had studied it harder in the first place.

Computer science is to programming what physics is to engineering. They are not the same thing. You can do some programming without knowing computer science, but a general knowledge of computer science will give you a much more solid foundation, and for some aspects is indispensable.

Thats a little like saying if you want to learn mechanical engineering, fix things around your home and then do research when you get stumped.

Building a bunch of software projects probably isn’t a very efficient way of learning computer science. You might figure out things like big-O or A* on your own, but a more academic approach is going to take you further, faster.

That seems more like a necessary precondition, than a path to learning computer science. Like you will probably need to learn penmanship and multiplication tables before you get into real mathematics, but, that isn’t really mathematics.