I'm also surprised Jeff Erickson's free lecture notes [3] aren't there given 1) its easy to remember domain 2) its incredibly high, practical quality. Practical because I've had interviews that just grab questions from the book, and also because his course is basically just a walk-through of the book. It's also very easy to read, and although I didn't do great in his class, his conceptual lessons still stick with me.
Couldn't recommend Jeff Erickson's lecture notes more for algorithms, DP, and the like. I too did rather poorly in the class so you're not alone! In a similar vein, Lawrence Angrave, a systems programming lecturer, has a wonderful crowd-sourced "book" [1] on all things systems programming. It is my go to resource for brushing up on these topics. Lastly, David Forsyth, a statistics/applied ML lecturer has a gold mine of a book for diving into ML and difficult concepts that come with it [2].
[1] https://github.com/angrave/SystemProgramming/wiki
[2] http://luthuli.cs.uiuc.edu/~daf/courses/AML-18/learning-book...
Not currently. Seems like a lot of work, especially with all the margin notes and figures. But if you could suggest tools that might make this easier, I'd give them a look.
It's also a bit intimidating and overhwelming. There is so much to learn and there is also a danger of just getting through textbooks that cover the same material that you've read before or covering stuff on a surface level without getting any practice with what you've learned. As a data scientist, it feels like anything from mathematics, computer science, statistics, large-scale systems, software engineering in general is within my domain and there is a real danger of spreading oneself a bit too thin and not getting that good at anything.
The fundamentals don’t change fast and are what everything else is built on, so I would start there.
For example, if you don’t have a traditional CS degree, https://teachyourselfcs.com/ is a curated and effective set of books.
If your trying to understand complex systems, I would read Designing Data Intensive Applications, which is perhaps the best and most useful technical book I have ever read, and covers the most important parts of distributed systems. A lot of what’s in the book are fundamental distributed systems, from the 70-80s?/newer things from early 2000s built by BigTechCo
I had a lot of qualms with my CS education, the professors in particular, but I have to hand it to them they always either tried to have the book and material online for free, or would use the international edition of whatever textbook we needed. Textbooks, and higher education in general, are a complete ripoff for 90% of people doing it.
Nothing worse than courses that require a textbook written by the lecturer decades ago and is now out-of-print, with only one copy in the library that all the students have to fight over.
https://runestone.academy/runestone/books/index has 100% free interactive versions of high quality textbooks. They use a JavaScript implementation of Python which runs in the browser (Skulpt) to provide a REPL.
Example Textbooks: Problem Solving with Algorithms and Data Structures using Python (3rd edition)
It strikes me as odd that these resources are listed by title, not by author. In my (natural science) field, it's very much the reverse. I wonder whether this is a disciplinary difference, or just a reflection of Gordon's preference.
Really helped me out when I was getting my CS degree. Some classes I'd use these free copies, but core classes ex. Algorithms I liked having the physical copy of CLRS.
It gives you a complete minimally ordered set to high quality a CS education.
> Still doesn’t really explain the contents of any of the linked courses or why you need to take them
That is the job of the course lecturer or author who know best. It is always done in the first lecture of any course and is provided in written form in the syllabus. All you need to do click on the links....
I'm also surprised Jeff Erickson's free lecture notes [3] aren't there given 1) its easy to remember domain 2) its incredibly high, practical quality. Practical because I've had interviews that just grab questions from the book, and also because his course is basically just a walk-through of the book. It's also very easy to read, and although I didn't do great in his class, his conceptual lessons still stick with me.
[1] http://acs.pub.ro/~cpop/SMPA/Computer%20Architecture%20A%20Q...
[2] https://github.com/Seanforfun/Books/blob/master/Computer/Com...
[3] http://algorithms.wtf