Teach Yourself Computer Science

I suspect this has helped you more than you seem to know. This knowledge opens doors; gets you jobs you couldn't otherwise get; and makes you a better programmer.

>> In Python (my language of choice for the last 7 years along with JavaScript) I do not really have to care about pointers, stack or heap allocation, how memory gets create or cleaned, etc. I also do not need to know if there is a virtual machine, a garbage collector, etc.

I have seen some horrendous Python code written by people who didn't understand these things. The Python VM "hides" them from you, but they're still relevant for writing fast, correct code as almost every operation in the language ends up using one or more of those "under the hood".

Best of luck with Rust and your startup!

Anyone in the EECS department who was serious about a future in programming took, at a minimum, two courses on algorithms / data structures (CS61B / CS170), one of which was required for the degree. I suspect that's partially covered by what any crack-the-coding-interview study course would try to teach you anyways (dynamic programming, tree / graph traversal; NP completeness and reductions probably less so).

In general, each CS course at Cal had ~3 hours a week of lecture, 1-3 of discussion / labs, and ~2-10+ hours of homework, varying on the individual and course (not to mention whatever auxiliary studying one might do for exams). With 15 weeks of instruction, that adds up to a minimum of 90 hours, but realistically, 200 might not even begin to describe some courses. A full courseload might look like 4 classes (potentially 3 CS classes + 1 humanities course) and is designed to be ~40 hours a week.

Agreed with the sibling comment that this list seems to have a number of notable omissions -- in particular, it looks to be very systems-oriented. But I also don't think that you _need_ all these courses to be a fully-fledged programmer. Sure, knowing how compilers / languages work might make it easier to pick up more languages, but learning languages across multiple programming paradigms (e.g. functional vs imperative vs declarative), which you'll get anyways if you follow this course list, is more valuable than implementing a recursive descent parser (which will make you really understand one language).

When I went there, Berkeley didn't have an undergrad course dedicated to distributed systems. But, to be fair, the MIT course listed is graduate-level. That seems to be the biggest outlier, as half the catalog consists of intro-level courses at Cal: Programming, Computer Arch, Math for CS, Algos / Data Structures (at least, the first half of the lectures mirrors CS61B; the latter half is closer to CS170).

If I had to suggest a sequencing based on the course parallels, I'd go with:

Core: Programming -> Computer Arch, (Math for CS -> Algorithms / Data Structures)

Intermediate: OS / Networking / DBs / Compilers

Advanced: Distributed Systems (probably want OS / Networking as prereqs, at a minimum)

But, I'd expect a small portion of undergrads took all of the intermediate topics, much less distributed systems.

Regularly-offered upper div courses that evidently didn't make the cut, off the top of my head: Security (this is a big one, IMO), AI, Graphics, Software Engineering, HCI, Computability / Complexity (automata theory among other things), Computer Architecture (beyond the intro-level treatment). Supposedly ML also started being regularly offered for undergrads (CS189).

On top of this I am chased by procrastination. I have gotten a much better grip at this. But just as an example - I have tried learning Rust at least 4 times before since Rust got highlight on HN I guess. I failed each each one. This time, I have at least gotten to the point where I opened the documentation on Vectors! My goodness, who am I? Complete lack of patience.

This directly causes me to sense pressure when I see statements like "type 1/2" engineer. It is not the material that is attacking me. It is my own failure hiding under the shelter of ego.