Teach Yourself Computer Science

I use my CS education every day. The trick was to take the bare minimum of theory and stuff my schedule with Systems:

- Thinking about and fixing bugs related to OS scheduling, like, why doesn’t my time.Sleep work as expected? (Intro to Systems, Operating Systems).

- Understanding the protocol layers involved in a network performance problem; writing network services to RFC specs (Networks and Distributed Systems).

- Selecting, working with, and understanding the limitations of distributed databases (Advanced Distributed Systems).

- Shifting the bulk of tricky computing into pure functions; borrowing design patterns like the state monad (Functional Programming).

- Parsing serialized data (Formal Languages for basic theoretical grounding, Programming Languages for compiler and interpreter implementation).

- Dealing with concurrency and using synchronization primitives (Intro to Systems).

- Awareness of security concerns and techniques, respect for the subtlety of crypto implementation, literacy when reading HN on subjects like ASLR or the Juniper RNG compromise (Intro to Security).

- Basic comfort with C, Make, vim, bash, awk, etc. (Intro to Programming).

The vast majority of my education was spent on programming projects to implement key components up and down the stack. Having a decent understanding of what they’re doing and how to approach writing them has been invaluable. The only thing I truly haven’t touched since college is discrete probability.

I doubt that parents or students who are spending tens of thousands of dollars on college and more than likely are leaving college with tons of debt are not expecting to be able to learn practical, marketable skills.

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.

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.

> Figuring them is easy from experience but the math behind that is way harder; anything beyond simple equations is like alien language to me ...

It took me a long (and often frustrating) time to figure out that the perception of difficulty is largely because of how implicit most everything is in mathematics. It's not like programming where eventually there is a compiler with a definite structure that's going to make your program behave in some exact specific way.

Even worse, is the way we're typically taught mathematics, it's always kinda starting in the middle (as opposed to the beginning). We aren't taught about what it is our how it works, so it's really hard to situate particular mathematical concepts we're taught into a larger coherent framework.

The way I interpret mathematical works changed so much (and for the better), once I realized that any significant bit of math you're using is the result of something a human was trying to accomplish. In the same way you can point to a piece of code and ask it's developer what the motivation was for some abstraction or algorithm, there are always reasons behind the development of mathematical systems. Once I understood that, rather than mathematical systems being these disconnected things that materialized out of nowhere, I started seeing commonalities and considering the reasoning behind them, and seeing more of the network that relates them. It became a lot more enjoyable. I'd recommend checking out some history of mathematics if you'd like to get a better sense of that (my personal intro recommendation: "Men of Mathematics" by E.T. Bell or maybe "Mathematics and the Imagination," which isn't history so much, but serves a similar goal).

If you're looking for something on algorithms specifically the book recommended by the author ("The Algorithm Design Manual") is a pretty friendly read and largely non-mathematical, while still containing all the useful bits (unless you're looking for a reference work on computational complexities or something).