jebmo’s posthaven

Frequently asked questions

What about AI/graphics/pet-topic-X?

We’ve tried to limit our list to computer science topics that we feel every practicing software engineer should know, irrespective of specialty or industry. With this foundation, you’ll be in a much better position to pick up textbooks or papers and learn the core concepts without much guidance. Here are our suggested starting points for a couple of common “electives”:

• For artificial intelligence: do Berkeley’s intro to AI course by watching the videos and completing the excellent Pacman projects. As a textbook, use Russell and Norvig’s Artificial Intelligence: A Modern Approach.
• For machine learning: do Andrew Ng’s Coursera course. Be patient, and make sure you understand the fundamentals before racing off to shiny new topics like deep learning.

How strict is the suggested sequencing?

Realistically, all of these subjects have a significant amount of overlap, and refer to one another cyclically. Take for instance the relationship between discrete math and algorithms: learning math first would help you analyze and understand your algorithms in greater depth, but learning algorithms first would provide greater motivation and context for discrete math. Ideally, you’d revisit both of these topics many times throughout your career.

As such, our suggested sequencing is mostly there to help you just get started… if you have a compelling reason to prefer a different sequence, then go for it. The most significant “pre-requisites” in our opinion are: computer architecture before operating systems or databases, and networking and operating systems before distributed systems.

Who is the target audience for this guide?

We have in mind that you are a self-taught software engineer, bootcamp grad or precocious high school student, or a college student looking to supplement your formal education with some self-study. The question of when to embark upon this journey is an entirely personal one, but most people tend to benefit from having some professional experience before diving too deep into CS theory. For instance, we notice that students love learning about database systems if they have already worked with databases professionally, or about computer networking if they’ve worked on a web project or two.

How does this compare to Open Source Society or freeCodeCamp curricula?

The OSS guide has too many subjects, suggests inferior resources for many of them, and provides no rationale or guidance around why or what aspects of particular courses are valuable. We strove to limit our list of courses to those which you really should know as a software engineer, irrespective of your specialty, and to help you understand why each course is included.

freeCodeCamp is focused mostly on programming, not computer science. For why you might want to learn computer science, see above.

What about language X?

Learning a particular programming language is on a totally different plane to learning about an area of computer science — learning a language is much easier and much less valuable. If you already know a couple of languages, we strongly suggest simply following our guide and fitting language acquisition in the gaps, or leaving it for afterwards. If you’ve learned programming well (such as through Structure and Interpretation of Computer Programs), and especially if you have learned compilers, it should take you little more than a weekend to learn the essentials of a new language.

What about trendy technology X?

No single technology is important enough that learning to use it should be a core part of your education. On the other hand, it’s great that you’re excited to learn about that thing. The trick is to work backwards from the particular technology to the underlying field or concept, and learn that in depth before seeing how your trendy technology fits into the bigger picture.

Why are you still recommending the Dragon book?

The Dragon book is still the most complete single resource for compilers. It gets a bad rap, typically for overemphasizing certain topics that are less fashionable to cover in detail these days, such as parsing. The thing is, the book was never intended to be studied cover to cover, only to provide enough material for an instructor to put together a course. Similarly, a self-learner can choose their own adventure through the book, or better yet follow the suggestions that lecturers of public courses have made in their course outlines.

How can I get textbooks cheaply?

Many of the textbooks we suggest are freely available online, thanks to the generosity of their authors. For those that aren’t, we suggest buying used copies of older editions. As a general rule, if there has been more than a couple of editions of a textbook, it’s quite likely that an older edition is perfectly adequate. It’s certainly unlikely that the newest version is 10x better than an older one, even if that’s what the price difference is!

Who made this?

This guide was written by Ozan Onay and Myles Byrne, instructors at the Bradfield School of Computer Science in San Francisco. It is based on our experience teaching foundational computer science to hundreds of mostly self-taught engineers and bootcamp grads. Thank you to all of our students for your continued feedback on self-teaching resources. Thanks too to Alek Sharma, Omar Rayward, Ammar Mian and Tyler Bettilyon for feedback on this guide.