Teach Yourself Computer Science

If the idea of self-studying 9 topics over multiple years feels overwhelming, we suggest you focus on just two books: Computer Systems: A Programmer's Perspective and Designing Data-Intensive Applications. In our experience, these two books provide incredibly high return on time invested, particularly for self-taught engineers and bootcamp grads working on networked applications. They may also serve as a "gateway drug" for the other topics and resources listed above.

There are 2 types of software engineer: those who understand computer science well enough to do challenging, innovative work, and those who just get by because they’re familiar with a few high level tools.

Both call themselves software engineers, and both tend to earn similar salaries in their early careers. But Type 1 engineers progress toward more fulfilling and well-remunerated work over time, whether that’s valuable commercial work or breakthrough open-source projects, technical leadership or high-quality individual contributions.

The global SMS system does around 20bn messages a day. WhatsApp is now doing 42bn. With 57 engineers. pic.twitter.com/zZrtSIzhlR

— Benedict Evans (@BenedictEvans) February 2, 2016

Type 1 engineers find ways to learn computer science in depth, whether through conventional means or by relentlessly learning throughout their careers. Type 2 engineers typically stay at the surface, learning specific tools and technologies rather than their underlying foundations, only picking up new skills when the winds of technical fashion change.

Currently, the number of people entering the industry is rapidly increasing, while the number of CS grads is relatively static. This oversupply of Type 2 engineers is starting to reduce their employment opportunities and keep them out of the industry’s more fulfilling work. Whether you’re striving to become a Type 1 engineer or simply looking for more job security, learning computer science is the only reliable path.

Most undergraduate CS programs start with an “introduction” to computer programming. The best versions of these courses cater not just to novices, but also to those who missed beneficial concepts and programming models while first learning to code.

Our standard recommendation for this content is the classic Structure and Interpretation of Computer Programs, which is available online for free both as a book, and as a set of MIT video lectures. While those lectures are great, our video suggestion is actually Brian Harvey’s SICP lectures (for the 61A course at Berkeley) instead. These are more refined and better targeted at new students than are the MIT lectures.

We recommend working through at least the first three chapters of SICP and doing the exercises. For additional practice, work through a set of small programming problems like those on exercism.

Since this guide was first published in 2016, one of the most commonly asked questions has been whether we’d now recommend recordings of a more recent iteration of 61A taught by John DeNero, and/or the corresponding book Composing Programs, which is “in the tradition of SICP” but uses Python. We think the DeNero resources are also great, and some students may end up preferring them, but we still suggest SICP, Scheme, and Brian Harvey’s lectures as the first set of resources to try.

Why? Because SICP is unique in its ability—at least potentially—to alter your fundamental beliefs about computers and programming. Not everybody will experience this. Some will hate the book, others won't get past the first few pages. But the potential reward makes it worth trying.

If you don't enjoy SICP, try Composing Programs. If that still doesn't suit, try How to Design Programs. If none of these seem to be rewarding your effort, perhaps that's a sign that you should focus on other topics for some time, and revisit the discipline of programming in another year or two.

Finally, a point of clarification: this guide is NOT designed for those who are entirely new to programming. We assume that you are a competent programmer without a background in computer science, looking to fill in some knowledge gaps. The fact that we've included a section on "programming" is simply a reminder that there may be more to learn. For those who've never coded before, but who'd like to, you might prefer a guide like this one.

Computer Architecture—sometimes called “computer systems” or “computer organization”—is an important first look at computing below the surface of software. In our experience, it’s the most neglected area among self-taught software engineers.

Our favorite introductory book is Computer Systems: A Programmer's Perspective, and a typical introductory computer architecture course using the book would cover most of chapters 1-6.

We love CS:APP for the practical, programmer-oriented approach. While there's much more to computer architecture than what's covered in the book, it serves as a great starting point for those who'd like to understand computer systems primarily in order to write faster, more efficient and more reliable software.

For those who'd prefer both a gentler introduction to the topic and a balance of hardware and software concerns, we suggest The Elements of Computing Systems, also known as “Nand2Tetris”. This is an ambitious book attempting to give you a cohesive understanding of how everything in a computer works. Each chapter involves building a small piece of the overall system, from writing elementary logic gates in HDL, through a CPU and assembler, all the way to an application the size of a Tetris game.

We recommend reading through the first six chapters of the book and completing the associated projects. This will develop your understanding of the relationship between the architecture of the machine and the software that runs on it.

The first half of the book (and all of its projects), are available for free from the Nand2Tetris website. It’s also available as a Coursera course with accompanying videos.

In seeking simplicity and cohesiveness, Nand2Tetris trades off depth. In particular, two very important concepts in modern computer architectures are pipelining and memory hierarchy, but both are mostly absent from the text.

Once you feel comfortable with the content of Nand2Tetris, we suggest either returning to CS:APP, or considering Patterson and Hennessy’s Computer Organization and Design, an excellent and now classic text. Not every section in the book is essential; we suggest following Berkeley’s CS61C course “Great Ideas in Computer Architecture” for specific readings. The lecture notes and labs are available online, and past lectures are on the Internet Archive.

Hardware is the platform

We agree with decades of common wisdom that familiarity with common algorithms and data structures is one of the most empowering aspects of a computer science education. This is also a great place to train one’s general problem-solving abilities, which will pay off in every other area of study.

There are hundreds of books available, but our favorite is The Algorithm Design Manual by Steven Skiena. He clearly loves algorithmic problem solving and typically succeeds in fostering similar enthusiasm among his students and readers. In our opinion, the two more commonly suggested texts (CLRS and Sedgewick) tend to be a little too proof-heavy for those learning the material primarily to help with practical problem solving.

For those who prefer video lectures, Skiena generously provides his online. We also really like Tim Roughgarden’s course, available on Coursera and elsewhere. Whether you prefer Skiena’s or Roughgarden’s lecture style will be a matter of personal preference. In fact, there are dozens of good alternatives, so if you happen to find another that you like, we encourage you to stick with it!

For practice, our preferred approach is for students to solve problems on Leetcode. These tend to be interesting problems with decent accompanying solutions and discussions. They also help you test progress against questions that are commonly used in technical interviews at the more competitive software companies. We suggest solving around 100 random leetcode problems as part of your studies.

Finally, we strongly recommend How to Solve It as an excellent and unique guide to general problem solving; it’s as applicable to computer science as it is to mathematics.

I have only one method that I recommend extensively—it’s called think before you write.

In some ways, computer science is an overgrown branch of applied mathematics. While many software engineers try—and to varying degrees succeed—at ignoring this, we encourage you to embrace it with direct study. Doing so successfully will give you an enormous competitive advantage over those who don’t.

The most relevant area of math for CS is broadly called “discrete mathematics”, where “discrete” is the opposite of “continuous” and is loosely a collection of interesting applied math topics outside of calculus. Given the vague definition, it’s not meaningful to try to cover the entire breadth of “discrete mathematics”. A more realistic goal is to build a working understanding of logic, combinatorics and probability, set theory, graph theory, and a little of the number theory informing cryptography. Linear algebra is an additional worthwhile area of study, given its importance in computer graphics and machine learning.

Our suggested starting point for discrete mathematics is the set of lecture notes by László Lovász. Professor Lovász did a good job of making the content approachable and intuitive, so this serves as a better starting point than more formal texts.

For a more advanced treatment, we suggest Mathematics for Computer Science, the book-length lecture notes for the MIT course of the same name. That course’s video lectures are also freely available, and are our recommended video lectures for discrete math.

For linear algebra, we suggest starting with the Essence of linear algebra video series, followed by Gilbert Strang’s book and video lectures.

If people do not believe that mathematics is simple, it is only because they do not realize how complicated life is.

Operating System Concepts (the “Dinosaur book”) and Modern Operating Systems are the “classic” books on operating systems. Both have attracted criticism for their lack of clarity and general student unfriendliness.

Operating Systems: Three Easy Pieces is a good alternative that’s freely available online. We particularly like the structure and readability of the book, and feel that the exercises are worthwhile.

After OSTEP, we encourage you to explore the design decisions of specific operating systems, through “{OS name} Internals” style books such as Lion's commentary on Unix, The Design and Implementation of the FreeBSD Operating System, and Mac OS X Internals. For Linux, we suggest Robert Love's fantastic Linux Kernel Development.

A great way to consolidate your understanding of operating systems is to read the code of a small kernel and add features. One choice is xv6, a port of Unix V6 to ANSI C and x86, maintained for a course at MIT. OSTEP has an appendix of potential xv6 labs full of great ideas for potential projects.

Given that so much of software engineering is on web servers and clients, one of the most immediately valuable areas of computer science is computer networking. Our self-taught students who methodically study networking find that they finally understand terms, concepts and protocols they’d been surrounded by for years.

Our favorite book on the topic is Computer Networking: A Top-Down Approach. The small projects and exercises in the book are well worth doing, and we particularly like the “Wireshark labs”, which they have generously provided online.

For those who prefer video lectures, we suggest Stanford’s Introduction to Computer Networking course previously available via Stanford's MOOC platform Lagunita, but sadly now only available as unofficial playlists on Youtube.

You can’t gaze in the crystal ball and see the future. What the Internet is going to be in the future is what society makes it.

— Bob Kahn

It takes more work to self-learn about database systems than it does with most other topics. It’s a relatively new (i.e. post 1970s) field of study with strong commercial incentives for ideas to stay behind closed doors. Additionally, many potentially excellent textbook authors have preferred to join or start companies instead.

Given the circumstances, we encourage self-learners to generally avoid textbooks and start with recordings of CS 186, Joe Hellerstein’s databases course at Berkeley, and to progress to reading papers after.

One paper particularly worth mentioning for new students is “Architecture of a Database System”, which uniquely provides a high-level view of how relational database management systems (RDBMS) work. This will serve as a useful skeleton for further study.

Readings in Database Systems, better known as the databases “Red Book”, is a collection of papers compiled and edited by Peter Bailis, Joe Hellerstein and Michael Stonebraker. For those who have progressed beyond the level of the CS 186 content, the Red Book should be your next stop.

If you're adamant about using an introductory textbook, we suggest Database Management Systems by Ramakrishnan and Gehrke. For more advanced students, Jim Gray’s classic Transaction Processing: Concepts and Techniques is worthwhile, but we don’t encourage using this as a first resource.

Finally, data modeling is a neglected and poorly taught aspect of working with databases. Our suggested book on the topic is Data and Reality: A Timeless Perspective on Perceiving and Managing Information in Our Imprecise World.

Most programmers learn languages, whereas most computer scientists learn about languages. This gives the computer scientist a distinct advantage over the programmer, even in the domain of programming! Their knowledge generalizes; they are able to understand the operation of a new language more deeply and quickly than those who have merely learned specific languages.

Our suggested introductory text is the excellent Crafting Interpreters by Bob Nystrom, available for free online. It's well organized, highly entertaining, and well suited to those whose primary goal is simply to better understand their languages and language tools. We suggest taking the time to work through the whole thing, attempting whichever of the "challenges" sustain your interest.

A more traditional recommendation is Compilers: Principles, Techniques & Tools, commonly called “the Dragon Book”. Unfortunately, it’s not designed for self-study, but rather for instructors to pick out 1-2 semesters worth of topics for their courses.

If you elect to use the Dragon Book, it’s almost essential that you cherry-pick the topics, ideally with the help of a mentor. In fact, our suggested way to utilize the Dragon Book, if you so choose, is as a supplementary reference for a video lecture series. Our recommended one is Alex Aiken’s, on edX.

Don’t be a boilerplate programmer. Instead, build tools for users and other programmers. Take historical note of textile and steel industries: do you want to build machines and tools, or do you want to operate those machines?

— Ras Bodik at the start of his compilers course

As computers have increased in number, they have also spread. Whereas businesses would previously purchase larger and larger mainframes, it’s typical now for even very small applications to run across multiple machines. Distributed systems is the study of how to reason about the trade-offs involved in doing so.

Our suggested book for self-study is Martin Kleppmann's Designing Data-Intensive Applications. Far better than a traditional textbook, DDIA is a highly readable book designed for practitioners, which somehow avoids sacrificing depth or rigor.

For those seeking a more traditional text, or who would prefer one that’s available for free online, we suggest Maarten van Steen and Andrew Tanenbaum’s Distributed Systems, 3rd Edition.

For those who prefer video, an excellent course with videos available online is MIT’s 6.824, a graduate course taught by Robert Morris with readings available here.

No matter the choice of textbook or other secondary resources, study of distributed systems absolutely mandates reading papers. A good list is here, and we would highly encourage attending your local Papers We Love chapter.