• What's this repository for?
• Background
• Specialization Plan
• Self Study Curriculum Overview and My Progress
• Projects & Exercises
  • Projects
  • Exercises
• Topics Breakdown
• Reference

Originally, I created this repository to show potential recruiters what I've been doing in my ~7 months career gap. However, I quickly realized that this would also be useful to anyone out there who also found themselves wanting to study everything there is in the computer science field of study by themselves.

⚠️ Disclaimer: I am by no means an expert in Computer Science, in fact, as you will soon read, I am meerly a novice. Any advice should be taken with a grain of salt.

I am an Electrical Engineering graduate and have been working in the hardware (RF / electronics / embedded) industry for over 4 years. However, after finally getting my dream job abroad at Keysight Technologies Malaysia, I resigned after only working for a shy of 2 years due to some personal reasons. At that point, I also realized that I am more suited to pursue a career that allows me to work remotely. Along with other reasons that I wont be discussing here, I realized that I want to be in the computing industry.

However, scrapping away (almost) all of my previous knowledge and 4 years of experience to start from basically "zero" is not easy. Moreover, I don't want to only have practical skills that would get me an interview quickly without having a solid understanding of the fundamentals. From my experience, having a holistic view of your field of study is the upmost important thing in mastering your field of study. That's why I searched for a "self study CS curriculum". And if you're like me, you'll stumble upon Teach Yourself Computer Science. That is the main source for my self study journey.

Teach Yourself Computer Science seems perfect for me at first. However, I quickly realized that I lack some knowledge that are not listed there. Moreover, I also still need some practical knowledge on current technologies to understand how the industry is running today. With that additions, I came up with this self study checklist.

Since the beginning of my journey, I have determined to at least scratch the surface of every sub domain there is in Computer Science. And I encourage you to do so to. The reasons are:

• To remove bias when you have to make a decision on which specialization path to choose.
• You might found yourself needing to draw knowledge from multiple domains to craft a solution / product.
• It simply is intelectually interesting to see how each of the specialization are related and interacts with one another from a holistic view.

However, of course once you feel confident that you know the crux of each subdomain, you have to make a decision on what to focus on if you want to be technically successful. For me, I would like to focus on Systems.

The reason why I love Systems the most throughout my study, is because of my deep curiosity on seeing what's behind the abstraction, to see the complex machinery that make the systems that we all have taken for granted just work. That's actually the reason why 10 years ago I chose EE, but let's not get into that here. There are of course further specialization down this road, but for now, one level of specialization below Computer Science is good enough for me.

Here is an overview of what I have and will learn throughout my self study jouney up till now (March 2024). Started studies with progress above 75% are marked with 🟢, started studies with progress above 25% and below 75% are marked with 🟡, while studies with progress below 25% or not yet started are marked with ⚫.

Note that some topics might get more sub topics with more knowledge acquired.

* Notice that most of my references are books. If you like online courses better, unfortunately you'll need to look it up yourself.

* Algorithms is not listed in any of the dependency lists, altough it is assumed that the Algorithms reference is already read with at least 50% progress before moving on to other topics.

To really solidify the knowledge of what you've learned, you'll have to use that knowledge to solve problems by writing some code. I categorize two ways that you can do this, by doing projects and exercises.

Projects are usually larger, strikes close to what you might actually do in a specific target industry, and requires knowledge on various technologies.

Exercises on the other hand are small close ended problems that are specifically designed to test and solidify your knowledge on a very specific topic, sometimes in a prepared learning environment. In contrast to projects, the resulting code itself usually don't have much practical use outside of the learning environment.

Both are essential for learning. Most of the time, you need to do some exercises before diving into a project. Just doing exercises will deprive you of a holistic view and practical skills, but just doing projects will deprive you of really understanding specific things that might otherwise be lost in abstractions, and therefore hinder your ability to tackle the project.

Started and finished (is it ever finished?) projects have an entry in the repository column.

* You can use any programming language of your choosing.

* A repository containing my solutions to selected exercises from several book references is given below

• The Algorithm Design Manual
• Computer Systems: A Programmer's Perspective
• Computer Networking: A Top-Down Approach
• Computer Architecture: A Quantitative Approach
• Mathematics for Computer Science
• SICP
• Introduction to Automata Theory, Languages, and Computation
• Head First Java
• Head First Design Patterns
• Database Management Systems
• Clean Code
• Operating Systems: Three Easy Pieces
• Designing Data-Intensive Applications
• RTL Modeling with SystemVerilog for Simulation and Synthesis
• SystemVerilog for Verification
• Modern Processor Design: Fundamentals of Superscalar Processors
• Crafting Interpreters
• Compilers: Principles, Techniques, and Tools
• Computer Security: Principles and Practice
• 3D Computer Graphics: A Mathematical Introduction with OpenGL
• MDN Learning Area
• The Rust Programming Language
• Tour of C
• The C Programming Language
• A Tour of Go
• Effective Java
• Learn React
• Docker Guides
• Makefile Tutorial By Example
• CMake Tutorial
• Advanced Programming in the UNIX Environment
• Zephyr Docs
• Understanding the Linux Kernel
• Making Embedded Systems: Design Patterns for Great Software
• MicroC/OS-III Real Time Kernel
• Embedded Linux Primer