# Essential Reads for Aspiring Self-Taught Computer Scientists
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To embark on a journey in computer science, you may find yourself eager to grasp the fundamental concepts of the field. Yet, it can be quite challenging to discover effective learning pathways. Many online materials come with hefty price tags or have varying assumptions about your prior knowledge. Most resources presume that you have a solid grasp of mathematics or a background in STEM fields. For those not coming from a scientific background, finding a manageable entry point can feel overwhelming. I can personally relate to this experience.
To assist others in a similar situation, I compiled a list of books structured to make learning feel attainable. This selection not only covers the foundational aspects of computer science but also addresses key course content, including Computer Architecture & Organization, Data Structures & Algorithms, and Computational Theory.
Anyone can learn programming without delving into academic computer science materials. However, if you genuinely wish to invest your time and effort into understanding these subjects, the rewards will be worth it. This is a lifelong journey, and the books listed here are just the starting point.
Computer Science Distilled
Kicking off the list is Computer Science Distilled by Wladston Ferreira Filho. If you're new to programming or computer science, this book serves as an excellent entry point. It distills mathematical concepts to their essentials at the outset and introduces many core ideas of the discipline. For newcomers, the book's aim to teach how to tackle and resolve computational challenges is particularly beneficial. Understanding the tools at your disposal and the limitations you may encounter is crucial. This book will help you grasp what it means to be a computer scientist and how to approach problems in the field.
Grokking Algorithms
Grokking Algorithms by Aditya Bhargava is another friendly introduction to core computer science concepts. This book focuses on data structures and algorithms, making it a vital resource before transitioning to more traditional textbook explanations. I found this book invaluable while studying data structures and algorithms in college. The illustrations simplify complex ideas, making them easy to understand and apply to your own situations. It effectively introduces concepts like time complexity, growth rates, and various notations used in measuring algorithm performance, which is incredibly useful for software engineering interviews. Mastering data structures and algorithms is fundamental to the discipline, as these topics dominate interview questions and your entire academic journey in computer science.
OpenStax Math Textbooks
Before proceeding further, I want to mention the OpenStax math textbook library, which is completely free to access and download. To delve into computer science concepts effectively, you should have a solid understanding of algebra, trigonometry, and introductory calculus. However, this doesn’t mean you must have mastered calculus before diving in. I tackled data structures and algorithms while simultaneously studying college algebra (though I wouldn’t recommend that). The point is that while tackling advanced computer science topics is feasible even without perfect math skills, if you're keen on computational theory or pursuing graduate studies, you’ll need a strong foundation in calculus. Most graduate programs expect knowledge of multivariable calculus, discrete mathematics, and linear algebra.
Introduction to Algorithms
Introduction to Algorithms by Thomas Cormen and colleagues is a staple in computer science education. You may have seen this book recommended elsewhere, but why is it on my list? If you follow the order of this list, you will have built enough foundational knowledge from the previous books to confidently tackle its content. This book offers rigorous academic explanations of computer science topics, presenting them in both computational and mathematical contexts. While Computer Science Distilled introduces time complexity concepts, and Grokking Algorithms visually breaks them down, Introduction to Algorithms dives deeply into complex ideas. Expect to encounter in-depth discussions on time complexity, sorting algorithms, notations, and asymptotic analysis. Following the order of this list should prepare you for this book. It’s essential for budding computer scientists, and I find myself referencing it more often than I expected.
Database Design for Mere Mortals
Michael J. Hernandez’s Database Design for Mere Mortals ranks fifth on my list because understanding databases is crucial for any computer scientist. If you're venturing into fields like data analytics, web development, software engineering, or data science, mastering relational database design and SQL is essential. This book serves as both a guide to relational database design and an SQL reference. After all, creating a database is pointless if you can’t interact with or retrieve data from it. SQL is a language you’ll likely utilize frequently in tech roles. A solid grasp of SQL and database structures is fundamental for computer scientists, paving the way for full-stack web application development and essential for thriving in many work environments.
Pragmatic Thinking & Learning
Andy Hunt’s Pragmatic Thinking & Learning makes the list for its focus on establishing effective learning habits for computer scientists. Like many fields, computer science demands continuous lifelong learning and skill development. Technology evolves, new programming languages emerge, tools are created, and paradigms shift. This book equips you with the mindset and tools necessary for practical, sustained learning. Managing and effectively assimilating new knowledge will become an invaluable skill for you. This book is beneficial for programmers of all backgrounds.
Essentials of Computer Organization and Architecture
The Essentials of Computer Organization and Architecture by Linda Null and Julia Lobur serves as the textbook counterpart to Code. It balances academic rigor with an enjoyable reading experience. Without overwhelming you with dense academic language, this book expands on the concepts introduced in *Code* and presents them in a digestible academic format. It is the best resource I’ve encountered for grasping the intricate ideas of computer architecture. The way mathematics is presented in this book is inviting and engaging; I never felt daunted or fatigued. If you struggle with traditional textbooks, this may be the first one you read from cover to cover. Whether you're preparing for a computer architecture class or simply reading for pleasure, you’ll be pleasantly surprised by how complex topics can be articulated so simply.
Formal Languages and Automata
An Introduction to Formal Languages and Automata by Peter Linz surprised me when I first picked it up. It is dense with mathematical notation and progresses quickly, assuming familiarity with these concepts. This makes sense, as it covers upper-division coursework on the theory of computation, which computer science students typically encounter after taking calculus, linear algebra, or discrete mathematics. I didn’t realize this before purchasing it, and you should definitely have a grasp of discrete math before diving in. The theory of computation isn’t for everyone, and the book requires a solid understanding of the underlying theories, as it often asks for mathematical proofs. This can be enjoyable for those who appreciate math but daunting for others.
Introduction to the Theory of Computation
Similar to the previous book, Introduction to the Theory of Computation by Michael Sipser presents intense upper-division computer science material. It was one of the most challenging reads for me when I first acquired it. An Introduction to Formal Languages and Automata features more visuals and examples that are easier to digest. Nevertheless, either book is a solid choice if you’re diving into theory. MIT offers a YouTube playlist that serves as excellent supplemental material for any book on computational theory.
That said, Introduction to the Theory of Computation and An Introduction to Formal Languages and Automata are likely not on everyone’s reading list. The truth is, diving into theory isn’t necessary unless you have a genuine interest in it (and if you don’t plan on pursuing a computer science degree). If your motivation for learning formal computer science concepts is for fun, to enhance your skills, or to improve your programming, then theory may just be a hindrance. A word of advice: if self-teaching becomes frustrating or overwhelming, consider revisiting the foundational topics that are causing distress. You might also take a break and explore other areas of interest, which can be refreshing after such fatigue. The aim is not to quit but to seek alternative approaches. Learning computer science can quickly become a test of perseverance if your enthusiasm starts to wane. Make sure to take breaks.
Conclusion
Engaging with all these books will provide a solid grounding in lower-division and some upper-division computer science topics. They lay the groundwork for understanding not only how computers operate but also how programmers consider the physical aspects of a computer and its dynamics when writing code. Adopting this mindset can be challenging initially, but once you familiarize yourself with these concepts, it becomes second nature to question your coding practices. Is this the most efficient way to process this data? How can I substantiate this? That’s the mindset of a computer scientist.
If you’re considering applying to a graduate program in computer science but lack a computer science background, I encourage you to follow my journey. I will soon be sharing articles detailing paths to formal computer science education. Thank you for reading!
-Joe