A Programmer's Guide to Computer Science: A virtual degree for the self-taught developer: Vol. 1

Book Details

Title	A Programmer's Guide to Computer Science: A Virtual Degree for the Self-Taught Developer, Vol. 1
Author(s)	William M. Springer II
Publisher	Jaxson Media
Year	2019
Edition	First Edition
Language	English
Pages	178
ISBN	9781951204006
Genre	Computer Science, Education, Programming
Series	A Programmer's Guide to Computer Science
Size	2.89 MB
Extension	PDF

Summary

A Programmer's Guide to Computer Science: A Virtual Degree for the Self-Taught Developer, Volume 1 is a focused, no-nonsense introduction to the theoretical underpinnings of computer science, written specifically for people who already know how to code but lack formal academic training in the field. Published by Jaxson Media in 2019, the book is authored by William M. Springer II, a senior software developer holding a PhD in computer science. Springer wrote the book after years of teaching a theoretical foundations course and observing that existing textbooks consistently failed to explain concepts in ways that struggling students could grasp. The result is a guide that prioritizes clarity and directness over exhaustive coverage, distilling the most interview-relevant and practically useful topics from a four-year computer science curriculum into a single accessible volume.

The book is organized into five parts that progress logically from foundational concepts to more advanced theoretical material. Part One addresses computer science fundamentals, covering asymptotic runtime analysis and essential data structures. Part Two is dedicated to graphs and graph algorithms, exploring graph theory, data structures on graphs, well-known algorithms, and common graph classes. Part Three covers non-graph algorithms with a focus on sorting. Part Four introduces problem-solving techniques, including brute force approaches, dynamic programming, and greedy algorithms. Part Five tackles complexity theory, with chapters on understanding complexity, languages and state machines, and Turing machines. This structure reflects the most commonly referenced topics in technical interviews and day-to-day engineering discussions, ensuring that readers gain the vocabulary and conceptual framework needed to participate confidently in professional settings.

What distinguishes this guide from traditional computer science textbooks is its deliberate brevity and its orientation toward the self-taught practitioner. Springer assumes that readers are already competent programmers and therefore skips introductory programming concepts entirely. Instead, the book delivers facts, diagrams, light mathematical notation, and small amounts of pseudocode to convey each topic efficiently. Reviewers have described it as a "light textbook" that is dry but information-dense, best consumed in small, consistent increments rather than read cover-to-cover in a single sitting. The chapters are designed to serve as both a structured learning path for newcomers to computer science theory and a quick refresher for experienced developers who want to revisit concepts they may have forgotten or never fully understood.

The book is intended for self-taught programmers, graduates of coding bootcamps, and developers who have learned to build software through practice but feel out of place when conversations turn to asymptotic bounds, algorithm design, or complexity classes. It is particularly valuable for those preparing for technical interviews at companies that emphasize computer science fundamentals, as well as for developers who want to fill gaps in their knowledge without committing to a full degree program. While the content is not comprehensive enough to replace a formal education, it serves as an effective roadmap and reference that points readers toward deeper study when needed. The author's background as both a PhD holder and a working senior developer lends the material a practical credibility that purely academic texts often lack.

A Programmer's Guide to Computer Science has been well received within the self-taught developer community, with reviewers praising its ability to condense a large body of theoretical material into a manageable length. The book spawned a second volume published in 2020, expanding on additional topics for readers who found the first volume valuable. Its reputation rests not on being entertaining or engaging in a narrative sense, but on being accurate, direct, and respectful of the reader's time. For developers who have ever felt intimidated by computer science jargon or overlooked for opportunities because of a missing degree, this book offers a practical and efficient path toward closing that gap and building the confidence to engage with the theoretical side of software development.

Key Features

• The book is written specifically for self-taught programmers and bootcamp graduates who already know how to code but lack formal computer science training.
• It condenses the most frequently referenced topics from a four-year computer science degree into a concise volume of approximately 178 pages.
• Part One covers asymptotic runtime analysis and essential data structures, providing the foundation for understanding algorithmic efficiency.
• Part Two is dedicated to graph theory and graph algorithms, including well-known algorithms and common graph classes.
• Part Three addresses non-graph algorithms with a focused chapter on sorting algorithms.
• Part Four introduces problem-solving techniques including brute force, dynamic programming, and greedy algorithms.
• Part Five covers complexity theory, with chapters on languages, state machines, and Turing machines.
• The author holds a PhD in computer science and works as a senior developer at a major software company, lending the material both academic rigor and practical relevance.
• The book is designed to serve as both a structured learning guide for newcomers and a quick refresher for experienced developers.
• It is particularly useful for technical interview preparation, covering the theoretical concepts most commonly tested by employers.
• The writing is deliberately concise and textbook-like, prioritizing accuracy and efficiency over narrative engagement.
• Each chapter includes diagrams and minimal pseudocode to illustrate concepts without overwhelming readers with implementation details.
• The book has spawned a second volume, indicating its success in addressing a genuine need within the developer community.
• It is suitable for developers at any career stage who want to fill gaps in their theoretical knowledge without pursuing a formal degree.

About Author

William M. Springer II holds a PhD in computer science and works as a senior software developer at a major technology company. His interest in computer science theory was sparked during his undergraduate studies when he took a course on the theoretical foundations of computer science, which inspired him to pursue graduate school. During his PhD program, he served as a teaching assistant for that same course for three years, an experience that gave him firsthand insight into the concepts students consistently struggled with and the ways in which existing textbooks failed to address those difficulties. This experience planted the seed for what would eventually become the Programmer's Guide series.

After completing his PhD, Springer began giving talks at programming conferences, where he observed a significant demand for material that explained computer science concepts to people who already knew how to code but had never studied the theory behind it. This realization led directly to the creation of A Programmer's Guide to Computer Science, a book designed to bridge the gap between practical programming skills and theoretical knowledge. Beyond his writing and software development work, Springer has taught middle school math and science, edited a textbook, and written for magazines. He maintains a blog at whatwilliamsaid.com and enjoys photography, board games, and science fiction in his spare time.

Related Books

• Introduction to Algorithms — Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein
• The Algorithm Design Manual — Steven S. Skiena
• Cracking the Coding Interview — Gayle Laakmann McDowell
• Grokking Algorithms — Aditya Bhargava
• Computer Science Distilled — Wladston Ferreira Filho
• Elements of Programming Interviews — Adnan Aziz, Tsung-Hsien Lee, and Amit Prakash
• Structure and Interpretation of Computer Programs — Harold Abelson and Gerald Jay Sussman
• Code: The Hidden Language of Computer Hardware and Software — Charles Petzold

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FAQ

Q : Who is the intended audience for A Programmer's Guide to Computer Science?

R : The book is written specifically for self-taught programmers, bootcamp graduates, and developers who already know how to code but lack formal computer science training. It is designed to fill the theoretical gaps that often become apparent during technical interviews, professional discussions, or when working alongside colleagues with computer science degrees. The author assumes readers are already competent programmers and skips introductory programming concepts entirely, focusing instead on the theoretical material that self-taught developers are most likely to have missed.

Q : What topics are covered in Volume 1?

R : Volume 1 covers five main areas: computer science fundamentals including asymptotic runtime and data structures; graph theory and graph algorithms; non-graph algorithms with a focus on sorting; problem-solving techniques including brute force, dynamic programming, and greedy algorithms; and complexity theory including languages, state machines, and Turing machines. The table of contents reflects the topics most frequently referenced in technical interviews and professional engineering discussions, ensuring that readers gain the vocabulary and conceptual framework needed to participate confidently in such settings.

Q : Is this book a replacement for a computer science degree?

R : The book is not intended to be a comprehensive replacement for a four-year computer science degree. It is a concise guide that covers the most commonly referenced topics and serves as a roadmap for self-directed study. Reviewers have noted that it is not comprehensive by design, but rather comprehensive enough to guide readers on their journey and to serve as a reference while they explore other, more detailed resources. The author's goal is to provide the tools needed to hold one's own in conversations with computer science graduates, not to replicate the full depth and breadth of a formal education.

Q : How long does it take to read this book?

R : The book is approximately 178 pages, and reviewers have suggested that reading about 10 pages per day would allow a reader to finish it in roughly a month. The material is dense and textbook-like, so it is best consumed in small, consistent increments with time for reflection and practice. It is not designed to be read cover-to-cover in a single sitting, but rather to be studied carefully and revisited as a reference. For experienced developers using it as a refresher, a faster pace may be appropriate for familiar material.

Q : Does the book include code examples or exercises?

R : The book includes diagrams and small amounts of pseudocode to illustrate concepts, but it is not a hands-on programming workbook. It focuses on explaining theoretical concepts clearly rather than providing extensive implementation exercises. Readers who want to practice implementing the algorithms and data structures discussed should supplement the book with coding practice on platforms like LeetCode or with more implementation-focused resources. The book's strength lies in building conceptual understanding, which can then be applied through practice.

Q : Is there a Volume 2, and do I need to read them in order?

R : Yes, a second volume was published in 2020, expanding on additional topics beyond those covered in Volume 1. While reading them in order may provide a more structured progression, each volume is designed to be usable independently depending on the reader's existing knowledge and goals. Volume 1 covers the foundational topics most commonly referenced in interviews and professional discussions, making it a natural starting point for most readers. Those who find Volume 1 valuable and want to deepen their understanding can proceed to Volume 2.

Q : What makes this book different from other computer science introductions?

R : The key difference is the author's specific target audience and purpose. Springer wrote the book after years of teaching a theoretical foundations course and observing that existing textbooks consistently failed to explain concepts in ways that struggling students could understand. The book is deliberately concise, assumes prior programming knowledge, and focuses on the topics most relevant to self-taught developers and interview preparation. It is not an academic textbook in the traditional sense, but rather a practical guide written by someone who understands both the academic material and the needs of working developers without formal training.

Q : Is the book suitable for beginners with no programming experience?

R : No, the book assumes that readers already know how to program and are comfortable with at least one programming language. It does not teach programming fundamentals, syntax, or basic problem-solving with code. Readers who are completely new to programming should first develop practical coding skills through other resources before attempting this book. The guide is designed for those who have already crossed the threshold of being able to build working software and now want to understand the theoretical principles that underpin their craft.