Further Reading: Welcome to Computer Science

Computational Thinking

Wing, J. M. (2006). "Computational Thinking." Communications of the ACM, 49(3), 33-35. (Tier 1) The foundational essay that popularized computational thinking as a skill for everyone, not just computer scientists. Short, readable, and still influential twenty years later.
Denning, P. J. & Tedre, M. (2019). Computational Thinking. MIT Press. (Tier 1) A concise, accessible book that traces the history of computational thinking and argues for its place in general education. Good companion to this chapter.

Ford, P. (2015). "What Is Code?" Bloomberg Businessweek. (Tier 1) A legendary 38,000-word article that explains software development to a non-technical audience. Entertaining, detailed, and surprisingly accurate about the culture of programming. Available free online.
Bossavit, L. (2015). The Leprechauns of Software Engineering. Leanpub. (Tier 1) A fascinating book that examines commonly cited "facts" about software development and traces their actual evidence (or lack thereof). Teaches critical thinking about industry claims.

Isaacson, W. (2014). The Innovators: How a Group of Hackers, Geniuses, and Geeks Created the Digital Revolution. Simon & Schuster. (Tier 1) A sweeping narrative history of computing from Ada Lovelace to the modern internet. Great for understanding the human stories behind the technology.
Abbate, J. (2012). Recoding Gender: Women's Changing Participation in Computing. MIT Press. (Tier 1) Explores how computing went from a field with many women (in the 1950s-60s) to one dominated by men, and what that shift reveals about who "belongs" in CS.

Peng, S., Kalliamvakou, E., Cihon, P. & Demirer, M. (2023). "The Impact of AI on Developer Productivity: Evidence from GitHub Copilot." arXiv:2302.06590. (Tier 1) A controlled experiment in which developers with Copilot finished a programming task (writing a small web server) 55.8% faster than those without it. Notice how narrow the task is — a single, well-defined job — and keep that in mind when you read headlines built on studies like this. https://arxiv.org/abs/2302.06590
METR (2025). "Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity." (Tier 1) A randomized trial with the opposite headline: experienced developers working in large codebases they already knew well took 19% longer with AI tools, while believing they had been faster. Read it alongside the Copilot study — together they show why "does AI make programmers faster?" has no one-word answer. https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/

Sweigart, A. (2025). Automate the Boring Stuff with Python, 3rd Edition. No Starch Press. Free to read online at https://automatetheboringstuff.com. (Tier 1) An excellent practical introduction to Python focused on automation. Complements this textbook by emphasizing real-world applications. We'll reference it in later chapters.
Downey, A. (2024). Think Python, 3rd Edition. O'Reilly Media. Free to read online at https://allendowney.github.io/ThinkPython/. (Tier 1) Another openly licensed Python textbook with a stronger emphasis on CS concepts. More concise than this book, good as a second perspective on the same material.