Further Reading — Chapter 1: Why Python? The Business Case for Coding

Curation note: Resources listed here have been selected for business professionals, not computer science students. Priority given to accessible, practical, and current materials.

Books

"Automate the Boring Stuff with Python" by Al Sweigart The definitive practical Python guide for non-programmers. Available free online at automatetheboringstuff.com. Sweigart focuses almost entirely on real-world automation tasks rather than computer science theory. Strongly recommended as a companion to this book — his examples complement the business-focused framing here.

"Python for Data Analysis" by Wes McKinney McKinney created pandas. This is the authoritative pandas reference, written by the person who built it. More technical than this textbook, but invaluable as a reference once you're comfortable with the concepts in Part 2.

"Data Science for Business" by Foster Provost and Tom Fawcett Doesn't teach Python, but teaches the business logic of data — what questions to ask, how to evaluate models, how to communicate findings. Excellent conceptual companion to the technical content of this book.

"The Pragmatic Programmer" by David Thomas and Andrew Hunt A classic on how professional programmers think and work. Not Python-specific, but the principles apply directly. Chapter 39 of this textbook draws heavily on the thinking in this book.

Online Resources

Python.org Official Tutorial python.org/doc/ The official Python tutorial is well-written and free. More technical than what you need for Chapter 1, but excellent reference for Chapters 3–8 concepts.

Real Python (realpython.com) High-quality tutorials specifically aimed at practical Python use cases. Their beginner and intermediate content is excellent. Particularly strong on data science, web development, and automation topics.

Towards Data Science (towardsdatascience.com) Medium publication focused on data science and Python. Variable quality (it's community-written), but many excellent practical tutorials on business use cases.

Kaggle Learn (kaggle.com/learn) Free, interactive Python and data science courses from the competitive data science platform. Their Python intro and pandas courses are particularly well-structured.

Research and Reports

Stack Overflow Developer Survey stackoverflow.com/insights/survey/ Annual survey of professional developers worldwide. Useful for understanding Python's adoption trends, the types of work it's used for, and how business professionals use it compared to traditional engineers.

McKinsey Global Institute: "The age of analytics: Competing in a data-driven world" mckinsey.com McKinsey's research on how data capabilities translate to competitive advantage. Provides business context for why Python skills matter strategically.

"The Economic Value of Skills" — NBER Working Paper Research on how technical skills translate to compensation premiums. Useful context for the "career capital" argument in Section 1.6.

Podcasts

Talk Python To Me talkpython.fm Long-running podcast featuring interviews with Python practitioners across many domains — data science, automation, web development, business. Excellent for understanding the breadth of what Python is used for in real organizations.

Lex Fridman Podcast (selected episodes) Episodes with Guido van Rossum (Python creator) provide fascinating historical context on the language's design decisions.

The "Why Python" Debate — Counterpoint Reading

For a more critical perspective on Python's limitations and when it's not the right tool:

"The Two Cultures of Computing" — Various Essays exploring the divide between statistical computing (R culture) and general programming (Python culture). Understanding this debate helps you make better tool choices.

"Excel Never Dies" — Various essays A genre of writing arguing that Excel's dominance is not a failure of the status quo but reflects genuine, irreplaceable strengths. Worth reading to sharpen your thinking about when not to use Python.

Getting Started Immediately

If you want to start before Chapter 2's environment setup: - Python.org/downloads — Download Python directly - Google Colab (colab.research.google.com) — Run Python in your browser with no installation - Replit (replit.com) — Another browser-based Python environment, good for quick experiments

All links were verified as of the book's publication date. For the most current URLs, search for the title/name directly.