Further Reading — Chapter 40: Building Your Python Business Portfolio

This is the final Further Reading section of the book. Rather than pointing backward to specific skills, it points forward — to where you go from here. Organized by the learning path you want to pursue.

On Building a Professional Portfolio

GitHub's own documentation — docs.github.com — is the authoritative reference for everything GitHub-related. The "Getting Started" section covers everything from creating a repository to writing a good README to using GitHub Actions for automation.

"The Complete Guide to Open Source" by GitHub — A free guide available at opensource.guide covering how to contribute to open source, including how to find projects, understand contribution conventions, and navigate code review.

Real Python: "Python Git and GitHub Introduction" — A practical tutorial at realpython.com covering the git workflow as it applies to Python projects. Good for understanding commit hygiene and repository structure from a Python-specific perspective.

"How to Write a Great README" by Danny Guo — A widely referenced post at dguo.github.io/blog/how-to-make-a-readme that covers the elements of an effective README with concrete examples. Short enough to read in one sitting.

For the Data Engineering Path

"Fundamentals of Data Engineering" by Joe Reis and Matt Housley (O'Reilly, 2022) — The definitive introduction to data engineering as a discipline. Covers the data engineering lifecycle, tooling, and architecture from first principles. Not Python-specific, which is appropriate — data engineering involves many tools and the concepts matter more than the specific implementations.

dbt documentation — docs.getdbt.com — The official documentation for dbt (data build tool) is exceptionally well written and serves as a primary learning resource, not just a reference. The "Getting Started" tutorial takes about two hours and gives you a genuine sense of how dbt transforms analytics workflows.

"Learning Apache Airflow" by Bas Harenslak and Julian de Ruiter (O'Reilly, 2021) — Covers workflow orchestration with Airflow from first principles, with practical examples grounded in real data engineering patterns.

"Designing Data-Intensive Applications" by Martin Kleppmann (O'Reilly, 2017) — The most thorough treatment of how modern data systems work: databases, distributed systems, stream processing, and the tradeoffs involved in each. Not a beginner text, but the most valuable technical reading investment for someone building toward data engineering. Read it slowly.

PySpark documentation and Databricks community edition — For large-scale data processing, Databricks offers a free community edition for learning PySpark. The official PySpark documentation at spark.apache.org/docs/latest/api/python is the reference.

For the Machine Learning Path

"Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron (O'Reilly, 3rd edition, 2022) — The standard introductory text for machine learning with Python. Covers classical ML with scikit-learn in detail before moving into neural networks. Well written, extensively practical, and updated for modern tooling.

scikit-learn documentation — scikit-learn.org/stable — The official documentation includes excellent user guides for every major algorithm, with examples that go beyond the API reference. The "Common pitfalls and recommended practices" section is particularly valuable for practitioners.

"Machine Learning Engineering" by Andriy Burkov (True Positive, 2020) — Focused on the engineering side of ML: how to deploy, monitor, and maintain machine learning systems in production. A necessary counterpart to the modeling-focused resources.

MLflow documentation — mlflow.org/docs/latest — The official documentation for experiment tracking and model management. The "Getting Started" tutorial covers the core workflow in about an hour.

FastAPI documentation — fastapi.tiangolo.com — The official FastAPI documentation is one of the best-written technical documents in the Python ecosystem. If you want to deploy a machine learning model as an API endpoint, start here.

For the Advanced Analytics Path

"Python for Data Analysis" by Wes McKinney (O'Reilly, 3rd edition, 2022) — Written by the creator of pandas. The definitive reference for advanced pandas work: MultiIndex, time series, performance optimization, and the full breadth of the library. An indispensable reference for serious data work.

"Introduction to Statistical Learning" by Gareth James et al. (Springer, 2nd edition, 2021) — Available free at statlearning.com. The accessible introduction to statistical learning theory that complements scikit-learn's practical focus. Covers regression, classification, resampling, regularization, and tree-based methods with strong conceptual grounding.

Statsmodels documentation — statsmodels.org — The official documentation includes detailed tutorials for time series analysis, regression with proper inference, and econometric models. More statistically rigorous than scikit-learn's approach, which makes it complementary rather than competing.

Polars user guide — pola.rs/user-guide — For practitioners who want to understand how Polars differs from pandas and when it is worth switching. The "Coming from pandas" section is directly useful for pandas practitioners.

For the Visualization and BI Path

Plotly documentation — plotly.com/python — The official documentation for Plotly's Python library, with extensive examples for every chart type. The Dash documentation at dash.plotly.com covers building full web applications with Python.

Streamlit documentation — docs.streamlit.io — Clear, well-organized documentation with a gallery of example applications. The "30 Days of Streamlit" challenge at the Streamlit blog is an effective structured learning path.

"Fundamentals of Data Visualization" by Claus O. Wilke (O'Reilly, 2019) — Available free at clauswilke.com/dataviz. A language-agnostic treatment of what makes visualizations effective, misleading, or beautiful. The conceptual foundation that makes you a better communicator with data regardless of which library you use.

Storytelling with Data by Cole Nussbaumer Knaflic (Wiley, 2015) — The accessible, practical guide to presenting data to business audiences. Covers chart selection, annotation, color, and the narrative structure of data presentations. Widely referenced in business and analytics contexts.

For the Software Engineering Path

pytest documentation — docs.pytest.org — The definitive reference for Python testing. The "Get Started" section is brief and immediately practical. The "How-to guides" section covers fixtures, parametrize, and other patterns that make tests maintainable.

"Architecture Patterns with Python" by Harry Percival and Bob Gregory (O'Reilly, 2020) — Available free at cosmicpython.com. Covers how to structure Python applications for testability and maintainability using domain-driven design patterns. Most relevant for practitioners building complex business applications.

"The Pragmatic Programmer" by David Thomas and Andrew Hunt (Addison-Wesley, 20th Anniversary Edition, 2019) — The canonical text on software craftsmanship. Not Python-specific, but the principles apply directly: DRY code, orthogonality, test-driven development, personal responsibility for quality. Required reading for any serious practitioner.

mypy documentation — mypy.readthedocs.io — The official documentation for Python's standard type checker. The "Getting Started" section explains type hints in practice and covers the most useful patterns.

Docker documentation — docs.docker.com — The official documentation, which includes a "Getting Started" guide that explains containerization from first principles. For Python practitioners, the "Dockerize your app" guides for Flask and FastAPI applications are the most directly relevant starting points.

For Learning to Communicate Technical Work

"Technical Writing Fundamentals" from Google — A free technical writing course available at developers.google.com/tech-writing. Covers clarity, precision, and audience-appropriate communication. Practical, short, and immediately applicable.

"Simply Put: A Guide for Creating Plain-Language Health Information" — Despite the title, the plain language principles described at ahrq.gov/health-literacy/simply-put apply universally to communicating technical work to non-expert audiences.

"The Elements of Style" by Strunk and White (Longman, 4th edition, 1999) — Brief, opinionated, and essential. The prose clarity principles apply directly to README writing, documentation, and any communication with non-technical stakeholders.

Community and Ongoing Learning

PyCon US talks — Available at pyvideo.org and on the PyCon YouTube channel. The archive going back to 2011 contains thousands of talks on practical Python applications. Search by topic. Excellent talks on data engineering, testing, type hints, packaging, and business applications exist at all skill levels.

Real Python — realpython.com — Tutorial-first Python learning with strong practical orientation. The learning paths section organizes tutorials by goal, which is more useful than browsing by topic.

Python Weekly — pythonweekly.com — Free weekly newsletter. Subscribe and read the headlines each week. You will absorb the ecosystem's current concerns and directions without having to seek them out.

Talk Python to Me — talkpython.fm — Episode archives cover practitioners across every Python domain. The interviews with data analysts, consultants, and business users who learned Python as their second discipline are particularly relevant to readers of this book.

Books Referenced Throughout This Textbook

For the complete annotated bibliography of all books referenced throughout Python for Business for Beginners, see Appendix: Bibliography.

Every recommendation in this list is for a real, currently available resource. No phantom citations. If something on this list is out of print or unavailable when you look for it, the publisher's or author's website will usually point you to a current equivalent.