There are few moments as satisfying in a writer’s life as the thud of a package on the doorstep—especially when that package contains the culmination of years of work, revision, and relentless debugging. I’m thrilled to share that I’ve received the author copies of the second edition of Data Science and Analytics with Python, and I must say: it feels real now.
This new edition has been a labour of love (and no small amount of Pandas wrangling). The first edition found its way into universities, companies, and home offices around the world, and the feedback from readers has been both humbling and inspiring. With this edition, I wanted to keep that practical, hands-on spirit alive while refreshing the content to reflect the evolving landscape of data science.
So, what’s new?
- Modernised Examples: Data science doesn’t stand still, and neither should code examples. I’ve updated them with newer libraries and better practices, with plenty of Jupyter-friendly walkthroughs.
- Expanded Topics: Adding information on Generative AI, and adding more on NLP, machine learning workflows, and practical data strategy—a response to what many of you have asked for.
- Streamlined Explanations: Concepts like dimensionality reduction, pipelines, and deployment are introduced more intuitively, with real-world context in mind.
And yes, the cover still holds up on a coffee table (or a desk strewn with notebooks and cables, if you’re anything like me).
What’s next? I have now turned my attention to reviewing the companion volume, “Advanced Data Science and Analytics with Python”, which will follow the same ethos: approachable, grounded, and deeply practical.
In the meantime, if you get a copy of the new edition, let me know what you think. Tag me, send a photo, or just share your favourite chapter. I’m always curious to see where the book travels and how it helps you shape your own data journey.
Onward!
