Writing a second edition of a technical book is a little like rebooting a long-running sci-fi series. You don’t want to erase the canon, but you do want better special effects, sharper dialogue, and fewer plot holes. With that spirit very much in mind, I’m delighted to say that the second edition of Advanced Data Science and Analytics with Python is now very much a thing: real, tangible, and caffeinated into existence.
This new edition follows closely on the heels of the updated companion volume, Data Science and Analytics with Python (2nd ed.), and together they form a slightly opinionated but well-armed duo. Think Luke and Leia, but with pandas and NumPy instead of lightsabers. (Although, to be fair, NumPy broadcasting can feel like the Force when it works.)
Since the first edition, a lot has happened. Transformers are no longer robots in disguise; they’re the backbone of modern AI. Generative AI has gone from research curiosity to dinner-table conversation. Reinforcement learning has escaped the lab and is now quietly optimising things that actually make money. In short: the map changed, so the field guide had to as well.
This edition responds directly to that shift. The deep learning chapter has been split into two — not out of indulgence, but necessity — making room for reinforcement learning, GANs, transformers, and large language models, all without sacrificing the practical, hands-on ethos that the book was built on. No ivory towers. No mystical incantations. Just working code, clear ideas, and a healthy suspicion of anything claiming to be “fully autonomous”.
The foundations remain reassuringly solid. Python is still our language of choice, and stalwarts like pandas, NumPy, scikit-learn, SciPy, and friends continue to do the heavy lifting. That said, the ecosystem has matured, and the book has matured with it.
All code examples have been updated for modern Python (3.12+), and the libraries reflect current, real-world versions — the ones you’re actually likely to encounter outside a museum of deprecated APIs. The result is a book that doesn’t just explain ideas, but does so in a way that fits naturally into contemporary workflows.
If you’ve read the first edition, the tone will feel familiar. The book remains conversational, practical, and lightly dusted with sci-fi, pop culture, and the occasional Monty Python reference. Margin notes still lurk at the edges, code still lives in boxes, and the Jackalope (curious, adaptable, and faintly mythical) still serves as our official mascot.
This is not a cookbook. It’s not a manual. It’s a field guide for practitioners who are already moving and want to move further. The chapters remain modular and self-contained, ranging from time series and NLP to network analysis, deep learning, generative AI, and the ever-thorny question of deployment: how models escape the notebook and survive contact with users.
What’s Coming Next
In the coming weeks, I’ll be publishing a series of follow-up posts, each diving into what’s new (and what’s evolved) in individual chapters: from modern NLP and vector search, through graph embeddings and reinforcement learning, to deploying models as real data products, including on-device AI using Apple’s Foundation Models.
Think of it as a guided tour of the upgrade: what changed, why it matters, and how you can put it to work without summoning ancient spirits or breaking production.
Until then: may your models generalise well, your deployments behave themselves, and your curiosity remain stubbornly unquenchable. 🚀