Books on Data Science Welcome to my curated bookshelf — a living, evolving list of data science books that have helped shape my understanding of the field and continue to influence how I think and work. Whether you’re starting out in data science or you’re deep into AI/ML systems, I hope this page helps you discover something new, insightful, or simply worth revisiting. ⚠️ Disclaimer This is a personal and opinionated list. I’ve included only books that I’ve personally read, referred to, and found useful in my learning or professional journey. It is not meant to be an exhaustive or universally definitive list of the best books in data science. 🧠 How I Learn My learning process is multi-layered. I absorb knowledge by doing, reading, watching videos, and even from micro-learning sources like tweets and blog snippets. Everyone has their own way of learning, but for me, books offer something unique. They provide structured thinking, deep context, and long-term clarity that’s often missing in fast, fragmented content. While I may not rely solely on books for execution, I always turn to them when I want to understand the “why” behind the “how.” Books are where I go to slow down, reflect, and see the bigger picture. List of books Approaching (Almost) Any Machine Learning Problem by Abhishek Thakur Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications by Chip Huyen Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps by Lak Foundations of Data Science by Blum, Hopcroft and Kannan Designing Data Intensive Applications by Martin Kleppmann 5. The Kubernetes Book by Nigel Poulton 6. The Manager’s Path by Camille Fournier 7. Strengthening Deep Neural Networks by Katy Warr 8. Learn Python The Hard Way by Zed Shaw 9. The Elements of Statistical Learning by Hastie, Tibshirani and Friedman How to best read books my recommendations – to be updated