[{"_1":2,"_64":-5,"_65":-5},"loaderData",{"_3":4,"_33":34,"_62":-5,"_63":-5},"root",{"_5":-5,"_6":-5,"_7":8,"_20":-5,"_21":22,"_23":24,"_27":28},"user","shoppingCartId","shoppingCart",{"_9":10,"_11":12,"_13":14,"_15":16,"_17":18,"_19":12},"count",0,"free",true,"updatedAt","2026-01-10T21:22:44.111Z","paymentGateway","STRIPE","cartItems",[],"loading","toast","locale","en","constants",{"_25":26},"paypalClientId","AX53Cwz8jR6rVVzzZiZNWWXl0dGagi-GZrjG-S3Ua14E5nrKMn7DxDQ730O4R8LusLPp49ALn6aRVaEy","publishers",{"_29":30,"_31":32},"companies",[],"universities",[],"routes/_store+/_readers+/email_author.$slug/route",{"_35":36,"_61":-5},"book",{"_37":38,"_39":40,"_41":42,"_43":44,"_45":46,"_47":48},"id","Z2lkOi8vbGVhbnB1Yi9TdG9yZTo6Qm9va1Byb2R1Y3QvNzQ4Mzc","title","Data Science & Analytics with Python Programming","state","published","contactAuthorMethod","email","aboutText","

Stop Scripting. Start Discovering.

Are you a Python programmer ready to pivot from building applications to deriving intelligence?

If you have mastered the syntax, memory management, and object-oriented principles of Python, you are ready for Volume 10. This book marks the transition from imperative scripting to the vectorized, statistical, and analytical mindset required for modern Data Science.

This is not a recipe book of copy-paste snippets. It is a rigorous blueprint for deconstructing the modern Python Data Science Stack. We strip away the magic to reveal the architectural foundations of NumPy, Pandas, and Scikit-Learn, teaching you not just how to use these libraries, but why they work the way they do.

What You Will Master:

The Jupyter Ecosystem: Move beyond the script. Master kernels, state management, and the art of the reproducible narrative.
Vectorized Thinking: Abandon slow loops. Learn to manipulate memory contiguously using NumPy arrays and broadcasting rules.
Data Wrangling at Scale: Master Pandas to ingest messy data, perform complex \"Split-Apply-Combine\" aggregations, and handle time-series data with precision.
Statistical Storytelling: Transform raw numbers into visual narratives. Build publication-quality charts with Matplotlib and Seaborn, and deploy interactive, web-ready dashboards with Plotly.
Probabilistic Modeling: Quantify risk and uncertainty using Monte Carlo simulations and rigorous statistical distributions.
Production-Grade Machine Learning: Go beyond model.fit(). Build robust, leakage-free Scikit-Learn Pipelines that encapsulate feature engineering, imputation, and scaling for deployment.

Who This Book Is For:

This volume is designed for Software Engineers, Analysts, and Aspiring Data Scientists who are done with \"Hello World\" tutorials. If you want to understand the difference between Supervised and Unsupervised learning, how to optimize a Gradient Boosting Regressor, or how to visualize multi-dimensional relationships, this book is your guide.

Bridge the gap between coding and science. Master the pipeline today. You can read this volume as a standalone.

All the source code is on GitHub.

Check also the other books in this series

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Email the Author