Practical Data Modeling and Machine Learning with Python
Shouke Wei · Zenodo (CERN European Organization for Nuclear Research) · 2026
Data is abundant, but understanding is not. Between raw data and meaningful decisions lies a crucial process: the ability to build, evaluate, and refine models that capture structure in the world. This book, *Practical Data Modeling and Machine Learning with Python*, focuses on that process. It is the second volume in the *Practical Data Science with Python* series. The first book introduced data exploration and visualization—how to observe patterns, clean data, and ask the right questions. This volume moves one step further: from understanding data to **modeling it**, and from intuition to **quantitative reasoning**. Purpose of This Book The central goal of this book is not simply to present algorithms, but to develop a coherent approach to **data modeling**. In practice, modeling is not a single step. It is a system: defining a problem clearly preparing data carefully selecting appropriate models evaluating performance rigorously refining and improving results This book follows that system. It integrates statistical modeling and modern machine learning into a unified workflow, emphasizing both principles and practical implementation. What This Book Covers This book is organized into six parts, each corresponding to a key stage in the data modeling and machine learning workflow. Part I — Foundations of Data Modeling introduces the fundamental concepts of data modeling and analytical thinking. It covers the practical setup of a Python environment and the essential steps of data preparation and feature engineering, establishing a solid foundation for all subsequent work. **Part II — Statistical Modeling Foundations** provides the necessary statistical background for modeling. Topics such as probability distributions, estimation, and hypothesis testing are presented with a focus on interpretation and practical relevance. **Part III — Statistical Modeling Techniques** develops core modeling approaches, including linear regression, regularization, and generalized linear models. These methods form the bridge between classical statistics and modern machine learning. **Part IV — Foundations of Machine Learning** introduces the principles that govern machine learning systems, including training and validation strategies, the bias–variance tradeoff, and the role of cross-validation and preprocessing pipelines in building reliable models. **Part V — Core Machine Learning Models** presents practical machine learning methods, including classification models, regression techniques, and ensemble approaches. Emphasis is placed on understanding model behavior and comparing different methods in realistic settings. **Part VI — Model Evaluation and Optimization** focuses on assessing and improving models. It covers performance metrics, validation strategies, hyperparameter tuning, and model interpretation techniques, providing a complete framework for building robust and trustworthy models. Together, these parts form a coherent progression from data preparation to model evaluation and optimization, reflecting the full lifecycle of data-driven modeling.