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Knowledge and information are critical assets for any industrial enterprise. It enables businesses to differentiate themselves from competitors and compete efficiently and effectively to the best of their abilities. At present, information technology, telecommunications, and manufacturing are merging as the means of production are becoming increasingly autonomous. Advanced manufacturing, or Industry 4.0, is based on three interconnected pillars: (1) Computerized Product Design and Smart Technology; (2) Smart Sensors, Internet of Things, and Data Collectors integrated in Manufacturing Lines; and (3) Analytics, Control Theory, and Data Science. Advanced manufacturing requires analytics and operational capabilities to interface with devices in real time. Software development has become agile in DevOps operations focused on providing continuous delivery, as opposed to the traditional waterfall versioning approach (Kenett et al. 2018a). Moreover, processing and analytic models have evolved in order to provide a high level of flexibility. The emergence of agile processing models enables the same instance of data to support batch analytics, interactive analytics, global messaging, database, and file-based models. The result is an application platform that supports the broadest range of processing and analytic models. The ability to understand data within a context, and assume the right business action, is a source of competitive advantage (Olavsrud 2017; Kenett and Redman 2019; Kang et al. 2021b). This book is about industrial statistics. It reflects many years of experience of the authors in doing research, teaching and applying statistics in science, healthcare, business, defense, and industry domains. The book invokes over 40 case studies and provides comprehensive Python applications. In 2020, there were 10 million developers in the world who code using Python which is considered the fastest-growing programming language. A special Python package, mistat, and additional Python code are available for download at https://gedeck.github.io/mistat-code-solutions/IndustrialStatistics. Everything in the book can be reproduced with mistat. We therefore provide, in this book, an integration of needs, methods, and delivery platform for a large audience and a wide range of applications. Industrial Statistics: A Computer-Based Approach with Python is a companion volume to Modern Statistics: A Computer-Based Approach with Python. Both books include mutual cross-references but are considered as stand-alone publications. Industrial Statistics: A Computer-Based Approach with Python can be used as textbook in a one-semester or two-semester course on industrial statistics. Every chapter includes exercises, data sets, and Python applications. These can be used in regular classroom setups, flipped classroom setups and on-line or hybrid education programs. This book is focused on industrial statistics with chapters on advanced process monitoring methods, computer experiments, and Bayesian reliability. Mod-ern Statistics: A Computer-Based Approach with Python is a foundational text and can be combined with any program requiring data analysis in its curriculum. This, for example, can be courses in data science, industrial statistics, physics, biology, chemistry, economics, psychology, social sciences, or any engineering discipline. Modern Statistics: A Computer-Based Approach with Python includes eight chapters. Chapter 1 is on analyzing variability with descriptive statistics. Chapter 2 is on probability models and distribution functions. Chapter 3 introduces statistical inference and bootstrapping. Chapter 4 is on variability in several dimensions and regression models. Chapter 5 covers sampling for estimation of finite population quantities, a common situation when one wants to infer on a population from a sample. Chapter 6 is dedicated to time series analysis and prediction. Chapters 7 and 8 are about modern data analytic methods. The Python code used in this book and the solutions to the exercises of this book are available from https://gedeck. github.io/mistat-code-solutions/ModernStatistics/. Industrial Statistics: A Computer-Based Approach with Python, this volume, contains 11 chapters: Chap.1—Introduction to Industrial Statistics, Chap.2— Basic Tools and Principles of Process Control, Chap.3—Advanced Methods of Statistical Process Control, Chap.4—Multivariate Statistical Process Control, Chap.5—Classical Design and Analysis of Experiments, Chap.6—Quality by Design, Chap.7—Computer Experiments, Chap.8—Cybermanufacturing and Digital Twins, Chap.9—Reliability Analysis, Chap.10—Bayesian Reliability Estimation and Prediction, and Chap.11—Sampling Plans for Batch and Sequential Inspection. The Python code used in these chapters and the solutions to the exercises of this book are available from https://gedeck.github.io/mistat-code-solutions/IndustrialStatistics/.