This book, which has been updated with extensive new chapters and material on AI (neural networks, deep learning, and generative AI), is aimed at the data scientist and the machine learning engineer who has some prior (perhaps spotty or ephemeral) exposure to statistics. Illustrations are provided using the R and/or Python programming languages. Some familiarity with those languages is useful, but, with the vibe coding capabilities of modern AI tools, even those with no programming background can benefit from this book.

Two of the authors came to the world of data science from the world of statistics, and have some appreciation of the contribution that statistics can make to the art of data science. At the same time, we are well aware of the limitations of traditional statistics instruction: statistics as a discipline is a century and a half old, and most statistics textbooks and courses are laden with the momentum and inertia of an ocean liner. The methods in the first portion of this book have some connection—historical or methodological—to the discipline of statistics. Neural nets—the underpinning of modern AI—evolved mainly out of computer science and are covered in the latter part of the book.

Data scientists, machine learning practitioners, and software engineers have come to know AI as a powerful coding tool. Vibe coding refers to the ability of AI models to produce code based on specifications (a prompt) written in English or some other natural language. A related concept, agentic coding, is broader, covering autonomous task execution by AI agents within a structured workflow that includes goal and task specification, testing, validation, and oversight. In this book, examples in R and Python are shown, and the initial chapters (the ones that focus on statistics) discuss the use of vibe coding, primarily at the conclusion of the chapters. The latter chapters discuss AI in more depth, and go into more detail on its use for coding.

Why learn about statistics, coding, and the models that underpin AI if large language models (LLMs) can do the work for you? If you can use AI as a quasi-magical tool to do your work, why do you really need to understand the underlying concepts?

• AI can make mistakes, mistakes which may not be readily apparent.
• If used properly in statistics and data science, AI will ask you questions and offer choices that require some knowledge of the underlying statistical concepts to answer.
• If you have some understanding of neural nets and how—based on a statistical and machine learning foundation—they enable deep learning and generative AI, you are in a better position to understand their strengths and weaknesses.
• While AI is very effective at providing “textbook” solutions to well-defined statistical problems, it does not yet have the ability to do the kind of critical thinking and problem solving that is required to work through an ambiguous data science project from end-to-end.