Computational and Inferential Thinking is a book that explores the powerful intersection between computation and statistical inference. Written by Ani Adhikari, John DeNero, David Wagner, two renowned computer scientists from the University of California, Berkeley, this book presents a comprehensive introduction to the principles and techniques of computational and inferential thinking.
The book starts with an overview of the fundamental concepts of statistics and probability theory, explaining how these concepts are used to make decisions and draw conclusions in a wide range of fields, from finance and healthcare to social sciences and engineering. It then delves into the world of computing, introducing readers to the programming languages and tools that are used to perform data analysis and visualization.
Throughout the book, Adhikari and DeNero emphasize the importance of computational thinking in the modern world, where vast amounts of data are generated and processed every day. They show how computer algorithms and statistical models can be used to uncover hidden patterns in data, make predictions about future events, and test hypotheses about the underlying causes of observed phenomena.
The book also covers a range of practical topics, including data cleaning and preprocessing, exploratory data analysis, hypothesis testing, regression analysis, and machine learning. The authors use real-world examples and case studies to illustrate these concepts, helping readers understand how they can be applied in practice.
Overall, Computational and Inferential Thinking is an essential resource for anyone who wants to learn how to think computationally and make sense of data in the 21st century. It is written in a clear and accessible style, making it suitable for students and professionals from a wide range of disciplines, including computer science, statistics, mathematics, and engineering. Whether you are a seasoned data analyst or a beginner just starting out, this book is sure to provide you with valuable insights and practical skills that you can use to tackle the most challenging data problems of our time.