The textbook provides students with tools they need to analyze complex data using methods from Data Science, Machine Learning and Artificial Intelligence. The authors include both the presentation of methods along with applications using the programming language R, which is the gold standard for analyzing data. The authors cover all three main components of Data Science: Computer Science; mathematics and statistics; and domain knowledge. The book presents methods and implementations in R side-by-side, allowing the immediate practical application of the learning concepts. Furthermore, this teaches computational thinking in a natural way. The book includes exercises, case studies, Q&A and examples. Regarding useful programming languages, R and Python are very popular today. However, while both provide similar capabilities, there are differences in certain situations. In this book, we prefer R over Python due to its statistical origin. In fact, R was developed to provide a “statistical programming language.” We will see the benefits of this when discussing hypothesis testing (Chap. 10), resampling methods (Chap. 4), and linear regression (Chap. 11), where R provides excellent functionalities. Although this book does not provide an introduction to programming and mathematics, it presents examples in R for the methods from Machine Learning, Artificial Intelligence, and statistics.