Data Science in Practice is the ideal introduction to Data Science. With or without math skills, here, you get the all-round view that you need for your projects. This book describes how to properly question data, in order to unearth the treasure that data can be. You will get to know the relevant analysis methods, and will be introduced to the programming language R, which is ideally suited for data analysis. Associated tools like notebooks that make Data Science programming easily accessible are included in this introduction. Because technology alone is not enough, this book also deals with problems in project implementation, illuminates various fields of application, and does not forget to address ethical aspects. Data Science in Practice includes many examples, notes on errors, decision-making aids, and other practical tips. This book is ideal as a complementary text for university students, and is a useful learning tool for those moving into more data-related roles. This English version has been updated to include the latest developments such as Quarto and ChatGPT. The rise of ChatGPT & Co raises voices as to whether Data Science is still necessary at all when Microsoft integrates AI into its Ofce product. In teaching practice, I have realized that the introduction to R is a lot faster than the introduction to Python, enabling me to get to the Data Science topics much faster.