1. Introduction to this book

In today’s world, data is generated at an unprecedented pace, and our ability to harness it is changing the way we live, work, and even think. Data science, the interdisciplinary field that blends statistics, computer science, and domain-specific knowledge, empowers us to extract insights from this vast ocean of data. As data science becomes increasingly essential across various industries and sectors, there is a growing need for skilled professionals who can make sense of data and transform it into actionable information. This book is designed to give you a very broad and at the same time a very practical hands-on tour through the full spectrum of data science approaches.

There are numerous data science books, courses, and materials available, catering to different levels of expertise and backgrounds. However, many of these resources assume a strong foundation in computer science, math, or quantitative scientific disciplines. This is because until very recently, such career shifts were the typical path to becoming a data scientist (which also holds for the author of this book). But more and more universities or higher educational programs are starting to aim at the formation of a new generation of data scientists. Students who have only little prior IT-related formation and might not come with a prior scientific degree. This book is for them! It fills the described gap by providing a comprehensive, hands-on introduction to data science for those who are just starting their journey or considering a career in this fascinating domain.

The book was designed to be understandable to new undergraduate students, with only basic Python programming and math skills as requirements. A large part of the topics is used in a broad data science introduction course for 2nd semester data science students. The course is also thought to master students of applied computer science, many of them without prior introduction to data science or machine learning. You don’t need to be an expert in computer science or have a strong background in statistics to grasp the concepts and techniques covered in this book. We hope, however, that this book will also be helpful for people in a career switch, researchers willing to deepen (or broaden) their data science skill set or people in industry feeling the need to move away from the limits in data analysis set by non-scripting software tools.

Throughout the chapters, you’ll find many Python code examples and exercises that will help you develop a deep understanding of data science concepts and techniques. By working through these practical examples, you’ll be able to apply your newly acquired knowledge to real-world situations, making you better equipped to tackle data-driven challenges in your chosen field.

We firmly believe that becoming proficient in data science is within reach for anyone who possesses a combination of intellectual curiosity, a passion for learning, and a knack for logical puzzles or detective work. Additionally, a basic affinity for math and statistics goes a long way.

But enough of the introduction. Let’s dive in and learn how to do the detective work of a data scientist to extract new knowledge from complex data.