This book is written for both newcomers and experienced practitioners working at the intersection of data science, machine learning, and finance. It is designed to allow readers with no formal prerequisites to enter these fields with confidence, while also providing sufficient depth to be valuable to professionals. Beginning with a gentle introduction to Python, the book gradually progresses to more advanced language features and the mathematical foundations required to understand key models in quantitative finance. Throughout, the emphasis is on developing both conceptual understanding and practical skills. The material strikes a careful balance between the mathematics underpinning modern financial models and the practical considerations of data science and machine learning. Concepts are introduced and reinforced through hands-on case studies based on real financial datasets, enabling readers to gain experience working with realistic data and workflows. The contents of this book have been refined over many years of teaching to students and practitioners with diverse backgrounds at Imperial College London and the Thalesians Intensive Summer School in Artificial Intelligence, and reflects both academic rigor and real-world relevance.