This textbook provides an easy-to-understand introduction to the mathematical concepts and algorithms at the foundation of data science. It covers essential parts of data organization, descriptive and inferential statistics, probability theory, and machine learning. These topics are presented in a clear and mathematical sound way to help readers gain a deep and fundamental understanding. Numerous application examples based on real data are included. The book is well-suited for lecturers and students at technical universities, and offers a good introduction and overview for people who are new to the subject. Basic mathematical knowledge of calculus and linear algebra is required.

Matthias Plaue is a versatile researcher with a background in mathematical physics. He has explored diverse domains, spanning from relativity theory to pedestrian dynamics. As a data scientist, he develops algorithms for data analysis and artificial intelligence, tailored to support strategic decision-making. In addition to his professional pursuits, he has devoted considerable time to mentoring students, imparting a deep understanding of mathematics and its practical application in tackling complex problems across the fields of science, technology, and engineering.