Full Description
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.
Contents
Preface.- Part I Basics.- 1 Elements of data organization.- 2 Descriptive statistics.- Part II Stochastics.- 3 Probability theory.- 4 Inferential statistics.- 5 Multivariate statistics.- Part III Machine learning.- 6 Supervised machine learning.- 7 Unsupervised machine learning.- 8 Applications of machine learning.- Appendix.- A Exercises with answers.- B Mathematical preliminaries.- Supplementary literature.- Index.
