The book Probabilities and Statistics in Data Science concerns "mathematical statistics" (families of distributions, expected values, value estimations, etc.), but it places particular emphasis on the "data science" aspect included in the title:

• With extensive use of data sets from real-life, everyday cases
• With programs written in the R language that support data analysis
• With references to many applications of data science, such as principal component analysis (PCA), mixture distributions, random graph models, hidden Markov models, neural networks, as well as linear and logistic regression
• With appropriate guidance for students, helping them critically examine the "hows" and "whys" of statistics and see the big picture in each case
• With precise formulation of concepts and models from a mathematical perspective, but without adhering to the classic dichotomy of "theorem formulation and proof"

The prerequisite knowledge that is good to have in order to make use of the book is some knowledge of mathematical calculus and matrix algebra, as well as some experience in computer programming.