Professor Wang Caixias translated work *Probability and Statistics: A Data Science Perspective* from the School of International Economics was published by China Machine Press in March 2022. Spanning over 360,000 words, the book provides a comprehensive exploration of probability and statistics—from probability distributions and expected values to estimation—through a data science lens. It extensively utilizes real-world datasets, with all analyses implemented in R. The book also features practical data science applications, including principal component analysis, mixed models, random graph models, hidden Markov models, linear regression, logistic regression, and neural networks.

This book provides a great introduction to statistics for students majoring in data science. The examples are usually from the application fields of data science, such as hidden Markov model and remote sensing. All models and concepts are not given from formal proof, but explained well with precise mathematical language, which helps readers to get more intuitive understanding.

Probability and Statistics: A Data Science Perspective is designed for readers in data science, computer science, or related fields. The book is structured into four parts: Part 1 (Chapters 1-6) covers probability theory, Monte Carlo simulation, discrete random variables, expected value and variance, discrete parameter distribution families, and continuous probability models. Part 2 (Chapters 7-10) introduces fundamental statistics, including sampling distributions, maximum likelihood estimation, central limit theorems, confidence intervals, and significance testing. Part 3 (Chapters 11-17) explores multivariate analysis, covering multivariate distributions, mixed distributions, principal component analysis, log-linear models, dimensionality reduction, overfitting, and predictive analytics. Part 4 (Appendix) provides an introduction to R programming basics.

Professor Cai Xia Wang obtained her masters and doctoral degrees from Beijing Jiaotong University. From 2015 to 2016, she was dispatched by the Chinese government to study Biomedical Engineering at Johns Hopkins University in the United States. Prior to joining the university in 2016, she conducted quantitative research at the Speech and Language Technology Center of the Institute of Information Technology, Tsinghua University.

Professor Wang teaches undergraduate courses such as Mathematical Analysis, Calculus, and Linear Algebra, and offers the graduate course Financial Data Mining and Analysis, earning widespread acclaim from students. Professor Wang Caixias current research interests include quantitative finance, algorithmic research in data analysis and mining, as well as nonlinear dynamical systems theory and its applications.

Professor Wang has published over ten SCI-indexed papers in international academic journals, including three in top-tier journals of the field, with these works being cited more than 100 times by scholars worldwide.

Professor Wang has organized academic competitions on multiple occasions, with students excelling in the Mathematical Contest in Modeling (MCM) and the National College Students Mathematics Competition (NCSMC).