1st Edition
Applied Statistics with Python Volume I: Introductory Statistics and Regression
Preface
1. Introduction
2. Descriptive Data Analysis
3. Probability
4. Probability Distributions
5. Inferential Statistics and Tests for Proportions
6. Goodness of Fit and Contingency Tables
7. Inference for Means
8. Correlation and Regression
Biography
Leon Kaganovskiy is an Associate Professor at the Mathematics Department of Touro College. He received a M.S. in Theoretical Physics from Kharkov State University, and M.S. and PhD in Applied Mathematics from the University of Michigan. His most recent interest is in a broad field of Applied Statistics, and he has developed new courses in Bio-Statistics with R, Statistics for Actuaries with R, and Business Analytics with R. He teaches Statistics research courses at the Graduate Program in Speech-Language Pathology at Touro College.
"In conclusion, Applied Statistics with Python: Volume I represents a timely and well-executed contribution to the modernization
of statistics education. Its computation-first approach, integration of Python throughout the narrative, and focus on interpretation and model-based reasoning provide students with a solid foundation in both statistical thinking and computational literacy. While the book is not intended as a comprehensive theoretical reference, it fills a crucial niche for applied programs seeking to introduce statistics in a practical, codeoriented context. By blending readable style, intuitive guidance, and contextualized examples, the text empowers students to engage confidently with data and prepares them for advanced study or professional work in data-driven fields."
- Maria Iannario in The American Statistician, March 2026"Overall, the author has done an excellent job presenting foundational statistical concepts for students in an introductory college-level course. The consistent use of Python is a notable strength, and the intuitive, concept-driven approach makes the material accessible to students who are not mathematics or statistics majors but are comfortable with programming. [...] In summary, this is a strong textbook for an introductory statistics course, particularly for the right audience."
- Pradipta Sarkar in Technometrics, April 2026
