Multivariate Statistics and Machine Learning: An Introduction to Applied Data Science Using R and Python

Multivariate Statistics and Machine Learning is a hands-on textbook providing an in-depth guide to multivariate statistics and select machine learning topics using R and Python software.

The book offers a theoretical orientation to the concepts required to introduce or review statistical and machine learning topics, and in addition to teaching the techniques, instructs readers on how to perform, implement, and interpret code and analyses in R and Python in multivariate, data science, and machine learning domains. For readers wishing for additional theory, numerous references throughout the textbook are provided where deeper and less “hands on” works can be pursued.

With its unique breadth of topics covering a wide range of modern quantitative techniques, user-friendliness, and quality of expository writing, Multivariate Statistics and Machine Learning will serve as a key and unifying introductory textbook for students in the social, natural, statistical, and computational sciences for years to come.

Daniel J. Denis, Ph.D., is Professor of Quantitative Psychology at the University of Montana, U.S.A, where he has taught applied statistics courses since 2004. He is author of Applied Univariate, Bivariate, and Multivariate Statistics and Applied Univariate, Bivariate, and Multivariate Statistics Using Python.