Introduction to Probability and Statistics for Data Science

'This book serves as an excellent resource for students with diverse backgrounds, offering a thorough exploration of fundamental topics in statistics. The clear explanation of concepts, methods, and theory, coupled with an abundance of practical examples, provides a solid foundation to help students understand statistical principles and bridge the gap between theory and application. This book offers invaluable insights and guidance for anyone seeking to master the principles of statistics. I highly recommend adopting this book for my future statistics class.' Haijun Gong, Saint Louis University
'Professors Rigdon, Fricker and Montgomery have put together an impressive volume that covers not only basic probability and basic statistics, but also includes extensions in a number of directions, all of which have immediate relevance to the work of practitioners in quantitative fields. Suffused with common sense and insights about real data and problems, it is both approachable and precise. I'm excited about the inclusion of material on power and on multiple testing, both of which will help users become smarter about what their analyses can do, and I applaud their omission of too much theory. I also appreciate their use of R and of real data. This would be an excellent text for undergraduate or graduate-level data analysts.' Sam Buttrey, Naval Postgraduate School (NPS)
'This is a comprehensive and rich book that extends foundational concepts in statistics and probability in easily accessible form into data science as an integrated discipline. The reader applies and validates theoretical concepts in R and connects results from R back to the theory across many methods: from descriptive statistics to Bayesian models, time series, generalized linear models and more. Thoroughly enjoyable!' Oliver Schabenberger, Virginia Tech Academy of Data Science