Statistical Inference via Data Science

Through apt use of analogies, hands-on exercises, and abundant opportunities to get coding, this book delivers on its promise to give a reader without a background in statistics or programming the tools necessary for understanding and conducting real-world statistical inference and data analysis. With an emphasis on learning new concepts first by hand, before turning to the code, it would make a particularly useful classroom companion. However, the learning checks provided throughout also make it a great guide for self-study. Students and teachers alike will benefit from this thoughtful introduction, as it addresses even the smallest of details that can trip beginners up, and keep them from getting to the more fruitful parts of data analysis. - Mara Averick, Developer Advocate, RStudio, Inc. This is a comprehensive, modern resource for teaching and learning data science. ModernDive couples the introduction of core statistical concepts directly with learning how to apply data science methods to realistic data sets using the R programming language. The pedagogical approach of ModernDive is thoughtful and highly effective. The text engages learners early with tangible and practical concepts, such as creating data visualizations, that enable students to see early returns on their investment in learning R. The authors have created a guide to learning data science that increases students' engagement and enthusiasm, while simultaneously providing students with the depth of understanding needed to conduct meaningful and reproducible data analyses. ModernDive is my go-to resource for teaching data science. I use it in all of my courses and workshops and I have found it to be the most effective and comprehensive introduction to data science in R available. - Rich Majerus, Queens University of Charlotte With its emphasis on visualization, real world data, and simulation, along with clear instructions about how to work with R and the Tidyverse, ModernDive is the most accessible and student-friendly statistics textbook I have taught from. The book's early chapters on data wrangling and visualization provide students with hands-on experience with real data and get them excited about making beautiful and informative figures with modern statistical tools like R and the Tidyverse. Where the book especially shines is its simulation-based approach to modeling, confidence intervals, and hypothesis testing. Instead of teaching a complicated flowchart with dozens of types of statistical tests, the book is instead centered around linear modeling and simulation. The chapters on hypothesis testing use simulation to teach about p-values, an approach that students find eminently intuitive. Overall, ModernDive is a phenomenal modern introduction to statistical inference-it is an essential book for any statistics instructor! -Dr. Andrew Heiss, Andrew Young School of Policy Studies, Georgia State University The monograph belongs to the The R series, and it can serve as a convenient way for learning data science and statistics simultaneously with the R language. The textbook consists of four parts, eleven chapters, and each chapter contains sections and subsections. In Preface, the authors describe the book structure and illustrate it with a pipeline going from importing data to making its tidy version, which is applied in a loop of transforming-modeling-visualizing, and finally is used for communication, or interpretation and reporting of the modeling results...The monograph supplies multiple links to the websites of the R packages and related statistical methods, and the online version of the book with all the codes and outputs is available at moderndive.com. The textbook presents to students and researchers a very useful introduction to the data science and contemporary R programing, with numerous examples of R implementation for solving various problems of statistical estimation and inference. - Stan Lipovetsky, Technometrics, Vol 62 The new ModernDive (Statistical Inference via Data Science) textbook is simply wonderful! It uses accessible language to introduce the topics of data science and statistics, as well as an intuitive simulation-based inference first approach. Importantly, it does not stop there. It also places great emphasis on how to do all of this in the R programming language! True to the book's name, the R code taught and demonstrated in the book uses a modern, tidy approach for data wrangling, visualization and statistics. I have used it successfully in an introductory statistics setting at both the undergraduate-level and the professional Master's level. Furthermore, I would choose to do this again. - Tiffany Timbers, University of British Columbia With the help of visualization, the authors give examples of identifying outliers and identifying relationships between continuous numerical data. Based on this, we can conclude that the authors very well describe one of the steps of data analysis - pre-processing. This step is important because it is a main milestone in the identification of the relationship between variables in the data...The authors also provide a detailed review of the main methods of presenting the classical results based on linear models. This part is very important in the preparation of articles or books and greatly simplifies the work on the preparation. - Igor Malyk, ISCB News, December 2020