Introduction to Data Science : Two-Volume Set

Thoroughly revised and updated, the first book introduces skills that can help the reader tackle real-world data analysis challenges. The second book teaches data science as a way of thinking statistically, not just as a collection of computational tools.

Unlike the first edition, the new edition has been split into two books, which have been brought together in this set. Thoroughly revised and updated, the first book (Introduction to Data Science: Data Wrangling and Visualization with R) introduces skills that can help the reader tackle real-world data analysis challenges. These include R programming, data wrangling with dplyr, data visualization with ggplot2, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation with Quarto and knitr. It includes additional material on data.table, locales, and accessing data through APIs. The book is divided into four parts: R, Data Visualization, Data Wrangling, and Productivity Tools. Each part has several chapters meant to be presented as one lecture and includes dozens of exercises. The second book (Introduction to Data Science: Statistics and Prediction Algorithms Through Case Studies) teaches data science as a way of thinking statistically, not just as a collection of computational tools. Building on the topics covered in Introduction to Data Science: Data Wrangling and Visualization with R, this book is designed for students with some programming experience and basic mathematical maturity, this book builds the foundations of probability, statistical inference, regression, high-dimensional data analysis, and machine learning through real data examples and reproducible R code. It is suitable for one-semester course in advanced data science.