Includes bibliographical references and index.

R and the tidyverse -- Reading in data locally and from the Web -- Cleaning and wrangling data -- Effective data visualization -- Classification I : training & predicting -- Classification II : evaluation & tuning -- Regression I : K-nearest neighbors -- Regression II : linear regression -- Clustering -- Statistical inference -- Combining code and text rwith Jupyter -- Collaboration with version control -- Setting up your computer.

"Data Science: An Introduction focuses on using the R programming language in Jupyter notebooks to perform basic data manipulation and cleaning, create effective visualizations, and extract insights from data using supervised predictive models. Based on sound educational research and active learning principles, the book uses a modern approach to the R programming language and accompanying sheets for self-directed learning this book will leave students well-prepared for data science projects. Data Science: An Introduction focuses on workflows and communication strategies that are clear, reproducible, and shareable. Aimed at first year undergraduates with only minimal prior knowledge of mathematics and programming this book is suitable for students across many disciplines. All source code is available online as a GitHub repository, demonstrating the use of good reproducible and clear project workflows and is also accompanied by autograded Jupyter worksheets, providing the reader with guided interactive instruction"--