1. R and the tidyverse, 2. Reading in data locally and from the web, 3. Cleaning and wrangling data, 4. Effective data visualization, 5. Classification I: training & predicting, 6. Classification II: evaluation & tuning, 7. Regression I: K-nearest neighbors, 8. Regression II: linear regression, 9. Clustering, 10. Statistical inference, 11. Combining code and text with Jupyter, 12. Collaboration with version control, 13. Setting up your computer
Data Science: A First Introduction focuses on using the R programming language in Jupyter notebooks to perform data manipulation and cleaning, create effective visualizations, and extract insights from data using classification, regression, clustering, and inference.
The text emphasizes workflows that are clear, reproducible, and shareable, and includes coverage of the basics of version control. All source code is available online, demonstrating the use of good reproducible project workflows.
Based on educational research and active learning principles, the book uses a modern approach to R and includes accompanying autograded Jupyter worksheets for interactive, self-directed learning. The book will leave readers well-prepared for data science projects.
The book is designed for learners from all disciplines with minimal prior knowledge of mathematics and programming. The authors have honed the material through years of experience teaching thousands of undergraduates in the University of British Columbia’s DSCI100: Introduction to Data Science course.