Michael Freeman is a senior lecturer at the University of Washington Information School, where he teaches courses in data science, interactive data visualization, and web development. Prior to his teaching career, he worked as a data visualization specialist and research fellow at the Institute for Health Metrics and Evaluation. There, he performed quantitative global health research and built a variety of interactive visualization systems to help researchers and the public explore global health trends. Michael is interested in applications of data visualization to social justice, and holds a Master's in Public Health from the University of Washington.







Joel Ross is a senior lecturer at the University of Washington Information School, where he teaches courses in web development, mobile application development, software architecture, and introductory programming. While his primary focus is on teaching, his research interests include games and gamification, pervasive systems, computer science education, and social computing. He has also done research on crowdsourcing systems, human computation, and encouraging environmental sustainability. Joel earned his M.S. and Ph.D. in information and computer sciences from the University of California, Irvine."

1 Using the Command Line

2 Version Control with git and GitHub

3 Using Markdown for Documentation

4 Introduction to R

5 Functions in R

6 Vectors and Lists

7 Data and Data Frames

8 Manipulating Data with dplyr

9 Reshaping Data with tidyr

10 Accessing Databases and Web APIs

11 Designing Data Visualizations

12 Creating Visualizations with ggplot2

13 Interactive Visualization in R

14 Dynamic Reports with R Markdown

15 Building Interactive Web Applications with Shiny

16 Working Collaboratively"

1. Guides students through setting up their computer for data science, understanding how the pieces fit together, and successfully using them to solve real problems

2. Introduces R, RStudio, git, GitHub, Markdown, Shiny, and other leading tools

3. Covers everything from preparing raw data to creating beautiful, sharable visualizations

4. Anticipates questions and demystifies complex ideas, reflecting the authors' experience introducing data science to thousands of students"