Programming Skills for Data Science brings together all the foundation skills needed to transform raw data into actionable insights for domains ranging from urban planning to precision medicine, even if you have no programming or data science experience. Guided by expert instructors Michael Freeman and Joel Ross, this book will help learners install the tools required to solve professional-level data science problems, including widely used R language, RStudio integrated development environment, and Git version-control system. It explains how to wrangle data into a form where it can be easily used, analyzed, and visualized so others can see the patterns uncovered. Step by step, students will master powerful R programming techniques and troubleshooting skills for probing data in new ways, and at larger scales.
Features:
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.
Table of Contents:
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
About the Author
- 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.
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