COGS 137: Practical Data Science in R

Course Info

Practical Data Science in R focuses on teaching students how to think rigorously throughout the data science process. To this end, through interaction with unique data sets and interesting questions, this course helps students 1) gain fluency in the R programming language, 2) effectively explore & visualize data, 3) use statistical thinking to analyze data and rigorously evaluate their conclusions, and 4) effectively communicate their results. Course objectives are accomplished through hands-on practice, using real-world data to learn via case studies, and project-based learning.

Days & Times

Lecture: MWF 10-10:50A (WLH 2205)
Lab: Th 5-5:50 (WLH 2205)

Instructional Staff & Office Hours
Instructor Shannon Ellis sellis@ucsd.edu Mon 11A-12P CSB 243
Tu 1-3P 10 min appt on Zoom (see Canvas for sign-up)
TA Vinuthna Hasthi Wed 3-4 10 min appt on Zoom (see Canvas for sign-up)

Course Objectives

  • Program at the introductory level in the R statistical programming language

  • Employ the tidyverse suite of packages to interact with, wrangle, visualize, and model data

  • Explain & apply statistical and machine learning concepts for data analysis

  • Communicate data science projects through effective visualization, oral presentation, and written reports

Texts

Texts are freely available online:

Introduction to Modern Statistics Çetinkaya-Rundel and Hardin OpenIntro, 2e, 2024
R for Data Science Wickham, Çetinkaya-Rundel, and Grolemund O’Reilly, 2e, 2023
Tidy Modeling With R Max Kuhn and Julia Silge O’Reilly, 1e, 2022

Materials

You should have access to a laptop and bring it to every class, fully charged (as possible).

Note: If you do not have consistent access to the technology needed, please reach out to Professor Ellis to discuss.

Acknowledgements

I want to first recognize Dr. Mine Çetinkaya-Rundel for her unparalleled efforts in support of education and educators in data science, statistics, and R programming. This course website was adapted from her course websites. Some of our course materials are also from her datascienceinabox project. I am so very indebted to Mine! I also want to thank the Open Case Studies team for their tireless work in putting together interesting and topical case studies, a handful of which have been used throughout the course over the years. And, finally, thanks to Allison Horst, whose artwork is inspiring, educational, and fun…and is used throughout this course. Further, thanks to the greater R (education) community who make planning and teaching R courses simply the best.