10/1/26, 5:46 PM
COGS 137 - COGS 137: Practical Data Science in R
COGS 137: Practical Data Science in R
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-
CSB 243
12P
Tu 1-3P
10 min appt on Zoom (see Canvas for signup)
TA
Vinuthna
Hasthi
Wed 3-4
10 min appt on Zoom (see Canvas for signup)
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
https://cogs137-fa26.github.io/cogs137-fa26/
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10/1/26, 5:46 PM
Tidy Modeling With R
COGS 137 - COGS 137: Practical Data Science in 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.
License
https://cogs137-fa26.github.io/cogs137-fa26/
🤍
This page is built with
, inspiration from
Mine Çetinkaya-Rundel, and Quarto.
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