STA 199-002: Intro to Data Science

Introduction to data science and statistical thinking. Learn to explore, visualize, and analyze data to understand natural phenomena, investigate patterns, model outcomes, make predictions, and do so in a reproducible and shareable manner. Gain experience in data wrangling and munging, exploratory data analysis, predictive modeling, data visualization, and effective communication of results. Work on problems and case studies based on real-world questions and data. The course will focus on the R statistical computing language.


Course information

All times listed are in US Eastern Time Zone.

Zoom links and email addresses are available on Sakai.

Lectures

Tuesday 10:15 - 11:30 AM Robert Eisinger
Thursday 10:15 - 11:30 AM Robert Eisinger

Labs

Friday (06L) 10:15 - 11:30 AM Daniel Deng (Carson Garcia)
Friday (05L) 12:00 - 1:15 PM Daniel Deng (Jenny Huang)
Friday (07L) 1:45 - 3:00 PM Emily Gentles (Mounika Adepu)
Saturday (asynchronous lab help) 8:00 - 10:00 AM Daniel Deng

Teaching team and office hours

Monday 9:30 - 11:30 PM George Lindner (technical help)
7:00 - 9:00 PM Mounika Adepu
3:00 - 5:00 PM Carson Garcia
Tuesday 12:00 - 2:00 PM Olivia Liu
Wednesday 8:00 - 10:00 AM Robert Eisinger
4:00 - 6:00 PM Jenny Huang
8:00 - 10:00 PM George Lindner (technical help)
Thursday 2:00 - 4:00 PM Paige Bartusiak
Friday 5:00 - 7:00 PM Emily Gentles

Additional instructor office hours are available by appointment.

Textbooks

All texts are available for free online.

R for Data Science Grolemund, Wickham O'Reilly, 1st edition, 2016
Introductory Statistics with Randomization and Simulation Diez, Barr, Çetinkaya-Rundel CreateSpace, 1st Edition, 2014