Overview
Intro to data science and statistical thinking. Learn to explore, visualize, and analyze data to understand natural phenomena, investigate patterns, model outcomes, and make predictions, all 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 inspired by and based on real-world questions and data. The course will focus on the R statistical computing language. No statistical or computing background is necessary. Not open to students who have taken a 100-level Statistical Science course, Statistical Science 210, or a Statistical Science course numbered 300 or above.
Meetings
| Meeting | Location | Time | Instructor / TAs |
|---|---|---|---|
| Lecture | Biological Sciences 111 | Tue & Thur 11:45 am - 1:00 pm | Dr. Mine Çetinkaya-Rundel |
| Lab 01 | Perkins LINK 071 (Classroom 5) | Mon 8:30 - 9:45 am | Marie + Xueyan |
| Lab 03 | Allen 326 | Mon 10:05 - 11:20 am | Eduardo + Jesse |
| Lab 04 | Perkins LINK 087 (Classroom 3) | Mon 10:05 - 11:20 am | Federico + Abuzar |
| Lab 05 | Perkins LINK 087 (Classroom 3) | Mon 11:45 am - 1:00 pm | Katie + Max |
| Lab 06 | Perkins LINK 071 (Classroom 5) | Mon 11:45 am - 1:00 pm | Sarah + Natasha |
| Lab 07 | Perkins LINK 087 (Classroom 3) | Mon 1:25 - 2:40 pm | Marie + Bethany |
| Lab 08 | Perkins LINK 071 (Classroom 5) | Mon 1:25 - 2:40 pm | Arijit + Allison |
| Lab 09 | Perkins LINK 087 (Classroom 3) | Mon 3:05 - 4:20 pm | Katie + Ziyang |
| Lab 11 | Perkins LINK 071 (Classroom 5) | Mon 4:40 - 5:55 pm | Luxman + Ben |