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, 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 |
|---|---|---|
| Lecture | Perkins LINK 087 (Classroom 3) | M/T/W/Th/F 9:30AM - 10:45AM |
| Lab | Perkins LINK 087 (Classroom 3) | M/Th 11:00AM - 12:15PM |
Office hours
| Name | Location | Time |
|---|---|---|
| Marie Neubrander | Old Chem 203A | Tu 3:30PM - 5:30PM F 1PM - 3PM |
| Mary Knox | Zoom | W 2PM - 3PM |
| Katie Solarz | Old Chem 203A | W 5:30-7:30 PM |
On May 21 (W), Marie will run Katie’s office hours. Katie will run Marie’s on May 23 (F). This week, Katie’s office hours will be on Zoom at https://duke.zoom.us/j/95513369376