STA 112FS: Data Science

Combines techniques from statistics, math, computer science, and social sciences, to learn how to use data to understand natural phenomena, explore patterns, model outcomes, and make predictions. Gain experience in data wrangling and munging, exploratory data analysis, predictive modeling, data visualization, and effective communication of results. Discussions around reproducibility, data sharing, data privacy. Focus on the R statistical computing language. No computing background necessary.


Course info
When Tue & Thu 10:05 - 11:20am
Where Perkins LINK 070 (Seminar 4)

Teaching team
Instructor Mine Çetinkaya-Rundel     OH: Mon 1 - 3pm & Wed by appointment at Old Chem 213
TA Kyle Burris   OH: Wed 9 - 10am & Fri 10 - 11am at Old Chem 211A

Texts

All texts are freely available online:
OpenIntro Data Science (link TBA) Çetinkaya-Rundel In progress
R for Data Science Grolemund, Wickham O'Reilly, 1st edition, 2016
OpenIntro Statistics Diez, Barr, Çetinkaya-Rundel CreateSpace, 3rd Edition, 2015

Materials

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

Green Classroom

This course has achieved Duke’s Green Classroom Certification. The certification indicates that the faculty member teaching this course has taken significant steps to green the delivery of this course. Your faculty member has completed a checklist indicating their common practices in areas of this course that have an environmental impact, such as paper and energy consumption. Some common practices implemented by faculty to reduce the environmental impact of their course include allowing electronic submission of assignments, providing online readings and turning off lights and electronics in the classroom when they are not in use. The eco-friendly aspects of course delivery may vary by faculty, by course and throughout the semester. Learn more at http://sustainability.duke.edu/action/certifications/classroom/index.php.