STA 199: Intro to Data Science

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, and do so in a reproducible and shareable manner. Gain experience in data wrangling and munging, exploratory data analysis, predictive modeling, data visualization, and effectively communicating 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.

Course info


  Soc Sci 139      Tue and Thu 10:05a - 11:20a


Lab 01      Old Chem 003       Fri 10:05a - 11:20p

Lab 02      Soc Psych 127      Fri 11:45a - 1p

Lab 03      Old Chem 003       Fri 1:25p - 2:40p

Teaching team and office hours

Instructor Prof. Maria Tackett   Thu 1p - 2:30p Old Chem 118B
TAs Salvador Arellano   Wed 5p - 7p Old Chem 203B
Max Bartlett   Tue 6:30p - 8:30p Old Chem 203B
Meredith Brown   Mon 12:30p - 2:30p Old Chem 203B
Steven Herrera   Thu 5:30p - 7:30p Old Chem 203B
Malavi Ravindran   Mon 3p - 5p Old Chem 203B
Becky Tang   Wed 3p - 5p Old Chem 203B


All books are freely available online. Hardcopies are also available for purchase.

R for Data Science Grolemund, Wickham O'Reilly, 1st edition, 2016
OpenIntro Statistics Diez, Barr, Çetinkaya-Rundel CreateSpace, 4th Edition, 2019
Introductory Statistics with Randomization and Simulation Diez, Barr, Çetinkaya-Rundel CreateSpace, 1st Edition, 2014


You should bring a fully-charged laptop, tablet with keyboard, or comparable device to every lecture and lab session.

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