Data Science Team Companion Course

CSCI 4802/5802, Fall 2019
Time: Tuesday, 5:00pm - 6:15pm
Room: DUAN G131
Instructor: Dan Larremore
Email: daniel.larremore
Team website:
Mailing list and Slack: subscribe at
Assignments: Moodle
Prerequisites: linear algebra or permission of instructor.

Course Description

Gives students hands-on experience applying data science techniques and machine learning algorithms to real-world problems. Students will work in small teams on internal challenges, many of which will be sponsored by local companies and organizations, and will represent the university in larger teams for external challenges at the national and global level, such as those hosted by Kaggle. Students will be expected to participate in both internal and external challenges, attend meetings, and present short presentations to the group when appropriate.


Data science is one of the fastest-growing sectors of our economy, and there is a great demand for data scientists with practical experience applying statistical techniques and machine learning algorithms to real data. While several courses in the CS curriculum develop these techniques, in the areas of machine learning, statistical modeling, network science, numerical analysis, and data science more broadly, and while these courses often include a hands-on project, no course specifically focuses on putting this myriad of tools to work on real data and developing intuition for when to apply certain techniques over others. The present course will fill in this gap, allowing students to work in teams both small and large to solve real-world prediction challenges, gaining valuable experience whether entering the workforce or remaining in academia.


To accompany the prediction challenges and other activities hosted by the team, we will have short presentations on topics relevant to the current competition or data science more broadly. A non-exhaustive list of topics is as follows.


The general requirement for the course is to participate in the competitions and other activities of the team. As the specifics of these competitions and activities will change from semester to semester, the course is formally structured as follows. You will submit three written reports to Moodle detailing what you have done. These reports should be structured as follows:

0. Attendance

1. Midterm report 1 (The Proposal)

2. Midterm report 2 (The Progress Report)

3. Final report