Data Science Competition


In this course, students enter a data science competition. They learn any skills necessary to gain insight (data sleuthing). This may include classical machine learning algorithms, time series analysis or point processes, multi-armed bandits, creating a new custom machine learning technique, handling imbalanced data, techniques for tuning parameters, any of a broad array of other techniques, or domain knowledge in another field. Students will work in teams, and during class they will have the opportunity to discuss possible stumbling blocks. Prerequisites: CS 671 (or STA 671D or ECE 687D - Theory and Algorithms for Machine Learning), STA 622 (Statistical Data Mining), Math 466 (Mathematics of Machine Learning), STA 231 (Advanced Introduction to Probability), STA 470S (Introduction to Statistical Consulting) or STA 440 (Case Studies in the Practice of Statistics)


Prerequisite: COMPSCI 671D/STA 671D/ECE 687D and STA 622/COMPSCI 579 and MATH 466 and STA 231/MATH 340 and (STA 470S or STA 440)

Curriculum Codes
  • R
  • QS
Typically Offered
Spring Only