Program Structure

The program is designed to be completed in twelve months (September to August). Students may elect to instead complete the program over 16, 21, or 24 months, and many do so. In some cases, exceptionally well-prepared students might be able complete their work in 9 months. All students begin the program in September; there is no option for starting in the spring semester.

For students taking longer than 12 months, full-time status for visa purposes is two credits per semester (and only one credit in the final semester). 

Course requirements are as follows: 

  • DATA 1030. Hands-on Data Science. Supervised machine learning pipelines in Python, from data exploration to deployment
  • DATA 1050. Data Engineering. Computer science for data science
  • APMA 1690. Computational Probability and Statistics. Probability theory and mathematical statistics from the perspective of computing
  • DATA 2020. Statistical Learning. Inferential methods for regression analysis and statistical learning in R
  • DATA 2080. Data and Society. Ethical and societal implications of data and data science,
  • CSCI 2470, or equivalent. Deep Learning. Overview of neural network architectures and hands-on practice
  • Machine Learning Theory: New course coming spring 2023
  • DATA 2050. Data Practicum. Real-world data project, internship in industry or academia; see examples here
  • Elective. (Domain knowledge relevant to individual interest, 1 credit, must be a graduate level course with 4-digit course number starting with a non-0 digit)