Certificate in Statistical and Computational Data Science

The Certificate in Statistical and Computational Data Science is a Master's-level, five-course (15 credit) certificate that includes graduate-level course requirements from both Statistics and Computer Science.  It consists of at least two computer science courses and two statistics courses.

Graduate matriculated students or post-graduate non-degree students can enroll in the required courses for the Certificate in Statistical and Computational Data Science, as long as they have the prerequisites for the courses and submit the required pre-application

This certificate is designed for professionals seeking to enhance their data science skills or current graduate students who want to round out their degrees. All non-computer sciences graduate students need to fill out override forms to enroll in computer science classes. Master's or doctoral students in Computer Science should consider the Computer Science Master's with Concentration in Data Science.

For students entering the program without strong computer programming skills, we offer a one-credit bridge course: Introduction to Numerical Computing with Python (COMPSCI 590N). This course covers the basics of Python, which is one of the core languages used in data science.


Bridge Course

  • Introduction to Numerical Computing with Python (COMPSCI 590N)

All students must complete one of the following courses:

  • Machine Learning (COMPSCI 589)
  • Machine Learning (COMPSCI 689)

Students need to complete one or two of the following courses:

  • Introduction to Natural Language Processing (COMPSCI 585)
  • Algorithms for Data Science (COMPSCI 590D)
  • Systems for Data Science (COMPSCI 590S)
  • Data Visualization and Exploration (COMPSCI 590V)

Students need to complete two or three of the following courses:

  • Computational Statistics (STAT 597A)
  • Intro to Probability and Math Statistics (STAT 597S)
  • Mathematical Statistics I  (STAT 607)
  • Mathematical Statistics II  (STAT 608)
  • Regression (STAT 697R)
  • Linear Models (STAT 705)