Data Science in Mechanical Engineering Track

Objective:  to fulfill an existing workforce need for engineers and researchers with expertise in Mechanical Engineering and Data Science.

Philosophy:  students will complete a multi-disciplinary graduate curriculum by combining Data Science (offered in CS and MATH) with Mechanical Engineering courses.  This is accomplished, and made highly valuable, by leveraging existing courses from the Graduate Data Science Certificate.


  • Together with an advisor, each student in the track may select from the list below to fulfill the track requirements.
  • Before entering this track, the student and advisor should review to course descriptions for COMP 5360 (Introduction to Data Science) and ME EN 5/6250 (Programming for Engineers). If the student does not have experience in these areas then those classes (or similar) should be taken before entering the track.
  • Any approved Mechanical Engineering graduate course may be applied to this track to fulfill that aspect of the course requirements.

Data science core:

  • MATH 5010, Introduction to Probability
  • CS 6140, Data Mining
  • CS 6350, Machine Learning
  • CS 6355, Structured Prediction
  • CS 6190, Probabilistic Modeling
  • CS 6230, Parallel Computing

Data science technical electives:

  • MATH 5740, Mathematical Modeling
  • MATH 6010, Linear Models
  • MATH 6020, Multilinear Models
  • MATH 6790, Case Studies in Computational Engineering and Science
  • CS 6300, Artificial Intelligence
  • CS 6635, Visualization of Scientific Data
  • CS 6955 Deep Learning
  • CH EN 7703, Bayesian Model Validation
  • CS 7960, Models of Computation for Massive Data