M.S. minor curriculum

To satisfy all program requirements for the M.S. minor, students must:

  1. Complete 9 credits of coursework, including:
    • One Tier I course from each of the three emphasis areas (for a total of at least 9 credits):
      • statistics
      • algorithmics, and
      • infrastructure and large-scale computing
  2. Maintain a 3.0 GPA for all courses used for the data science minor
  3. Take all courses on the A/F grading scale (transfer coursework will not be accepted)

Please note: all courses must be taken through the University of Minnesota - Twin Cities campus.


Statistics courses
Statistics courses

Take one or more course(s) totaling three or more credits from the following list of courses:

  • STAT 5101 - Theory of Statistics I (4.0 cr) or MATH 5651 - Basic Theory of Probability (4.0 cr)
  • STAT 5102 - Theory of Statistics II (4.0 cr)
  • STAT 5302 - Applied Regression Analysis (4.0 cr)
  • STAT 5511 - Time Series Analysis (3.0 cr)
  • STAT 5401 - Applied Multivariate Methods (3.0 cr)
  • STAT 8051 - Advanced Regression Techniques: linear, nonlinear and nonparametric methods (3.0 cr)
  • STAT 8101 - Theory of Statistics I
  • STAT 8102 - Theory of Statistics II
  • PUBH 7401 - Fundamentals of Biostatistical Inference
  • PUBH 7402 - Biostatistics Modeling and Methods
  • PUBH 7440 - Introduction to Bayesian Analysis (3.0 cr)
Algorithmics courses
Algorithmics courses

Take one or more course(s) totaling three or more credits from the following list of courses:

  • CSCI 5521 - Introduction to Machine Learning (3.0 cr)
  • CSCI 5523 - Introduction to Data Mining (3.0 cr)
  • CSCI 5525 - Machine Learning (3.0 cr)
  • EE 8591 - Predictive Learning from Data (3.0 cr)
  • PUBH 7475 - Statistical Learning and Data Mining (3.0 cr)
  • PUBH 8475 or STAT 8056 - Statistical Learning and Data Mining (3.0 cr)
Infrastructure and Large Scale Computing courses
Infrastructure and Large Scale Computing courses

Take one or more course(s) totaling three or more credits from the following list of courses:

  • CSCI 5105 - Introduction to Distributed Systems (3.0 cr)
  • CSCI 5451 - Introduction to Parallel Computing: Architectures, Algorithms, and Programming (3.0 cr)
  • CSCI 5707 - Principles of Database Systems (3.0 cr)
  • CSCI 5708 - Architecture and Implementation of Database Management Systems (3.0 cr)
  • EE 5351 - Applied Parallel Programming (3.0 cr)
  • EE 8367 or CSCI 8205 - Parallel Computer Organization (3.0 cr)

Questions?

Allison Small headshot

Allison Small

Graduate Program Coordinator

Current students: csgradmn@umn.edu
Prospective students: csadmit@umn.edu