Ph.D. minor curriculum

To satisfy all program requirements for the Ph.D. minor, students must:

  1. Complete 12 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
    • An additional elective course of at least 3 credits (for a total of at least 12 credits).
      • This course may be any Tier I or II course, or approved electives (as long as it is outside their major program). Note: Use of 4xxx courses towards program requirements is not permitted.
  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

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

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

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)
Doctoral minor electives

Complete an additional elective course of at least 3 credits to reach the 12 credit requirement. These may include related courses from departments in the College of Science & Engineering and the School of Statistics (but not STAT 5021).  Please note: students cannot use a course from the department housing their degree program as an elective.

The following PUBH courses may also be used: 

  • PUBH 7445 - Statistics for Human Genetics and Molecular Biology
  • PUBH 7461 - Exploring and Visualizing Data in R
  • PUBH 8445 - Statistics for Human Genetics and Molecular Biology
  • PUBH 8446 - Advanced Statistical Genetics and Genomics
  • PUBH 8472 - Spatial Biostatistics

No PUBH 4xxx, 5xxx, or 6xxx level courses can be used as electives. 

Questions?

Allison Small headshot

Allison Small

Graduate Program Coordinator

Current students: [email protected]
Prospective students: [email protected]