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):
    • 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

Please note: students cannot use a course from the department housing their degree program as an elective.

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

  • AST 5731 or STAT 5731 - Bayesian Astrostatistics (4.0 cr)
  • CSCI 5103 - Operating Systems (3.0 cr)
  • CSCI 5105 - Introduction to Distributed Systems (3.0 cr)
  • CSCI 5106 - Programming Languages (3.0 cr)
  • CSCI 5123 - Recommender Systems (3.0 cr)
  • CSCI 5211 - Data Communications and Computer Networks (3.0 cr)
  • CSCI 5231 - Wireless and Sensor Networks (3.0 cr)
  • CSCI 5271 - Introduction to Computer Security (3.0 cr)
  • CSCI 5302 - Analysis of Numerical Algorithms (3.0 cr)
  • CSCI 5304 - Computational Aspects of Matrix Theory (3.0 cr)
  • CSCI 5421 - Advanced Algorithms and Data Structures (3.0 cr)
  • CSCI 5461 - Functional Genomics, Systems Biology, and Bioinformatics (3.0 cr)
  • CSCI 5451 - Introduction to Parallel Computing: Architectures, Algorithms, and Programming (3.0 cr)
  • CSCI 5511 - Artificial Intelligence I (3.0 cr)
  • CSCI 5512 - Artificial Intelligence II (3.0 cr)
  • 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)
  • CSCI 5527 - Deep Learning: Models, Computation, and Applications (3.0 cr)
  • CSCI 5561 - Computer Vision (3.0 cr)
  • CSCI 5609 - Visualization (3.0 cr)
  • CSCI 5707 - Principles of Database Systems (3.0 cr)
  • CSCI 5708 - Architecture and Implementation of Database Management Systems (3.0 cr)
  • CSCI 5715 - From GPS and Virtual Globes to Spatial Computing (3.0 cr)
  • CSCI 5751 - Big Data Engineering and Architecture (3.0 cr)
  • CSCI 5980 - Special Topics in Computer Science (1.0-3.0 cr)
  • CSCI 8271 - Security and Privacy in Computing (3.0 cr)
  • CSCI 8314 - Sparse Matrix Computations (3.0 cr)
  • CSCI 8363 - Numerical Linear Algebra in Data Exploration (3.0 cr)
  • CSCI 8581 - Big Data in Astrophysics (4.0 cr)
  • CSCI 8701 - Overview of Database Research (3.0 cr)
  • CSCI 8715 - Spatial Data Science Research (3.0 cr)
  • CSCI 8725 - Databases for Bioinformatics (3.0 cr)
  • CSCI 8980 - Special Advanced Topics in Computer Science (1.0-3.0 cr)
  • EE 5239 - Introduction to Nonlinear Optimization (3.0 cr)
  • EE 5251 - Optimal Filtering and Estimation (3.0 cr)
  • EE 5351 - Applied Parallel Programming (3.0 cr)
  • EE 5389 - Introduction to Predictive Learning (3.0 cr)
  • EE 5371 - Computer Systems Performance Measurement and Evaluation (3.0 cr)
  • EE 5381 - Telecommunications Networks (3.0 cr)
  • EE 5393 - Circuits, Computation and Biology (3.0 cr)
  • EE 5501 - Digital Communication (3.0 cr)
  • EE 5531 - Probability and Stochastic Processes (3.0 cr)
  • EE 5542 - Adaptive Digital Signal Processing (3.0 cr)
  • EE 5551 - Multiscale and Multirate Signal Processing (3.0 cr)
  • EE 5561 - Image Processing and Applications (3.0 cr)
  • EE 5581 - Information Theory and Coding (3.0 cr)
  • EE 5585 - Data Compression (3.0 cr)
  • EE 8231 - Optimization Theory (3.0 cr)
  • EE 8367 - Parallel Computer Organization (3.0 cr) OR CSCI 8205 - Parallel Computer Organization (3.0 cr)
  • EE 8581 - Detection and Estimation Theory (3.0 cr)
  • EE 8591 - Predictive Learning from Data (3.0 cr)
  • IE 5531 - Engineering Optimization I (4.0 cr)
  • IE 8534 - Advanced Topics in Operations Research (1.0 - 4.0 cr)
  • IE 8535 - Introduction to Network Science (4.0 cr)
  • IE 8564 - Optimization for Machine Learning (4.0 cr)
  • PUBH 7405 - Biostatistics: Regression (4.0 cr)
  • PUBH 7430 - Statistical Methods for Correlated Data (3.0 cr)
  • PUBH 7440 - Introduction to Bayesian Analysis (3.0 cr)
  • PUBH 7445 - Statistics for Human Genetics and Molecular Biology (3.0 cr)
  • PUBH 7460 - Advanced Statistical Computing (3.0 cr)
  • PUBH 7461 - Exploring and Visualizing Data in R (2.0 cr)
  • PUBH 7475 - Statistical Learning and Data Mining (3.0 cr)
  • PUBH 8401 - Linear Models (4.0 cr)
  • PUBH 8432 - Probability Models for Biostatistics (3.0 cr)
  • PUBH 8442 - Bayesian Decision Theory and Data Analysis (3.0 cr)
  • PUBH 8445 - Statistics for Human Genetics and Molecular Biology (3.0 cr)
  • PUBH 8446 - Advanced Statistical Genetics and Genomics (3.0 cr)
  • PUBH 8472 - Spatial Biostatistics (3.0 cr)
  • MATH 5467 - Introduction to the Mathematics of Image and Data Analysis (4.0 cr)
  • STAT 5052 - Statistical and Machine Learning (3.0 cr)
  • STAT 5101 - Theory of Statistics I (4.0 cr) OR MATH 5651 - Basic Theory of Probability and Statistics (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 5701 - Statistical Computing (3.0 cr)
  • STAT 8051 - Advanced Regression Techniques: linear, nonlinear and nonparametric methods (3.0 cr)
  • STAT 8101 - Theory of Statistics 1 (3.0 cr)
  • STAT 8102 - Theory of Statistics 2 (3.0 cr)
  • STAT 8112 - Mathematical Statistics II (3.0 cr)

Course links

Class descriptions:

Class schedules:

Questions?

For questions about the data science minor curriculum, contact:

Allison Small
Graduate Program Coordinator

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