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
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)
Doctoral minor electives
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)

Questions?

Allison Small headshot

Allison Small

Graduate Program Coordinator

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