Graduate minor curriculum

Academic requirements

To satisfy all program requirements for both the M.S. minor or the Ph.D. minor, students must:

  • Take all courses through the University of Minnesota - Twin Cities campus and on the A/F grading scale (transfer coursework will not be accepted)
  • Maintain a 3.0 GPA for all courses used for the data science minor
  • Take one Tier I course from each of the three emphasis areas (for a total of at least 9 credits). Doctoral students must take 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 for the M.S. (as long as it is outside their major program). Note: Use of 4xxx courses towards program requirements is not permitted. 

Courses

View the full list of data science graduate courses.

Statistics

Take 1 or more course(s) totaling 3 or more credit(s) from the following:

  • STAT 5101 - Theory of Statistics I (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

Take 1 or more course(s) totaling 3 or more credit(s) from the following:

  • 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 - Statistical Learning and Data Mining (3.0 cr)

Infrastructure and Large Scale Computing

Take 1 or more course(s) totaling 3 or more credit(s) from the following:

  • 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 Elective

Students cannot use a course from the department housing their degree program as an elective. 

Take 1 or more course(s) totaling 3 or more credit(s) from the following:

  • CSCI 5105 - Introduction to Distributed 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 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 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 5980 - Special Topics in Computer Science (1.0-3.0 cr)
  • CSCI 8205 - Parallel Computer Organization (3.0 cr)
  • CSCI 8314 - Sparse Matrix Computations (3.0 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 5371 - Computer Systems Performance Measurement and Evaluation (3.0 cr)
  • EE 5381 - Telecommunications Networks (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)
  • 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 (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 7460 - Advanced Statistical Computing (3.0 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)
  • STAT 5101 - Theory of Statistics I (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)