Ph.D. minor curriculum
To satisfy all program requirements for the Ph.D. minor, students must:
- 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.
- One Tier I course from each of the three emphasis areas (for a total of at least 9 credits):
- Maintain a 3.0 GPA for all courses used for the data science minor
- 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
- Algorithmics courses
- Infrastructure and Large Scale Computing courses
- Doctoral minor electives
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)
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)
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)
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
Graduate Program Coordinator
Current students: csgradmn@umn.edu
Prospective students: csadmit@umn.edu