Data science graduate minor

Overview

Current University of Minnesota graduate students have the option to complete a minor in data science. The Data Science minor provides a strong foundation in the science of big data and its analysis by gathering together the knowledge, expertise, and educational assets in data collection and management, data analytics, scalable data-driven pattern discovery, and the fundamental concepts behind these methods. Students completing this program will learn the state-of-the-art methods for treating big data and be exposed to the cutting edge methods and theory forming the basis for the next generation of big data technology.

Students take each least one course in each of the three focus areas (statistics, algorithmics, and infrastructure and large scale computing).

Length of program

  • Master's: 9 credits (3 courses)
  • Doctorate: 12 credits (4 courses)

Declaring a minor

Students must be currently enrolled in a University of Minnesota M.S. or Ph.D. program. 

A minor is declared by completing the Graduate Degree Plan. Students should consult with the Graduate Program Coordinator to obtain the appropriate signatures. Be sure to follow the University catalog description of the data science minor for the most accurate and up-to-date requirements.

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)
  • 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 7401 - Fundamentals of Biostatistical Inference (4.0 cr)
  • PUBH 7402 - Biostatistics Modeling and Methods (4.0 cr)
  • PUBH 7475 - 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 8980 - Topic: Cloud Computing/Big Data (temporary until developed into a regular class)
  • EE 5351 - Applied Parallel Programming (3.0 cr)
  • EE 8367 or CSCI 8205 - Parallel Computer Organization (3.0 cr) (these courses are cross-listed)

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)