Courses

For class descriptions please go to Quick Course Descriptions
(or alternatively to the UMN Class Search).

For class schedules please go to Quick Class Schedules.

To use any course requiring advisor or DGS approval, please submit a syllabus or detailed course description and a short note on the relevancy of the course to data science (in many cases one sentence may suffice)

Statistics

  (6cr: take 2 courses, at least one of which is from Tier I)

  Tier I Courses

  •  STAT 5101/5102 - Theory of Statistics I & II
  •  STAT 5302 - Applied Regression Analysis
  •  STAT 5511 - Time Series Analysis
  •  STAT 5401 - Applied Multivariate Methods
  •  STAT 8051 - Applied Statistical Methods 1: Computing and Generalized Linear Models
  •  PUBH 7440 - Introduction to Bayesian Analysis

  Tier II Courses

  •  PUBH 8401 - Linear Models
  •  PUBH 8432 - Probability Models for Biostatistics
  •  PUBH 7405 - Biostatistics Regression
  •  PUBH 7430 - Statistical Methods for Correlated Data
  •  PUBH 7460 - Advanced Statistical Computing
  •  PUBH 8429 - Probability Models for Biostatistics
  •  PUBH 8442 - Bayesian Decision Theory
  •  EE 5531 Probability and Stochastic Processes
  •  EE 8581 - Detection and Estimation Theory
  •  Any course from a list of STAT/Biostat 5xxx/8xxx classes (but not STAT 5021) with advisor and DGS approval

Algorithmics

  (6cr: take 2 courses, at least one of which is from Tier I)

  Tier I Courses

  •  CSCI 5521 - Introduction to Machine Learning (formerly Pattern Recognition) 
  •  CSCI 5523 - Introduction to Data Mining
  •  CSCI 5525 - Machine Learning
  •  EE 8591 - Predictive Learning from Data
  •  PUBH 7475 - Statistical Learning and Data Mining

  Tier II Courses

  •  CSCI 5302 - Analysis of Numerical Algorithms
  •  CSCI 5304 - Computational Aspects of Matrix Theory
  •  CSCI 5511 - Artificial Intelligence I
  •  CSCI 5512 - Artificial Intelligence II
  •  CSCI 5609 - Visualization (renumbered from CSci 5109)
  •  CSCI 8314 - Sparse Matrix Computations
  •  EE 5239 - Introduction to Nonlinear Optimization
  •  EE 5251 - Optimal Filtering and Estimation
  •  EE 5542 - Adaptive Digital Signal Processing
  •  EE 5551 - Multiscale and Multirate Signal Processing
  •  EE 5561 - Image Processing and Applications
  •  EE 5581 - Information Theory and Coding
  •  EE 5585 - Data Compression
  •  EE 8231 - Optimization Theory
  •  IE 5531 - Engineering Optimization I
  •  IE 8534 - Advanced Topics in Operations Research
  •  Any advanced class in Optimization, Game Theory, or topic related to the listed Algorithmics courses (with advisor and DGS approval)

Infrastructure and Large Scale Computing

  (6cr: take 2 courses, at least one of which is from Tier I)

  Tier I Courses

  •  CSCI 5105 - Introduction to Distributed Systems
  •  CSCI 5451 - Introduction to Parallel Computing: Architectures, Algorithms, and Programming
  •  CSCI 5707 - Principles of Database Systems
  •  CSCi 8980 - Cloud Computing/Big Data (will be developed into a regular class)
  •  EE 5351 - Applied Parallel Programming
  •  EE 8367/CSCI 8205 - Parallel Computer Organization

  Tier II Courses

  •  CSCI 5211 - Data Communications and Computer Networks
  •  CSCI 5231 - Wireless and Sensor Networks
  •  CSCI 5271 - Introduction to Computer Security
  •  CSCI 5708 - Architecture and Implementation of Database Management Systems
  •  CSCI 5715 - Spatial Computing (ECAS approval pending)
  •  CSCI 5980 - Topic: Big Data Engineering and Analytics 
  •  CSCI 8701 - Overview of Database Research
  •  CSCI 8715 - Spatial Databases and Applications
  •  CSCI 8725 - Databases for Bioinformatics
  •  EE 5371 - Computer Systems Performance Measurement and Evaluation
  •  EE 5381 - Telecommunications Networks
  •  EE 5501 - Digital Communication 
  •  Any advanced class in Large-scale data management or analysis, or topic related to the listed Infrastructure courses (with advisor and DGS approval)

Electives

(Suggestions only. Non-exclusive list.  An elective course is a course that explores more deeply concepts or methodologies addressed in a regular track course listed above, or a course that addresses tools or methodologies needed to make the methods above work, or a course in an application area outside data science in which issues of data management, data analysis, or data mining are discussed in the context of that application area.   There are potentially many such courses around the University.  To use a course not specifically listed below as an elective, submit a short note explaining how the course meets one of these criteria together with a syllabus or other details of the course which lists the topics and the level at which the topics are taught.  The level is often indicated indirectly by the prerequisites.)

  •  CSCI 5461 - Functional Genomics, Systems Biology, and Bioinformatics
  •  CSCI 5561 - Computer Vision
  •  CSCI 8271 - Security and Privacy in Computing
  •  CSCI 8363 - Numerical Linear Algebra in Data Exploration
  •  PUBH 7445 - Statistics in Genetics and Molecular Biology
  •  PUBH 8445 - Statistical Genetics I
  •  PUBH 8446 - Statistical Genetics II
  •  PUBH 8472 - Spatial Biostatistics
  •  MATH 5467-Introduction to the Mathematics of Image and Data Analysis
  •  5xxx 8xxx topics classes in CSCI EE STAT PUBH (Biostat) etc. (See Advisor for approval)
  •  Data Driven Applications -- Emerging applications

Course Catalog