M.S. Plan B Curriculum

Take two courses (totaling 6-8 credits) from the following list of courses, at least one of which is a Tier I course:

Tier I courses

• STAT 5101 - Theory of Statistics I / MATH 5651 - Basic Theory of Probability and Statistics (STAT 5101 and MATH 5651 are equivalencies)
• STAT 5102 - Theory of Statistics II
• STAT 5302 - Applied Regression Analysis
• STAT 5401 - Applied Multivariate Methods
• STAT 5511 - Time Series Analysis
• STAT 8051 - Advanced Regression Techniques: Linear, Nonlinear and Nonparametric methods
• 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

Tier II courses

• AST/STAT 5731 - Bayesian Astrostatistics
• PUBH 7405 - Biostatistical Inference I
• PUBH 7406 - Biostatistical Inference II
• PUBH 7407 - Analysis of Categorical Data
• PUBH 7430 - Statistical Methods for Correlated Data
• PUBH 7460 - Advanced Statistical Computing
• PUBH 7485 - Methods for Causal Inference
• PUBH 8401 - Linear Models
• PUBH 8432 - Probability Models for Biostatistics
• PUBH 8442 - Bayesian Decision Theory and Data Analysis
• STAT 5052 - Statistical and Machine Learning
• STAT 5201 - Sampling Methodology in Finite Populations
• STAT 5303 - Designing Experiments
• STAT 5421 - Analysis of Categorical Data
• STAT 5601 - Nonparametric Methods
• STAT 5701 - Statistical Computing
• STAT 8112 - Mathematical Statistics II
• EE 5531 Probability and Stochastic Processes
• Any course from a list of STAT/Biostat 5xxx/8xxx classes (but not STAT 5021) with advisor and DGS approval

Take two courses (totaling 6 credits) from the following list of courses, at least one of which is a Tier I course:

Tier I courses

• CSCI 5521 - Machine Learning Fundamentals
• CSCI 5523 - Introduction to Data Mining
• CSCI 5525 - Machine Learning: Analysis and Methods
• EE 8591 - Predictive Learning from Data
• PUBH 7475 - Statistical Learning and Data Mining
• PUBH 8475/STAT 8056 - 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 5527 - Deep Learning: Models, Computation, and Applications
• CSCI 5609 - Visualization
• CSCI 8314 - Sparse Matrix Computations
• CSCI 8581 - Big Data in Astrophysics
• EE 5239 - Intro to Nonlinear Optimization with Applications in Machine Learning and Artificial Intelligence
• EE 5251 - Optimal Filtering and Estimation
• EE 5389 - Introduction to Predictive Learning
• EE 5542 - Adaptive Digital Signal Processing
• EE 5561 - Image Processing and Applications: From linear filters to artificial intelligence
• EE 5581 - Information Theory and Coding
• EE 5585 - Data Compression
• EE 8231 - Optimization Theory
• IE 5531 - Engineering Optimization I
• IE 8521 - Optimization
• IE 8531 - Discrete Optimization
• IE 8564 - Optimization for Machine Learning
• Any advanced class in optimization, game theory, or topic related to the listed Algorithmics courses (with advisor and DGS approval)

Take two courses (totaling 6 credits) from the following list of courses, at least one of which is a Tier I course:

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 5708 - Architecture and Implementation of Database Management Systems
• EE 5351 - Applied Parallel Programming
• EE 8367/CSCI 8205 - Parallel Computer Organization

Tier II courses

• CSCI 5103 - Operating Systems
• CSCI 5211 - Data Communications and Computer Networks
• CSCI 5271 - Introduction to Computer Security
• CSCI 5715 - From GPS, Google Maps, and Uber to Spatial Data Science
• CSCI 5801 - Software Engineering I
• CSCI 5802 - Software Engineering II
• CSCI 8102 - Foundations of Distributed Computing
• CSCI 8701 - Overview of Database Research
• CSCI 8715 - Spatial Data Science Research
• CSCI 8725 - Databases for Bioinformatics
• CSCI 8735 - Advanced Database Systems
• CSCI 8801 - Advanced Software Engineering
• EE 5355 - Algorithmic Techniques for Scalable Many-core Computing
• EE 5371 - Computer Systems Performance Measurement and Evaluation
• EE 5501 - Digital Communication
• SENG 5709 - Event Driven Architecture & Real-time Data Processing
• Any advanced class in large-scale data management or analysis, or topic related to the listed Infrastructure courses (with advisor and DGS approval)

If completing a one semester capstone project
Students must take at least one 3-credit 8xxx level course if completing a one-semester capstone project. One semester of DSCI 8760 will not count towards this requirement. The 8xxx level course can be from either one of the three track requirements or an approved elective. 

If completing a two semester capstone project
Students can satisfy the 8xxx level requirement by completing a two semester capstone project. This requires students to register for DSCI 8760 for two semesters. 

Other graduate-level credits to reach a total of at least 31 regular coursework credits.  These can include additional courses from the three track requirements and related courses from departments in the College of Science & Engineering and the School of Statistics (but not STAT 5021). Students can use one course of independent study/directed research as an elective with program approval. 

The following PUBH courses may also be used: 

• PUBH 7445 - Statistics for Human Genetics and Molecular Biology
• PUBH 7461 - Exploring and Visualizing Data in R
• PUBH 8445 - Statistics for Human Genetics and Molecular Biology
• PUBH 8446 - Advanced Statistical Genetics and Genomics
• PUBH 8472 - Spatial Biostatistics

No PUBH 4xxx, 5xxx, or 6xxx level courses can be used as electives. 

To use a course not specifically listed here as an elective, please send a request to the Graduate Program Coordinator ([email protected]) that includes the syllabus or detailed list of the topics covered in the course, and details on how the course relates to your capstone project.  This information will be passed on to the Director of Graduate Studies for review.

Take 1 credit of the Data Science Colloquium (or equivalent in a participating department).

A colloquium course is mandatory and must appear on the student’s graduate degree plan form.

• DSCI 8970 - Data Science M.S. Colloquium 

  OR

• CSCI 8970 - Computer Science Colloquium

Take 3 or 6 credits for the capstone project

• DSCI 8760 - Data Science M.S. Plan B Project

One of the key features of the M.S. in Data Science curriculum is a capstone project that makes the theoretical knowledge gained in the program operational in realistic settings.

Under the supervision of a faculty member, students will go through the entire process of solving a real-world problem: from collecting and processing real-world data, to designing the best method to solve the problem, and finally, to implementing a solution. The problems and datasets that students engage with will come from real-world settings identical to what you might encounter in industry, academia, or government. Examples of projects and the wide variety of topics they cover can be found on the research page, though this is only a partial list. Data-driven capstone projects may also grow from temporary internships at companies, with DGS approval.

Once students have confirmed who their faculty advisor will be, they should request a permission number for the capstone course (DSCI 8760) through our Declaration of Advisor form. 

The Plan B project is completed under the guidance of a data science faculty member. Students are responsible for identifying and selecting their faculty advisor. Data Science M.S. students will present a final poster at the annual Poster Fair on their Plan B project as part of their degree requirements. The annual Poster Fair takes place every spring semester. Students should then complete their written report as soon as possible following their poster presentation. Questions on this process can be directed to the Graduate Program Coordinator.

Committee requirements

Your M.S. degree committee must consist of three University of Minnesota-Twin Cities faculty members with formal graduate education responsibilities. Two committee members must be from the Data Science Program, including your advisor who serves as committee chair, and one committee member from an outside program

A qualified advisor from outside the data science faculty may be selected with DGS approval. You may be asked to provide a CV for that potential advisor. Your final project report will be approved by a committee of three faculty including your advisor and including at least one member of the data science faculty. If not already on the data science faculty, your advisor may like to join (they should send a short CV to the DGS), otherwise you will need to find a current member acting as a co-advisor to approve the final report. In any case, the three committee members should represent at least two different home departments.  In other words, all three committee members cannot be from the same department. You will also be expected to give a short oral presentation on your project open to faculty, students, and other interested parties.