Campuses:
Twin Cities Campus
Data Science M.S.Computer Science and Engineering
College of Science and Engineering
Link to a list of faculty for this program.
Contact Information
Data Science Graduate Program, Department of Computer Science and Engineering, University of Minnesota, 4192 Keller Hall, 200 Union Street S.E., Minneapolis, MN 55455 (612 6254002; fax: 6126250572).
Email:
csgradmn@umn.edu
Website: https://cse.umn.edu/datascience
Along with the programspecific requirements listed below, please read the
General Information section of
this
website for requirements that apply to all major fields.
The Data Science MS program provides a strong foundation in the science of Big Data and its analysis by gathering in a single program the knowledge, expertise, and educational assets in data collection and management, data analytics, scalable datadriven pattern discovery, and the fundamental concepts behind these methods.
Students who graduate from this regular 2year master's program will learn the stateoftheart methods for treating Big Data, be exposed to the cuttingedge methods and theory forming the basis for the next generation of Big Data technology, and will complete a project demonstrating that they can use the fundamental concepts to design innovative methods for new application areas arising from business, government, security, medicine, biology, physical sciences, and the environment.
Program Delivery
Prerequisites for Admission
The preferred undergraduate GPA for admittance to the program
is 3.00.
A bachelor's degree from an accredited college or university in computer science, math, statistics, engineering, natural sciences, or a related field.
Other requirements to be completed before admission:
The undergraduate degree must include statistics, calculus, multivariable calculus, linear algebra, and mathematical software environments such as Matlab or R or the equivalent, programming languages such as C+, C++, Java, programming experience including algorithms and data structures normally taught in beginning computer science courses either as part of the undergraduate degree or subsequent work experience.
Special Application Requirements:
The application deadline is March 1.
Applicants are only considered for fall admission and decisions are made after all applications are received following the close of the application cycle.
GRE test scores are not required, but are recommended for those applying from international institutions. If submitted, the GRE is only one of many factors considered for admission, and no score will guarantee or preclude admission. Applications without the GRE will be considered based on the material submitted.
International applicants must submit score(s) from one of the following tests:
Key to test
abbreviations
(TOEFL, IELTS, MELAB).
For an online application or for more information about graduate education admissions, see the
General Information section of this
website.
Program Requirements
Plan B: Plan B requires
31
major credits and
0
credits outside the major.
The final exam is written and oral.
A capstone project is required.
Capstone Project:Students must complete 3 credit hours of DSCI 8760 (capstone project) under the supervision of a faculty member.
This program may be completed with a minor.
Use of 4xxx courses towards program requirements is not permitted.
A minimum GPA of 3.25
is required for students to remain in good standing.
Courses offered on both the AF and S/N grading basis must be taken AF.
At least 3 8xxxlevel credits, either from an emphasis or an elective, are required.
Complete a presentation at the Data Science Poster Fair for Plan B project as part of degree requirements in the semester of anticipated graduation. Consult with the advisor on an appropriate timeline to present.
Statistics (6 credits)
Statistics Tier I (3 to 6 credits)
Select at least 3 credits from the following in consultation with the advisor:
PUBH 7401  Fundamentals of Biostatistical Inference
(4.0 cr)
PUBH 7402  Biostatistics Modeling and Methods
(4.0 cr)
PUBH 7440  Introduction to Bayesian Analysis
(3.0 cr)
STAT 5102  Theory of Statistics II
(4.0 cr)
STAT 5302  Applied Regression Analysis
(4.0 cr)
STAT 5401  Applied Multivariate Methods
(3.0 cr)
STAT 5511  Time Series Analysis
(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)
MATH 5651  Basic Theory of Probability and Statistics
(4.0 cr)
or
STAT 5101  Theory of Statistics I
(4.0 cr)
Statistics Tier II (0 to 3 credits)
Select credits from the following, in consultation with the advisor, as needed to meet the 6credit Statistics requirement:
EE 5531  Probability and Stochastic Processes
(3.0 cr)
EE 8581  Detection and Estimation Theory
(3.0 cr)
PUBH 7405  Biostatistical Inference I
(4.0 cr)
PUBH 7406  Biostatistical Inference II
(3.0 cr)
PUBH 7407  Analysis of Categorical Data
(3.0 cr)
PUBH 7430  Statistical Methods for Correlated Data
(3.0 cr)
PUBH 7460  Advanced Statistical Computing
(3.0 cr)
PUBH 7485  Methods for Causal Inference
(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 5052  Statistical and Machine Learning
(3.0 cr)
STAT 5201  Sampling Methodology in Finite Populations
(3.0 cr)
STAT 5303  Designing Experiments
(4.0 cr)
STAT 5421  Analysis of Categorical Data
(3.0 cr)
STAT 5601  Nonparametric Methods
(3.0 cr)
STAT 5701  Statistical Computing
(3.0 cr)
STAT 8112  Mathematical Statistics II
(3.0 cr)
Algorithmics (6 credits)
Algorithmics Tier I (3 to 6 credits)
Select at least 3 credits from the following in consultation with the advisor:
CSCI 5521  Machine Learning Fundamentals
(3.0 cr)
CSCI 5523  Introduction to Data Mining
(3.0 cr)
CSCI 5525  Machine Learning: Analysis and Methods
(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)
Algorithmics Tier II (0 to 3 credits)
Select credits from the following, in consultation with the advisor, as needed to meet the 6credit Algorithmics requirement:
CSCI 5302  Analysis of Numerical Algorithms
(3.0 cr)
CSCI 5304  Computational Aspects of Matrix Theory
(3.0 cr)
CSCI 5511  Artificial Intelligence I
(3.0 cr)
CSCI 5512  Artificial Intelligence II
(3.0 cr)
CSCI 5609  Visualization
(3.0 cr)
CSCI 8314  Sparse Matrix Computations
(3.0 cr)
CSCI 8581  Big Data in Astrophysics
(4.0 cr)
EE 5239  Introduction to Nonlinear Optimization
(3.0 cr)
EE 5251  Optimal Filtering and Estimation
(3.0 cr)
EE 5389  Introduction to Predictive Learning
(3.0 cr)
EE 5391  Computing With Neural Networks
(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)
IE 5531  Engineering Optimization I
(4.0 cr)
IE 8521  Optimization
(4.0 cr)
IE 8531  Discrete Optimization
(4.0 cr)
Infrastructure and LargeScale Computing (6 credits)
Infrastructure and LargeScale Computing Tier I (3 to 6 credits)
Select at least 3 credits from the following in consultation with the advisor:
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)
CSCI 8205  Parallel Computer Organization
(3.0 cr)
or
EE 8367  Parallel Computer Organization
(3.0 cr)
Infrastructure and LargeScale Computing Tier II (0 to 3 credits)
Select credits from the following, in consultation with the advisor, as needed to complete the 6credit Infrastructure and LargeScale Computing requirement.
CSCI 5103  Operating Systems
(3.0 cr)
CSCI 5211  Data Communications and Computer Networks
(3.0 cr)
CSCI 5231 {Inactive}
(3.0 cr)
CSCI 5271  Introduction to Computer Security
(3.0 cr)
CSCI 5715  From GPS, Google Maps, and Uber to Spatial Data Science
(3.0 cr)
CSCI 5751  Big Data Engineering and Architecture
(3.0 cr)
CSCI 5801  Software Engineering I
(3.0 cr)
CSCI 5802  Software Engineering II
(3.0 cr)
CSCI 8102  Foundations of Distributed Computing
(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 8735  Advanced Database Systems
(3.0 cr)
CSCI 8801  Advanced Software Engineering
(3.0 cr)
EE 5355  Algorithmic Techniques for Scalable Manycore Computing
(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)
Electives (9 credits)
Select 9 credits from the following in consultation with the advisor. Courses from above lists that are not applied to other requirements can be selected with advisor approval. Other electives may be selected in consultation with the advisor and director of graduate studies. If 3 credits of DSCI 8760 have already been taken in a semester an additional 3 credits in a subsequent semester can be used towards elective coursework after consultation with the advisor.
CSCI 5106  Programming Languages
(3.0 cr)
CSCI 5123  Recommender Systems
(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 5561  Computer Vision
(3.0 cr)
CSCI 5980  Special Topics in Computer Science
(1.03.0 cr)
CSCI 8271  Security and Privacy in Computing
(3.0 cr)
CSCI 8363  Numerical Linear Algebra in Data Exploration
(3.0 cr)
CSCI 8980  Special Advanced Topics in Computer Science
(1.03.0 cr)
DSCI 8760  Data Science M.S. Plan B Project
(3.0 cr)
EE 5393  Circuits, Computation, and Biology
(3.0 cr)
IE 8534  Advanced Topics in Operations Research
(1.04.0 cr)
IE 8535  Introduction to Network Science
(4.0 cr)
MATH 5467  Introduction to the Mathematics of Image and Data Analysis
(4.0 cr)
PUBH 7445  Statistics for Human Genetics and Molecular Biology
(3.0 cr)
PUBH 7461  Exploring and Visualizing Data in R
(2.0 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)
Capstone Course (3 credits)
Take the following in consultation with the advisor:
DSCI 8760  Data Science M.S. Plan B Project
(3.0 cr)


Credits:  4.0 [max 4.0] 
Typically offered:  Every Fall 
Credits:  4.0 [max 4.0] 
Typically offered:  Every Spring 
Credits:  3.0 [max 3.0] 
Typically offered:  Every Spring 
Credits:  4.0 [max 4.0] 
Typically offered:  Every Fall & Spring 
Credits:  4.0 [max 4.0] 
Typically offered:  Every Fall, Spring & Summer 
Credits:  3.0 [max 3.0] 
Typically offered:  Periodic Fall 
Credits:  3.0 [max 3.0] 
Typically offered:  Every Fall 
Credits:  3.0 [max 3.0] 
Grading Basis:  AF or Aud 
Typically offered:  Every Fall 
Credits:  3.0 [max 3.0] 
Typically offered:  Every Fall 
Credits:  3.0 [max 3.0] 
Typically offered:  Every Spring 
Credits:  4.0 [max 4.0] 
Course Equivalencies:  Math 5651/Stat 5101 
Typically offered:  Every Fall & Spring 
Credits:  4.0 [max 4.0] 
Typically offered:  Every Fall 
Credits:  3.0 [max 3.0] 
Typically offered:  Every Fall 
Credits:  3.0 [max 3.0] 
Typically offered:  Periodic Spring 
Credits:  4.0 [max 4.0] 
Typically offered:  Every Fall 
Credits:  3.0 [max 4.0] 
Typically offered:  Every Spring 
Credits:  3.0 [max 3.0] 
Typically offered:  Every Spring 
Credits:  3.0 [max 3.0] 
Typically offered:  Every Fall 
Credits:  3.0 [max 3.0] 
Typically offered:  Every Fall 
Credits:  3.0 [max 3.0] 
Typically offered:  Every Fall 
Credits:  4.0 [max 4.0] 
Typically offered:  Every Fall 
Credits:  3.0 [max 3.0] 
Typically offered:  Every Fall 
Credits:  3.0 [max 3.0] 
Typically offered:  Every Spring 
Credits:  3.0 [max 4.0] 
Grading Basis:  AF only 
Typically offered:  Every Fall 
Credits:  3.0 [max 3.0] 
Typically offered:  Every Spring 
Credits:  4.0 [max 4.0] 
Typically offered:  Every Fall, Spring & Summer 
Credits:  3.0 [max 3.0] 
Typically offered:  Every Fall & Spring 
Credits:  3.0 [max 3.0] 
Typically offered:  Every Fall & Spring 
Credits:  3.0 [max 3.0] 
Prerequisites:  (Stat 5102 or Stat 8102) and (Stat 5302 or STAT 8051) or consent 
Grading Basis:  AF or Aud 
Typically offered:  Every Fall 
Credits:  3.0 [max 3.0] 
Typically offered:  Every Spring 
Credits:  4.0 [max 4.0] 
Course Equivalencies:  Ast 5731/Stat 5731 
Grading Basis:  AF only 
Typically offered:  Every Fall 
Credits:  4.0 [max 4.0] 
Course Equivalencies:  Ast 5731/Stat 5731 
Grading Basis:  AF only 
Typically offered:  Every Fall 
Credits:  3.0 [max 3.0] 
Typically offered:  Periodic Fall 
Credits:  3.0 [max 3.0] 
Typically offered:  Periodic Fall & Spring 
Credits:  3.0 [max 3.0] 
Typically offered:  Fall Even Year 
Credits:  3.0 [max 3.0] 
Typically offered:  Fall Even Year 
Credits:  3.0 [max 3.0] 
Typically offered:  Periodic Spring 
Credits:  3.0 [max 3.0] 
Course Equivalencies:  PubH 7475/PubH 8475/Stat 8056 
Typically offered:  Periodic Spring 
Credits:  3.0 [max 3.0] 
Typically offered:  Every Spring 
Credits:  3.0 [max 3.0] 
Typically offered:  Every Fall 
Credits:  3.0 [max 3.0] 
Course Equivalencies:  CSci 4511W/CSci 5511 
Prerequisites:  [2041 or #], grad student 
Typically offered:  Every Fall 
Credits:  3.0 [max 3.0] 
Course Equivalencies:  CSci 5512W/CSci 5512 
Typically offered:  Every Spring 
Credits:  3.0 [max 3.0] 
Typically offered:  Fall Even Year 
Credits:  3.0 [max 3.0] 
Typically offered:  Periodic Spring 
Credits:  4.0 [max 4.0] 
Course Equivalencies:  Ast 8581/CSci 8581/Phys 8581 
Grading Basis:  AF only 
Typically offered:  Every Spring 
Credits:  3.0 [max 3.0] 
Typically offered:  Periodic Fall & Spring 
Credits:  3.0 [max 3.0] 
Course Equivalencies:  AEM 5451/EE 5251 
Typically offered:  Every Fall 
Credits:  3.0 [max 3.0] 
Course Equivalencies:  EE 4389W/EE 5389 
Typically offered:  Fall Even Year 
Credits:  3.0 [max 3.0] 
Typically offered:  Periodic Fall & Spring 
Credits:  3.0 [max 3.0] 
Typically offered:  Periodic Fall & Spring 
Credits:  3.0 [max 3.0] 
Typically offered:  Periodic Fall & Spring 
Credits:  3.0 [max 3.0] 
Course Equivalencies:  EE 5561/EE 8541 
Typically offered:  Every Spring 
Credits:  3.0 [max 3.0] 
Typically offered:  Fall Even Year 
Credits:  3.0 [max 3.0] 
Typically offered:  Periodic Fall & Spring 
Credits:  3.0 [max 3.0] 
Typically offered:  Periodic Fall 
Credits:  4.0 [max 4.0] 
Typically offered:  Every Fall 
Credits:  4.0 [max 4.0] 
Typically offered:  Every Fall 
Credits:  4.0 [max 8.0] 
Typically offered:  Every Fall & Spring 
Credits:  3.0 [max 3.0] 
Typically offered:  Periodic Spring 
Credits:  3.0 [max 3.0] 
Typically offered:  Every Spring 
Credits:  3.0 [max 3.0] 
Course Equivalencies:  CSci 4707/CSci 5707/INET 4707 
Typically offered:  Every Fall 
Credits:  3.0 [max 3.0] 
Typically offered:  Every Spring 
Credits:  3.0 [max 3.0] 
Typically offered:  Every Fall 
Credits:  3.0 [max 3.0] 
Course Equivalencies:  CSci 8205/EE 8367 
Typically offered:  Every Spring 
Credits:  3.0 [max 3.0] 
Course Equivalencies:  CSci 8205/EE 8367 
Typically offered:  Every Spring 
Credits:  3.0 [max 3.0] 
Typically offered:  Every Fall 
Credits:  3.0 [max 3.0] 
Course Equivalencies:  CSci 4211/CSci 5211/INET 4002 
Typically offered:  Every Fall 
Credits:  3.0 [max 3.0] 
Typically offered:  Every Fall 
Credits:  3.0 [max 3.0] 
Typically offered:  Spring Even Year 
Credits:  3.0 [max 3.0] 
Typically offered:  Every Fall 
Credits:  3.0 [max 3.0] 
Prerequisites:  2041 or # 
Typically offered:  Every Fall 
Credits:  3.0 [max 3.0] 
Typically offered:  Periodic Spring 
Credits:  3.0 [max 3.0] 
Typically offered:  Periodic Spring 
Credits:  3.0 [max 3.0] 
Typically offered:  Periodic Fall & Spring 
Credits:  3.0 [max 3.0] 
Typically offered:  Periodic Fall & Spring 
Credits:  3.0 [max 3.0] 
Typically offered:  Periodic Spring 
Credits:  3.0 [max 3.0] 
Grading Basis:  AF or Aud 
Typically offered:  Periodic Fall 
Credits:  3.0 [max 3.0] 
Typically offered:  Periodic Fall & Spring 
Credits:  3.0 [max 3.0] 
Typically offered:  Spring Odd Year 
Credits:  3.0 [max 3.0] 
Course Equivalencies:  EE 5371/5863 
Typically offered:  Periodic Fall & Spring 
Credits:  3.0 [max 3.0] 
Typically offered:  Periodic Fall & Spring 
Credits:  3.0 [max 3.0] 
Typically offered:  Every Fall 
Credits:  3.0 [max 3.0] 
Typically offered:  Every Fall 
Credits:  3.0 [max 3.0] 
Typically offered:  Fall Odd Year 
Credits:  3.0 [max 3.0] 
Typically offered:  Every Fall & Spring 
Credits:  3.0 [max 3.0] 
Typically offered:  Every Spring 
Credits:  3.0 [max 3.0] 
Typically offered:  Every Spring 
Credits:  1.0 3.0 [max 27.0] 
Typically offered:  Periodic Fall & Spring 
Credits:  3.0 [max 3.0] 
Grading Basis:  AF or Aud 
Typically offered:  Periodic Fall 
Credits:  3.0 [max 3.0] 
Typically offered:  Periodic Spring 
Credits:  1.0 3.0 [max 27.0] 
Typically offered:  Every Fall & Spring 
Credits:  3.0 [max 6.0] 
Grading Basis:  SN only 
Typically offered:  Every Fall, Spring & Summer 
Credits:  3.0 [max 3.0] 
Typically offered:  Periodic Fall & Spring 
Credits:  1.0 4.0 [max 8.0] 
Typically offered:  Every Fall & Spring 
Credits:  4.0 [max 4.0] 
Typically offered:  Every Fall 
Credits:  4.0 [max 4.0] 
Typically offered:  Every Spring 
Credits:  3.0 [max 3.0] 
Typically offered:  Every Spring 
Credits:  2.0 [max 2.0] 
Typically offered:  Every Fall 
Credits:  3.0 [max 3.0] 
Course Equivalencies:  PubH 7445/PubH 8445 
Typically offered:  Fall Odd Year 
Credits:  3.0 [max 3.0] 
Typically offered:  Every Spring 
Credits:  3.0 [max 3.0] 
Typically offered:  Periodic Fall & Spring 
Credits:  1.0 [max 1.0] 
Grading Basis:  SN or Aud 
Typically offered:  Every Fall & Spring 
Credits:  1.0 [max 1.0] 
Grading Basis:  SN or Aud 
Typically offered:  Every Fall 
Credits:  3.0 [max 6.0] 
Grading Basis:  SN only 
Typically offered:  Every Fall, Spring & Summer 