Graduate Programs

Degree Programs

Master of Data Science

The M.S. in Data Science (MSDS) is a two-year residential program offering multidisciplinary coursework in computer science, information science, informatics, statistics, engineering, and other disciplines. It prepares students to pursue a data science related career or admission to a Ph.D. program.  In addition, MSDS students may select to specialize in the Computational and Analytical Data Science track.

Curriculum

Students are required to complete 30 credit hours of graduate-level coursework for this degree. Individual program choices will vary. Students pursuing the program will develop expertise in four areas (15 credit hours):

Statistics (3 credit hours)

Select one course from the following:

  • SPEA-V 506 Statistical Analysis for Effective Decision-making
  • STAT-S 520 Introduction to Statistics
    • Higher level statistics course may be taken with departmental approval

Machine Learning, Data Mining, Text Mining (6 credit hours)

Select two courses from the following:

  • CSCI-B 505 Applied Algorithms
  • CSCI-B 551 Elements of Artificial Intelligence
  • CSCI-B 555 Machine Learning
  • CSCI-B 565 Data Mining
  • CSCI-B 657 Computer Vision
  • CSCI-P 556 Applied Machine Learning
  • ILS-Z 534 Search
  • INFO-I 606 Network Science

Data Engineering and Stewardship (3 credit hours)

Select one course from the following:

  • ENGR-E 516 Engineering Cloud Computing
  • ENGR-E 517 High Performance Computing
  • INFO-I 523 Big Data Applications and Analytics
  • INFO-I 524 Big Data Software and Projects
  • INFO-I 535 Management, Access, and Use of Big and Complex Data

Visualization and Storytelling (3 credit hours)

Select one course from the following:

  • ENGR-E 583/ILS-Z 637 Information Visualization
  • ENGR-E 584 Scientific Visualization
  • INFO-I 590 Topics in Informatics (Topic: Data Visualization)
  • INFO-I 590 Topics in Informatics (Topic: Data and Society)

The remaining 15 credit hours are selected from courses above or additional data science-related course offerings.  In consultation with a Data Science faculty advisor, students may choose to pursue an independent study or relevant internship opportunity that blends the learning in data science to a major project or a custom specialization. Be creative in your course strategies.

MS in Data Science - Computational and Analytical Track

Students with a strong computer science background wishing to drive deeper into the mechanics of data science methodologies may wish to pursue a more rigorous curriculum.  For those pursuing the Computational and Analytics (C&A) track, students must pursue more technical and theoretical coursework in four areas (15 credit hours):

Data Systems Foundation (3 credit hours)

  • CSCI-B 561 Advanced Database Concepts

Algorithmic Foundation (3 credit hours)

Select one course from the following:

  • CSCI-B 503 Algorithms Design and Analysis
  • CSCI-B 505 Applied Algorithms
  • CSCI-B 609 Topics in Algorithms and Computing Theory (Topic: Foundations in Data Science)

Data Analytics Foundation (6 credit hours)

  • STAT-S 520 Introduction to Statistics
    • Higher level statistics course may be taken with departmental approval

Select one additional course from the following:

  • CSCI-B 555 Machine Learning
  • CSCI-B 565 Data Mining

Big Data Infrastructure (3 credit hours)

Select one course from the following:

  • ENGR-E 516 Engineering Cloud Computing
  • INFO-I 535 Management, Access and Use of Big and Complex Data

The remaining 15 credit hours are selected from a wide range of data science-related course offerings. A course in data ethics or a major project is highly encouraged. Students are encouraged to seek advice on their individual study plans with the assistance of a Computational & Analytical Track Director.

Academic Bulletins