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.  As an MSDS student, you have the option of two district tracks: Applied Data Science or Computational and Analytical Data Science.  For either track, you are required to successfully complete 30 graduate-level credit hours.

Applied Data Science Track

Student following the applied Data Science Track will complete 18 credit hours of core coursework - including 6 credit hours in a domain area, 3 credit hours of project-based coursework, and 9 credit hours of electives.

Statistical Methods (3 credit hours)

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

Data Mining and Search (3 credit hours)

Select one course from the following:

  • CSCI-B 551 Elements of Artificial Intelligence
  • CSCI-B 555 Machine Learning
  • CSCI-B 565 Data Mining
  • CSCI-P 556 Applied Machine Learning
  • ENGR-E 511 Machine Learning for Signal Processing
  • ILS-Z 534 Search
  • INFO-I 606 Network Science

Data Management and Engineering  (3 credit hours)

Select one course from the following:

  • CSCI-B 561 Advanced Database Concepts
  • ENGR-E 516 Engineering Cloud Computing
  • INFO-I 535 Management, Access, and Use of Big and Complex Data

Data Visualization and Storytelling (3 credit hours)

Select one course from the following:

  • ENGR-E 583
  • ENGR-E 584 Scientific Visualization
  • INFO-I 590 Topics in Informatics
    • Topic: Data Visualization
  • STAT-S 670 Exploratory Data Analysis

Domain Data Science (6 credit hours)

Select two courses from the same category:

     Data Security and Privacy

  • INFO-I 520 Security for Networked Systems
  • INFO-I 525 Organizational Informatics and Economics of Security
  • INFO-I 533 Systems and Protocol Security and Information Assurance
  • INFO-I 536 Foundational Mathematics of Cybersecurity
  • INFO-I 538 Introduction to Cryptography

     Health and Biomedical Data Science

  •  CSCI-B 609 Topics in Algorithms and Computational Theory
    • Topic: Bioinformatics for Precision Medicine
  • INFO-I 507 Introduction to Health Informatics
  • INFO-I 519 Introduction to Bioinformatics
  • INFO-I 529 Machine Learning in Bioinformatics
  • INFO-I 590 Topics in Informatics
    • Topic: Data Science for Drug Discovery, Health and Translational Medicine
    • Topic: SNP Discovery and Population Genetics

     Human Robotic Interaction

  • CSCI-B 657 Computer Vision
  • ENGR-E 523 Internet of Things
  • ENGR-E 599 Topics in Intelligent Systems Engineering
    • Topic: Autonomous Robotics
  • INFO-I 527 Mobile and Pervasive Design
  • INFO-I 540 Human Robot Interaction
  • INFO-I 542 Foundations of HCI

     Social Data Science

  • ENGR-E 583 Information Visualization (may be counted only once)
  • ILS-Z 639 Social Media Mining
  • INFO-I 590 Topics in Informatics
    • Topic: Data and Society
    • Topic: Data Visualization (may be counted only once)
  • INFO-I 606 Network Science (may be counted only once)

Project (3 credit hours)

Select one course from the following:

  • DSCI-D 590 Topics in Data Science
    • Topic: Data Science in Practice
  • DSCI-D 699 Independent Study in Data Science

Electives (9 credit hours)

The remaining 9 credit hours are selected from courses above or additional data science-related course offerings of his or her choosing within the School of Informatics, Computing, and Engineering.

Big Data Systems Track

The Big Data Systems Track requires 18-21 credit hours of core coursework that covers 3 credit hours of Statistical Methods, 6 credit hours of AI and Machine Learning, 9 credit hours of Big Data, Cloud Computing, and Visualization. Core Engineering has 3 credit hours.  The remaining 9-12 credit hours are electives. This track is more hands-on and project-based than the other DS tracks.

Pre-requisites: Students in this program need to have a solid foundation in STEM course work, specifically the following:

  • Proficient level of programming experience in C, Java or Python;
  • Familiarity with R and MATLAB is useful;
  • Calculus I and II and basic understanding or probability and elements of discrete mat


Statistical Methods (3 credit hours)

Select one course from the following:

  • SPEA-V 506 Statistical Analysis for Effective Decision-making
  • STAT-S 520 Introduction to statistics

AI and Machine Learning for Engineering (6 credit hours)

Select two courses from the following:

  • CSCI-B 555 Machine Learning
  • CSCI-B 565 Data Mining
  • ENGR-E 511 Machine Learning for Signal Processing
  • ENGR-E 533 Deep Learning Systems
  • ENGR-E 635 Big Data Graph Analytics

Big Data, Cloud Computing, and Visualization (9 credit hours)

Select three courses from the following:

  • CSCI-B 561 Advanced Database Concepts
  • ENGR-E 516 Engineering Cloud Computing
  • ENGR-E 522 HPC and Cloud Computing for Large Scale Image Applications
  • ENGR-E 534 Big Data Applications
  • ENGR-E 583 Information Visualization
  • ENGR-E 584 Scientific Visualization
  • ENGR-E 616 Advance Cloud Computing
  • ENGR-E 623 Applied Streaming Data Systems

Core Engineering (3 credit hours)

Select one course from the following:

  • ENGR-E 503 Introduction to Intelligent Systems
  • ENGR-E 517 High Performance Computing
  • ENGR-E 523 Internet of Things
  • ENGR-E 535 Image Processing for Medical Applications
  • ENGR-E 551 Simulating Nanoscale Systems

Elective Courses (9-12 credit hours)

The remaining 9-12 credit hours can be selected from courses below or additional data science-related course offerings of his or her choosing within the Luddy School of Informatics, Computing, and Engineering

Select one or two courses from the following:

  • ENGR-E 788 Master's Thessis (3 credit hours of projct and 3 hours or research)

 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. Students following the Computational and Analytical Track will complete 15 credit hours of core coursework - comprised of Data Systems, Algorithmic Foundations, Data Analytics, and Big Data Infrastructures, as well as 15 credit hours of electives.

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 unselected courses above or additional data science-related offerings of his or her choosing within the School of Informatics, Computing, and Engineering. A course in data ethics or a major project is highly encouraged, but not required.

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