Graduate Programs

Degree Programs

Master of Data Science Online

The M.S. in Data Science - Online (MSDSO) aims to enhance data skills for managers, practitioners, and domain scientists.  Due to its asynchronous format, this program is fully online with no residency requirements. Students have up to - but no more than - five (5) years to complete the degree requirements through part-time or full-time enrollment.


Students in this distance education program need to have programming experience in Python and R, as well as basic math (probability, linear algebra, calculus).

Students who lack the above prerequisites are encouraged to reach out to the Luddy Office of Online Education for recommendations.


Students are required to complete 30 credit hours of graduate-level coursework for this degree. Students are required to complete two core courses (6 credit hours), two courses to form a specialization (6 credit hours), and a capstone project (3 credit hours). The remaining 15 credit hours are counted as electives, selected to best suit individual interests, needs, and overall career goals.

Students may transfer no more than nine (9) graduate-level credit hours, with grades of B or higher, to the program from another institution or university.  These credits may not have already been utilized to confer a prior graduate program or graduate certificate; the only exception is those who previously completed the Graduate Certificate in Data Science.

Statistics (3 credit hours)

Select one course from the following:

  • SPCN-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 and Artificial Intelligence (3 credit hours)

Select one course from the following:
  • CSCI-B 551 Elements of Artificial Intelligence
  • ENGR-E 511 Machine Learning for Signal Processing
  • ENGR-E 533 Deep Learning Systems
  • INFO-I 526 Applied Machine Learning

Data Science Application Area (6 credit hours)

Students must select one of the following application areas and complete two courses within that specific area:

Data Analytics and Visualization

  • DSCI-D 590 Topics in Data Science
    • Topic: Introduction to Business Analytics Modeling
    • Topic: Data Visualization
  • ENGR-E 516 Engineering Cloud Computing
  • ENGR-E 533 Deep Learning Systems (if not used as core)
  • ENGR-E 534 Big Data Applications
  • ENGR-E 583 Information Visualization
  • ENGR-E 616 Advanced Cloud Computing
  • ILS-Z 534 Search
  • INFO-I 535 Management, Access, and Use of Big and Complex Data
  • INFO-I 606 Network Science

Intelligent Systems Engineering

  • ENGR-E 523 Internet of Things
  • ENGR-E 599 Topics in Intelligent Systems Engineering 
    • Topic: Autonomous Robotics

Precision Health

  • DSCI-D 590 Topics in Data Science
    • Topic: Data Science for Drug Discovery, Health and Translational Medicine
    • Topic: Real World Data Science
  • ENGR-E 541 Simulating Cancer as an Intelligent System
  • INFO-I 529 Machine Learning in Bioinformatics


  • INFO-I 520 Security in Networked Systems
  • INFO-I 525 Organizational Informatics and Economics of Security
  • INFO-I 533 Systems and Protocol Security and Information Assurance

Data Science Capstone (3 credit hours)
Students will be required to work on a project that applies the knowledge and skills learned to solve real-world problems for a company, organization, or individual. This may be fulfilled through a capstone course or an independent study project. The aim of this requirement is to demonstrate students' capabilities to prospective employers and inspire innovation.

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

Electives (15 credit hours)

The remaining 15 credit hours are selected from courses listed above or additional data science-related course offerings listed below. Students may not earn credit for courses taken to fulfill core, application, or capstone requirements.

  • CSCI-B 505 Applied Algorithms
  • CSCI-B 561 Advanced Database Concepts
  • CSCI-B 657 Computer Vision
  • DSCI-D 590 Topics in Data Science
    • Topic: Advanced Data Science On-Ramp **
    • Topic: Applied Data Science
    • Topic: Basic Data Science On-Ramp **
    • Topic: Introduction to NLP for Data Science
    • Topic: Introduction to  Python Programming
    • Topic: SQL and NoSQL
    • Topic: Time Series Analysis
  • DSCI-D 591 Graduate Internship *
  • ENGR-E 517 High Performance Computing
  • ILS-Z 636 Data Semantics
  • ILS-Z 639 Social Media Mining
  • SPCN-P 507 Data Analysis and Modeling for Public Affairs
  • STAT-S 681 Topics in Applied Statistics
    • Topic: Introduction to Regression Models and Nonparametrics

(*) No more than three (3) credit hours may be earned
(**) No more than three (3) credit hours combined may be earned in Basic and Advanced Data Science On-Ramp



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