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 are required to complete 30 credit hours of graduate-level coursework for this degree.

Prerequisites

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.

Curriculum

Students are required to complete 6 credit hours of core coursework that covers 3 credit hours of Statistical Methods, and 3 credit hours of Machine Learning and Artificial Intelligence.  Students will specialize in 6 credit hours of a Data Science Domain. The remaining 18 credit hours are 3 credit hours of capstone project and 15 credit hours of electives, selected to best suit individual interests, needs, and overall career goals.

Students may transfer no more than 9 graduate-level credit hours, with grades of B or higher, to the program from another institution or university.  These credits may not have previously been utilized to award another degree or certificate; the only exception is those who previously completed the Graduate Certificate in Data Science that is comprised of 12 credit hours, in which up to a total of 21 credit hours may be transferred.

Statistical Methods (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
  • CSCI-P 556 Applied Machine Learning
  • ENGR-E 511 Machine Learning for Signal Processing
  • ENGR-E 533 Deep Learning Systems (may be counted only once)

Data Science Domain (6 credit hours)

Students must select one of the following domains and complete two courses within that specific domain:

Data Analytics and Visualization

  • DSCI-D 590 Topics in Data Science
    • Topic: Optimization and Simulation for Business Analytics
    • Topic: Data Visualization
  • ENGR-E 534 Big Data Applications
  • ENGR-E 583 Information Visualization
  • 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 516 Engineering Cloud Computing
  • ENGR-E 517 High Performance Computing
  • ENGR-E 533 Deep Learning Systems (may be counted only once)
  • ENGR-E 599 Topics in Intelligent Systems Engineering
    • Topic: Autonomous Robotics

Cybersecurity

  • 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

Capstone Project (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: Faculty Assistance in Data Science
  • DSCI-D 592 Data Science in Practice
  • DSCI-D 699 Independent Study in Data Science 

Electives (15 credit hours)

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

  • CSCI-B 505 Applied Algorithms
  • CSCI-B 561 Advanced Database Concepts
  • CSCI-B 657 Computer Vision
  • DSCI-D 532 Applied Database Technologies
  • DSCI-D 590 Topics in Data Science
    • Topic: Applied Data Science
    • Topic: Data Science On-Ramp **
    • Topic: Introduction to NLP for Data Science
    • Topic: Introduction to  Python Programming
    • Topic: Time Series Analysis
  • DSCI-D 591 Graduate Internship *
  • INFO-I 529 Machine Learning in Bioinformatics
  • 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 3 credit hours  of DSCI-D 591 may be earned
(**) No more than three (3) credit hours of DSCI-D 590, Data Science On-Ramp, may be earned

 

 

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