Data Science Master's Degree Curriculum Overview

The data science master’s degree program is an interdisciplinary program between the Department of Mathematical Sciences, the Department of Computer Science, and the Department of Electrical and Computer Engineering. The program offers an extensive list of interdisciplinary electives which allows students to focus their degree toward business application, industry, or academia, as well as four concentrations. Students can optionally work on a master’s thesis for six credits as part of their electives.

By the end of this program, students will be able to:

  • Analyze data with state-of-the-art modeling techniques with both high interpretability and prediction power
  • Effectively communicate analysis findings to non-experts
  • Construct new relevant models and design algorithms tailored for challenging for real-life situations
  • Interpret model outputs, infer, and make relevant decisions pertaining to applications
  • Manage the collection, storage and exploration of data of large sizes and of various nature, with an emphasis on privacy issues
  • Design and implement efficient distributed systems tailored for challenging real-life applications
  • Assess the vulnerabilities of a network, detect threats, and enforce appropriate security measures
  • Design, deploy and grow business intelligence systems tailored for challenging real-life applications
  • Interpret model outputs, infer, and make relevant decisions pertaining to business and industry-related questions

Optional Concentrations Include:

  • Fundamentals of Data Science
  • Data Acquisition and Management
  • Data Security
  • Business Applications

Degree Requirements: 

The Master of Science in Data Science requires 30 graduate credits in an approved plan of study, consisting of five core courses and five electives. Electives are to be chosen among the courses listed below. The choice of electives can lead to a concentration, which is optional. If a student chooses a concentration, at least three courses (9 credits) must be from the electives available in that concentration. If a student does not choose a concentration, then electives can be freely selected from all lists below. Also, admission to the Master program may be conditional on passing two particular courses (MA 540 Introduction to Probability Theory and MA 547 Advanced Calculus I) during the first semester, in which case these two courses count as two electives towards the degree and only three electives are left to be chosen among the courses listed below.

Core Courses Include:

  • MA 541 Statistical Methods
  • CS 583 Deep Learning
  • MA 630 Advanced Optimization Methods
  • MA 661 Dynamic Programming and Reinforcement Learning
  • CPE 695 Applied Machine Learning

MA 612 Mathematical Statistics or MA 701 Statistical Inference can be taken instead of MA 541 given sufficient preparation.

General Electives: 

Students may choose MA 900 Master of Science Thesis for six credits as one of their electives to work on a specific project with an advisor. The approval of the program coordinator is required for enrollment in MA 900.

Students may choose one (and only one) of the following as one of their electives: 

  • CS 570 Introduction to Programming, Data Structures and Algorithms
  • EE 551 Engineering Programming: Python

Fundamentals of Data Science Electives:

  • MA 544 Numerical Linear Algebra for Big Data
  • MA 641 Time Series Analysis I
  • MA 613 Spatial and Spatio-Temporal Statistical Modeling
  • MA 617 Tensor Methods in Data Science
  • MA 620 Introduction to Networks and Graph Theory
  • MA 623 Stochastic Processes
  • MA 622 Stochastic Optimization
  • MA 720 Multivariate Statistics
  • CPE 646 Pattern Recognition and Classification
  • CS 584 Natural Language Processing
  • CS 601 Algorithmic Complexity

Data Acquisition and Management Electives:

  • CS 526 Enterprise and Cloud Computing
  • CS 549 Distributed Systems and Cloud Computing
  • CS 561 Database Management Systems I
  • CS 562 Database Management Systems II
  • CS 609 Data Management and Exploration on the Web
  • EE 627 Data Acquisition and Processing I
  • EE 628 Data Acquisition and Processing II
  • CS 696 Database Security

Data Security Electives:

  • CS 573 Fundamentals of Cybersecurity
  • CS 503/MA 503* Discrete Mathematics for Cryptography
  • CS 579/CPE 579* Foundations of Cryptography
  • CS 578* Privacy in a Networked World
  • CS 594 Enterprise Security Information Assurance
  • CS 696 Database Security
  • CS 595 Information Security and the Law
  • CPE 691 Information Systems Security

*These three courses must be taken in the sequence CS 503 - CS 579 - CS 578

Business Applications:

  • CS 526 Enterprise and Cloud Computing
  • BIA 660 Web Analytics
  • BIA 662 Cognitive Computing
  • BIA 672 Marketing Analytics
  • BIA 674 Supply Chain Analytics
  • BIA 676 Data Streams Analytics: Internet of Things
  • BIA 678 Big Data Technologies Seminar
  • FE 555 2D Data Visualization Programming for Financial Applications
  • MIS 636 Data Warehousing and Business Intelligence

If needed, students may consider taking short programming courses offered by the School of Business, such as FE 515 Introduction to R, FE 516 MATLAB for Finance, and FE 520 Introduction to Python for Financial Applications (1 credit, not counting towards MSDS).

LEARN MORE ABOUT GENERAL REQUIREMENTS →

If you have existing graduate credits or experience in this area of study, contact [email protected] to discuss opportunities to include it in the curriculum.