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Master of Science in Data Science

The Master of Science in Data Science provides the theoretical knowledge and practical skills required for dealing with the contemporary collection, exploration, analysis, and modeling of data along with the related challenges pertaining to inference and prediction. Emphasis is put on the mathematical foundations underpinning state-of-the-art methodologies: 

  • Probability Theory

  • Statistical modeling

  • Numerical methods

  • Optimization

  • Markov decision processes for statistical learning

The core courses cover generally applicable theory and methods in a self-sufficient framework, while elective courses can tailor particular paths towards industry and business applications, or towards an academic career. The choice of electives can lead to a concentration, which is optional. 

Concentrations

  • Fundamentals of Data Science

  • Data Acquisition and Management

  • Data Security

  • Business Applications

Program Objectives

The program prepares students to:

  • know the principles of the collection, storage, exploration, and analysis of data.

  • master methods of inference, prediction and decision-making based on data, including complex structures.

  • recognize the complexity of data and the assumptions underlying modeling strategies in real-life applications.

  • learn efficient computational techniques for large-scale (big) data.

Program Outcomes

Program Educational Outcomes common to all concentrations:

  • Analyze data with state-of-the-art modeling techniques with both high interpretability and prediction power.

  • Effectively communicate analysis findings to non-experts.

Program Outcomes specific to the concentration in Fundamentals of Data Science:

  • Construct new relevant models and design algorithms tailored for challenging real-life situations.

  • Interpret model outputs, infer, and make relevant decisions pertaining to applications.

Program Outcomes specific to the concentration in Data Acquisition and Management:

  • Manage the collection, storage and exploration of data of large sizes and of various nature.

  • Design and implement efficient distributed systems tailored for challenging real-life applications.

Program Outcomes specific to the concentration in Data Security:

  • Manage the collection and storage of data of large sizes and of various nature, with an emphasis on privacy issues.

  • Assess the vulnerabilities of a network, detect threats, and enforce appropriate security measures.

Program Outcomes specific to the concentration in Business Applications

  • 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.


Degree Requirements

The program is a 30-credit degree program. Students are required to complete:

  • 5 core courses for 15 credits

  • 5 electives for 15 credits:

    • 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.

    • Either CS 570 or EE 551


Choosing a concentration is not a requirement. Students that do choose a concentration must then select at least 9 credits out of the 15 electives from the corresponding list.

Core Courses

15 credits totaled over the following 5 core courses.

MA 541Statistical Methods

3
CPE 695Applied Machine Learning

3
CS 583Deep Learning

3

MA 576Optimization for Data Science

3
Or
MA 630Advanced Optimization Methods

3

MA 544Numerical Linear Algebra for Big Data

3
Or
MA 641Time Series Analysis I

3
Or
MA 661Dynamic Programming and Reinforcement Learning

3

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

Electives

15 credits to be chosen among the elective classes listed below.

Students may choose MA 900 Master of Science Thesis for 6 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.

Also, students may choose one (and only one) of the following as one of their electives:

CS 570Introduction to Programming, Data Structures, and Algorithms

3
EE 551Engineering Programming: Python

3

The elective courses listed below are grouped in 4 concentrations. Choosing a concentration is not a requirement. Students that do choose a concentration must then select at least 9 credits out of the 15 electives from the corresponding list.

Elective Courses for Concentration in Fundamentals of Data Science

At least 9 credits out of the 15 credits in elective classes must be chosen among the following courses.

MA 544Numerical Linear Algebra for Big Data

3
MA 577Statistical Network Analysis

3
MA 613Spatial and Spatio-Temporal Statistical Modeling

3
MA 617Tensor Methods for Data Analysis

3
MA 620Intro Network & Graph Theory

3
MA 623Stochastic Processes

3
MA 630Advanced Optimization Methods

3
MA 641Time Series Analysis I

3
MA 654Topological Data Analysis

3
MA 661Dynamic Programming and Reinforcement Learning

3
MA 662Stochastic Programming

3
MA 720Advanced Statistics

3
CPE 646Pattern Recognition and Classification

3
CS 584Natural Language Processing

3
CS 601Algorithmic Complexity

3

Elective Courses for Concentration in Data Acquisition and Management

At least 9 credits out of the 15 credits in elective classes must be chosen among the following courses.

CS 526Enterprise and Cloud Computing

3
CS 549Distributed Systems and Cloud Computing

3
CS 561Database Management Systems I

3
CS 562Database Management Systems II

3
CS 609Data Management and Exploration on the Web

3
EE 627Data Acquisition, Modeling and Analysis: Big Data Analytics

3
EE 628Data Acquisition, Modeling and Analysis: Deep Learning

2

Elective Courses for Concentration in Data Security

At least 9 credits out of the 15 credits in elective classes must be chosen among the following courses.

CS 573Fundamentals of CyberSecurity

3

CS 503Discrete Mathematics for Cryptography

3
Or
MA 503Discrete Mathematics for Cryptography

3

CS 579Foundations of Cryptography

3
Or
CPE 579Foundations of Cryptography

3

CS 578Privacy in a Networked World

3
CS 594Enterprise and Cloud Security

3
CS 595Information Security and the Law

3
CS 696Database Security

3
CPE 691Information Systems Security

3

CS 503, MA 503, CS 579, CPE 579, and CS 578: These three courses must be taken in the sequence CS 503 - CS 579 - CS 578.

Elective Courses for Concentration in Business Applications

At least 9 credits out of the 15 credits in elective classes must be chosen among the following courses.

CS 526Enterprise and Cloud Computing

3
BIA 660Web Mining

3
BIA 662Augmented Intelligence and Generative AI

3
BIA 672Marketing Analytics

3
BIA 674Supply Chain Analytics

3
BIA 676Data Stream Analytics

3
BIA 678Big Data Technologies

3
FE 5552D Data Visualization Programming for Financial Applications

3
MIS 636Data Integration for Business Intelligence and Analytics

3