Master of Science in Data Science
Data Science is a coherent framework of principles for processing and analyzing data towards decision-making. The Master of Science in Data Science (MSDS) 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, and 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. Four concentrations are offered: Fundamentals of Data Science, Data Acquisition and Management, Data Security, and Business Applications.
Program Educational Objectives:
- 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 Educational Outcomes common to all concentrations:
Upon completion of the program, student should 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.
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.
Data Science Requirements
Core Courses
15 credits totaled over the following 5 core courses.
| MA 541 | Statistical Methods | 3 |
| CPE 695 | Applied Machine Learning | 3 |
| CS 583 | Deep Learning | 3 |
| | | |
| MA 576 | Optimization for Data Science | 3 |
| | Or | |
| MA 630 | Advanced Optimization Methods | 3 |
| | | |
| | | |
| MA 544 | Numerical Linear Algebra for Big Data | 3 |
| | Or | |
| MA 641 | Time Series Analysis I | 3 |
| | Or | |
| MA 661 | Dynamic 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 570 | Introduction to Programming, Data Structures, and Algorithms | 3 |
| EE 551 | Engineering 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 544 | Numerical Linear Algebra for Big Data | 3 |
| MA 613 | Spatial and Spatio-Temporal Statistical Modeling | 3 |
| MA 617 | Tensor Methods for Data Analysis | 3 |
| MA 620 | Introduction to Network and Graph Theory | 3 |
| MA 623 | Stochastic Processes | 3 |
| MA 630 | Advanced Optimization Methods | 3 |
| MA 641 | Time Series Analysis I | 3 |
| MA 654 | Topological Data Analysis | 3 |
| MA 661 | Dynamic Programming and Reinforcement Learning | 3 |
| MA 662 | Stochastic Programming | 3 |
| MA 720 | Advanced Statistics | 3 |
| CPE 646 | Pattern Recognition and Classification | 3 |
| CS 584 | Natural Language Processing | 3 |
| CS 601 | Algorithmic 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 526 | Enterprise and Cloud Computing | 3 |
| CS 549 | Distributed Systems and Cloud Computing | 3 |
| CS 561 | Database Management Systems I | 3 |
| CS 562 | Database Management Systems II | 3 |
| CS 609 | Data Management and Exploration on the Web | 3 |
| EE 627 | Data Acquisition, Modeling and Analysis: Big Data Analytics | 3 |
| EE 628 | Data Acquisition, Modeling and Analysis: Deep Learning | 3 |
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 573 | Fundamentals of CyberSecurity | 3 |
| | | |
| CS 503 | Discrete Mathematics for Cryptography | 3 |
| | Or | |
| MA 503 | Discrete Mathematics for Cryptography | 3 |
| | | |
| CS 579 | Foundations of Cryptography | 3 |
| | Or | |
| CPE 579 | Foundations of Cryptography | 3 |
| | | |
| CS 578 | Privacy in a Networked World | 3 |
| CS 594 | Enterprise and Cloud Security | 3 |
| CS 595 | Information Security and the Law | 3 |
| CS 696 | Database Security | 3 |
| CPE 691 | Information Systems Security | 3 |
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 526 | Enterprise and Cloud Computing | 3 |
| BIA 660 | Web Mining | 3 |
| BIA 662 | Cognitive Computing | 3 |
| BIA 672 | Marketing Analytics | 3 |
| BIA 674 | Supply Chain Analytics | 3 |
| BIA 676 | Data Stream Analytics | 3 |
| BIA 678 | Big Data Technologies | 3 |
| FE 555 | 2D Data Visualization Programming for Financial Applications | 3 |
| MIS 636 | Data Integration for Business Intelligence and Analytics | 3 |