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:
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
Program Objectives
- Analyze data with state-of-the-art modeling techniques with both high interpretability and prediction power.
- Make informed decisions based on the data.
- Effectively communicate analysis findings to non-experts.
- Work effectively in teams.
Program Outcomes
Program Educational Outcomes common to all concentrations:
- Explain the principles of the collection, storage, exploration, and analysis of data.
- Recognize the complexity of data and the assumptions underlying modeling strategies in real-life applications.
- Examine and implement efficient computational techniques for analyzing large-scale (big) data.
- Analyze and implement appropriate methods of inference, prediction and decision-making based on the data and complex structures.
- Develop and analyze data-based models.
- Effectively communicate analysis findings to an audience with a diverse background.
Program Outcomes specific to the concentration in Fundamentals of Data Science:
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Construct new relevant models and design algorithms tailored for challenging real-life situations.
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Interpret model outputs, infer, and make relevant decisions pertaining to applications.
Program Outcomes specific to the concentration in Data Acquisition and Management:
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Manage the collection, storage and exploration of data of large sizes and of various nature.
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Design and implement efficient distributed systems tailored for challenging real-life applications.
Program Outcomes specific to the concentration in Data Security:
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Manage the collection and storage of data of large sizes and of various nature, with an emphasis on privacy issues.
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Assess the vulnerabilities of a network, detect threats, and enforce appropriate security measures.
Program Outcomes specific to the concentration in Business Applications:
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Design, deploy and grow business intelligence systems tailored for challenging real-life applications.
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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:
Choosing a concentration is not a requirement. Students that do choose a concentration must then select at least 9 credits out of the 12 credits from the corresponding list.
Core Courses
18 credits totaled over the 6 core courses described under Requirements 1 and 2 as follows.
Core Courses - Requirement 1
Students must take MA 540, MA 541, and CS 583 and one of the following three courses: CPE 595, CS 559, or FA 590 (only one of these three courses will count towards a student's degree requirements).
| MA 540 | Introduction to Probability Theory | 3 |
| MA 541 | Statistical Methods | 3 |
| CPE 595 | Applied Machine Learning | 3 |
| Or | |
| CS 559 | Machine Learning: Fundamentals and Applications | 3 |
| Or | |
| FA 590 | Statistical Learning | 3 |
| CS 583 | Deep Learning | 3 |
For students with sufficient mathematical background, MA 540 may be replaced with a course specified under Core Courses - Requirement 2, below.
MA 612 Mathematical Statistics or MA 701 Statistical Inference can be taken instead of MA 541 given sufficient preparation.
Core Courses - Requirement 2
Two courses (6 credits) must be taken from the following list. Only one of MA 576 or MA 630 may be taken towards the degree.
| MA 544 | Numerical Linear Algebra for Big Data | 3 |
| MA 576 | Optimization for Data Science | 3 |
| Or | |
| MA 630 | Advanced Optimization Methods | 3 |
| MA 641 | Time Series Analysis I | 3 |
| MA 661 | Dynamic Programming and Reinforcement Learning | 3 |
Electives
12 credits to be chosen from the elective courses 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 12 credits from the corresponding list.
Elective Courses for Concentration in Fundamentals of Data Science
At least 9 out of the 12 credits of electives must be chosen from the following list of courses.
| MA 544 | Numerical Linear Algebra for Big Data | 3 |
| MA 577 | Statistical Network Analysis | 3 |
| MA 613 | Spatial and Spatio-Temporal Statistical Modeling | 3 |
| MA 617 | Tensor Methods for Data Analysis | 3 |
| MA 620 | Intro Network & 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 out of the 12 credits of electives must be chosen from the following list of 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 out of the 12 credits of electives must be chosen from the following list of 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 out of the 12 credits of electives must be chosen from the following list of courses.
| CS 526 | Enterprise and Cloud Computing | 3 |
| BIA 660 | Web Mining | 3 |
| BIA 662 | Augmented Intelligence and Generative AI | 3 |
| BIA 672 | Marketing Analytics | 3 |
| BIA 674 | Supply Chain Analytics | 3 |
| BIA 676 | Data Stream Analytics | 3 |
| BIA 678 | Big Data Technologies | 3 |
| FA 555 | 2D Data Visualization Programming for Financial Applications | 3 |
| MIS 636 | Data Integration for Business Intelligence and Analytics | 3 |