Our Master of Science in Data Science program is accredited by New York State and provides a strong background in the both the fundamentals and applications of data science.
The program can be completed in either two semesters (fall/spring) or three semesters (fall/spring/fall) of full-time study. The two semester version is appropriate for students who enter with a strong background in computer science and mathematics, and are eager to take on a relatively heavy course load (four courses per semester) in order to graduate quickly. In the three semester version, students take three courses per semester, and many students work at internships during the summer between the spring and fall semesters. (Our program provides opportunities for students to meet corporate recruiters and provides advice on applying for internships, but we do not guarantee placement in an internship.)
The program is designed for students with a background in any field of science, engineering, mathematics, or business. We welcome mid-career applicants as well as students fresh out of college. Prospective students should have experience in programming, and should be comfortable with first-year college mathematics.
The components of the program are as follows:
- An optional summer bridging course for students who come without a strong computer science background.
- Five required core courses, for a total of 20 credits. Students may place out of one or more of the required core courses, but will still be required to complete the 30 credits required for the program.
- Three electives selected from the area courses, for a total of 10 credits or more. Some of the area courses have prerequisites that students must satisfy. Eight credits or more in one area would constitute a concentration, but a concentration is not required.
- A four-credit practicum, in which the student works in a team to implement a significant system or analysis with a final oral presentation provided by each student. A committee of two faculty members from within the institute will evaluate the final oral presentation in order for it to serve as the master’s degree exit exam.
A total of 30 credits are required to complete the program (without the bridging course) and many students will finish the program with more than 30 credits, depending on the elective area courses they select.
Optional Summer Bridging Course
- CSC 162: The Art of Data Structures*
- CSC 440: Data Mining
- CSC 461: Database Systems
- DSC 462: Computational Introduction to Statistics
- DSC 465: Intermediate Statistical and Computational Methods
- DSC 450: Data Science Practicum
A minimum of 10 credits total required, across three areas. Eight or more of these credits in one specific area will qualify as a concentration. Students have the option to substitute an independent study (DSC 491) in place of an area course with the appropriate permissions.
Computational and Statistical Methods
- DSC 401: Tools for Data Science
- CSC 412: Human Computer Interaction
- CSC 446: Machine Learning
- CSC 444: Logical Foundations of AI
- CSC 447: Natural Language Processing
- CSC 448: Statistical Speech and Language Processing
- CSC 449: Machine Vision
- CSC 458: Parallel and Distributed Systems
- CSC 577: Advanced Topics in Computer Vision
- CSC 576: Advanced Machine Learning and Optimization
- CSP 519: General Linear Approaches to Data Analysis II
- BST 421W/STT 221W: Sampling Techniques
- ECE 440: Introduction to Random Processes
- ECE 442: Network Science Analytics
- ECE 443: Probabilistic Models for Inference Estimation
- EES 414: Geospatial Data Analysis
- LIN 450: Data Sciences for Linguistics
- PHY 403: Data Science I: Modern Statistics and Exploration of Large Data Sets
- PHY 525: Data Science II: Complexity and Network Theory
Health and Biomedical Sciences
- BST 431: Introduction to Computational Biology
- BST 432: Introduction to Bioinformatics
- BST 433: Introduction to Computational Systems Biology
- BST 467: Applied Statistics in the Biomedical Sciences
- BST 520: Current Topics in Bioinformatics
- BCS 547: Introduction to Computational Neurosciences
- BCS 512: Computational Methods in Cog Sci
- BCS 513: Introduction to fMRI
- PM 410: Introduction to Data Management/Analysis
- PM 416: Epidemiologic Methods
- PM 421: US Health Care System
- PM 422: Quality of Care and Risk Adjustment
- BIO 453: Computational Biology
- DSC 530: Methods in Data-Enabled Research into Human Behavior and its Cognitive and Neural Mechanisms (NRT students only)
- DSC 531: Methods in Data-Enabled Research into Human Behavior and its Cognitive and Neural Mechanisms Practicum (NRT students only)
Business and Social Science*
- CIS 417: Introduction to Business Analytics
- CIS 418: Advanced Business Modeling and Analytics
- CIS 442C: Social Media Analytics
- MKT 412: Marketing Research
- MKT 437: Digital Marketing Strategy
- MKT 451: Advanced Quant Marketing
- PSC 404: Probability and Inference
- PSC 405: Linear Models
- PSC 504: Causal Inference
- PSC 505: Maximum Likelihood Estimation
*Please note that any course in this concentration that is housed in the Simon Business School does not run on the semester system and is offered at a different credit hour rate than AS&E courses.
- DSC 450: Data Science Practicum