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, in any programming language, and should be comfortable with first-year college mathematics.
The curriculum includes a core set of four courses that ensure a foundation in computing and statistics: introductory and intermediate computational statistics, database systems, and data mining. Students choose three additional elective courses from one or more areas in computational and statistical methods, health and biomedical sciences, or business and social science. The last course that students take is a practicum, in which the students work in small teams to address a specific problem or application in data science. Teams will conduct a significant analysis for their project and each student will provide a final presentation.
The components of the program are as follows:
- An optional summer bridging course for students who come without a strong computer science background.
- 4 required Core courses, for a total of 16 credits. Students may place out of one or more of the required core courses as specified below, and thereby increase the number of elective courses they choose.
- 3 electives selected from the area courses, for a total of 10 credits or more (minimum of 10 credits required). Some of the area courses have prerequisites that the individual student must satisfy by prior coursework at the undergraduate level and will generally be evaluated by the instructor. A student’s electives may include a combination of areas; for example, one course from computer science and statistics and two courses from Biomedical Sciences. Eight credits or more in one area would constitute a concentration, but a concentration is not required. Note: Several area courses from the Simon School and the School of Medicine and Dentistry are three credit courses.
- 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 GIDS will evaluate the final oral presentation in order for it to serve as the Plan B (non-thesis) Master’s Degree Exit Exam.
A total of 30 credits are required to complete the program (without the bridging course) and some students will finish the program with more than 30 credits, depending on the elective area courses they select.
Optional Summer Bridging Course
For students without a strong computer science background and prior programming experience
- CSC 162 The Art of Data Structures
All five courses are required
- CSC 440 Data Mining (Fall & Spring)
- CSC 461 Database Systems (Spring)
- DSC 462 Computational Introduction to Statistics (Fall)
- DSC 465 Intermediate Statistical and Computational Methods (Spring)
- DSC 450 Data Science Practicum (Spring)
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 & Statistical Methods
- CSC 446 Machine Learning (Spring)
- CSC 444 Logical Foundations of AI (Fall)
- CSC 447 Natural Language Processing (Fall)
- CSC 448 Statistical Speech & Language Processing (Fall)
- CSC 449 Machine Vision (Spring)
- CSC 458 Parallel and Distributed Systems (Spring)
- CSC 577 Advanced Topics in Computer Vision (Fall)
- CSC 576 Advanced Machine Learning & Optimization (Fall)
- CSP 519 General Linear Approaches to Data Analysis II (Spring)
- BST 421W (STT 221W) Sampling Techniques (Fall)
- ECE 440 Introduction to Random Processes (Fall)
- ECE 442 Network Science Analytics (Spring)
- ECE 443 Probabalistic Models for Inference Estimation (Fall)
- EES 414 Geospatial Data Analysis (Not currently being offered)
- LIN 450 Data Sciences for Linguistics (Spring)
- PHY 403 Data Science I: Modern Statistics & Exploration of Large Data Sets (Spring)
- PHY 525 Data Science II: Complexity and Network Theory (Fall)
Health & Biomedical Sciences
- BST 431 Intro to Computational Bio (every other Spring)
- BST 432 Intro to Bioinformatics (every other Fall)
- BST 433 Intro to Computational Systems Bio (Spring)
- BST 467 Applied Statistics in the Biomedical Sciences (Spring)
- BST 520 Current Topics in Bioinformatics (Fall)
- BCS 547 Intro to Computational Neurosciences (Spring)
- BCS 512 Computational Methods in Cog Sci (Fall)
- BCS 513 Intro to fMRI (Fall)
- PM 421 U.S. Health Care System (Fall)
- PM 422 Quality of Care & Risk Adjustment (Spring)
- BIO 453 Computational Bio (Fall & Spring)
- DSC 530 Methods in Data-Enabled Research into Human Behavior and its Cognitive and Neural Mechanisms (Fall; NRT students only)
- DSC 531 Methods in Data-Enabled Research into Human Behavior and its Cognitive and Neural Mechanisms Practicum (Spring; NRT students only)
Business & Social Science
- CIS 417 Intro to Business Analytics (Fall)
- CIS 442C Social Media Analytics (TBD)
- MKT 412 Marketing Research (Winter)
- MKT 437 Digital Marketing Strategy (Winter)
- MKT 451 Advanced Quant Marketing (TBD)
- CIS 418 Advanced Business Modeling & Analytics (Winter)
- PSC 404 Prob & Inference (Fall)
- PSC 405 Linear Models (Spring)
- PSC 504 Causal Inference (Fall)
- PSC 505 Max. Likelihood Estimation (Fall)
*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.
*Courses are subject to change and should all be researched using the CDCS when creating a program of study. The CDCS provides additional course information, the instructor's name, and the time and location of each course.