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 find 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.
- Four required core courses for a total of 16 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.
- A required 4 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.
- Three electives selected from the area courses or research, 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.
- No more than 6 credits can come from research. Research cannot be substituted for the practicum.
A total of 30 credits are required to complete the program (without the bridging course) and many students will finish the program with slightly more than 30 credits, depending on the elective area courses they select.
Optional Summer Bridging Course
*Students will be notified in their offer letter if they are required to take this course.
- DSCC 462: Computational Introduction to Statistics (offered every fall)
- DSCC 465: Intermediate Statistical and Computational Methods (offered every spring; prereq: DSCC 462 or equivalent)
- DSCC 440: Data Mining (offered fall and spring)
- DSCC 461: Introduction to Databases (formerly Database Systems) (offered fall and spring)
- DSCC 483: Data Science Practicum (to be taken final semester, offered fall and 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 though a concentration is not necessary for graduation.
Students have the option to substitute an independent study of research (DSCC 491) in place of an area course with the appropriate permissions. Students may also use 1-2 internship credits (DSCC 494) with appropriate permissions. No more than 6 credits of research/internships may be used on a program of study. Research cannot take the place of the practicum course (DSCC 483.)
While we have tried to indicate the semester the course is offered, please note that scheduled courses can change. Some courses may be discontinued and new courses relevant to data science may be offered. To confirm if a course is offered this semester, see the Course Description/Course Schedule database.
- DSCC 401: Tools for Data Science (fall and spring)
- DSCC 402: Data Science at Scale (spring)
- DSCC 475: Time Series Analysis and Forecasting in Data Science (fall)
- DSCC 481: Artificial Intelligence and Deep Learning in Healthcare (not offered 2020)
- CSC 412: Human Computer Interaction (fall, not offered in fall 2018)
- CSC 442: Artificial Intelligence (fall and spring)
- CSC 446: Machine Learning (spring)
- CSC 444: Knowledge Representation and Reasoning in AI (fall)
- CSC 447: Natural Language Processing (fall)
- CSC 448: Statistical Speech and Language Processing (every other fall)
- CSC 449: Machine Vision (fall)
- CSC 452: Computer Organization (spring)
- CSC 458: Parallel and Distributed Systems (spring)
- CSC 482: Design and Analysis of Efficient Algorithms (fall)
- CSC 486: Computational Complexity (fall)
- CSC 576: Advanced Topics in Data Management (fall)
- CSC 577: Advanced Topics in Computer Vision (fall)
- CSC 578: Deep Learning (spring)
- CSSP 519: General Linear Approaches to Data Analysis II (spring)
- BST 421W/STT 221W: Sampling Techniques (fall)
- ECE 477/CSC 464 Computer Audition (fall)
- EESC 410: Stochastic Inverse Modeling in Geophysics (spring)
- EESC 414: Earth Science Data Analysis (fall)
- EESC 421: Quantitative Environmental Problem Solving (spring)
- LING 424: Intro to Computational Liguistics (fall)
- LING 450: Data Sciences for Linguistics (spring)
- LING 470: Tools for Language Documentation (fall)
- LING 481: Statistical and Neural Methods for Computational Linguistics (spring)
- PHYS 573: Physics and Finance
- STAT 416: Applied Statistical Methods-I
- STAT 417: Applied Stat Methods II
- STAT 418: Categorical Data Analysis
- STAT 419: Nonparametric Inference
- STAT 423: Bayesian Inference
- STAT 476: Statistical Inference in R
- STAT 477: Introduction to Statistical Software
- ECE 440: Introduction to Random Processes (fall)
- ECE 441: Detection Estimation Theory (spring)
- ECE 442: Network Science Analytics (spring)
- ECE 443: Probabilistic Models for Inference Estimation (fall, not offered in 2018-19)
- PHYS 403: Data Science I: Modern Statistics and Exploration of Large Data Sets
- PHYS 525: Data Science II: Complexity and Network Theory
Health and Biomedical Sciences
- BIOL 453: Computational Biology (spring)
- BIOL 457L: Applied Genomics with Lab (fall)
- BST 432: High Dimensional Data Analysis (fall)
- BST 433: Introduction to Computational Systems Biology (not offered in 2018-19)
- BST 467: Applied Statistics in the Biomedical Sciences (spring)
- BCSC 547: Introduction to Computational Neurosciences (every other spring, offered in spring 2019)
- BCSC 512: Computational Methods in Cog Sci (every other fall, offered in spring 2020)
- BCSC 513: Introduction to fMRI (fall, offered in fall 2019)
- CSPS 504/BCSC 510: Data Analysis I (fall)
- PM 410: Introduction to Data Management/Analysis (fall)
- PM 416: Epidemiologic Methods (spring)
- PM 421: US Health Care System (fall)
- PM 422: Quality of Care and Risk Adjustment (spring)
- DSCC 481: Artificial Intelligence and Deep Learning in Healthcare (not offered fall 2020)
- DSCC 530: Methods in Data-Enabled Research into Human Behavior and its Cognitive and Neural Mechanisms (NRT students only) (fall - by instructor permission)
- DSCC 531: Methods in Data-Enabled Research into Human Behavior and its Cognitive and Neural Mechanisms Practicum (NRT students only) (spring - by instructor permission)
Business and Social Science*
- CIS 417*: Introduction to Business Analytics
- CIS 418*: Advanced Business Modeling and Analytics
- CIS 432* Predictive Analytics/Python
- CIS 434*: Social Media Analytics
- CIS 442F*: Big Data
- FIN 418*: Quantitative Finance w/ Python
- MKT 412*: Marketing Research
- MRT 436R*: Marketing Analytics using R
- MKT 437*: Digital Marketing Strategy
- MKT 451*: Advanced Quant Marketing
- PSCI 404: Probability and Inference (fall)
- PSCI 405: Linear Models (spring)
- PSCI 504: Causal Inference (spring)
- PSCI 505: Maximum Likelihood Estimation (fall)
*Please note that any course in this concentration that is housed in the Simon Business School does not run on the full semester system and is offered at a different credit hour rate than ASandE courses.