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Graduate Program

MS Program

lecture in wegmans hall

The Master of Science (MS) in data science program at the University of Rochester provides students with a strong background in the fundamentals and applications of data science, and is accredited by New York State.

The program is designed for students with a background in any field of science, engineering, mathematics, or business. We welcome applicants of all career stages, from mid-career to fresh out of college. Prospective students should have experience in programming, and should be proficient in first-year, college mathematics.

The MS in data science program can be completed in two (fall/spring) or three semesters (fall/spring/fall) of full-time study. The two-semester version is designed for students with a strong background in computer science and mathematics, who 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 each semester, and many find internships over the summer. Although students are given opportunities to meet with corporate recruiters and offered internship advice, we cannot guarantee summer internship placement.

Program Components

A total of 30 credits are required to complete the program (without the optional summer bridging course) and many students will finish the program with slightly more than 30 credits, depending on the elective area courses they select. Students are not required to write a master's thesis to earn their degrees.

Optional Summer Bridging Course (4 credits)

An optional summer bridging course for students matriculating without a strong computer science background.

*Students will be notified in their offer letter if they are required to take this course.*  

Required Core Courses (16 credits)

Students may place out of one or more of the required core courses, but must complete the 30 credits required to complete the program.

Required Practicum (4 credits)

Practicum students work in teams of three to four to understand a sponsoring organization's business problem, clean and analyze data, and devise an appropriate solution. Students also explore ethical issues related to the use of data science, and give a final presentation to the sponsor and the class. Two faculty members from within the Goergen Institute evaluate the final presentation, which serves as the master’s degree exit exam.

Required Application Area Courses (minimum 10 credits)

Three elective courses, taken for a minimum total of 10 credits. Eight or more credits in one application area qualifies as a concentration, though earning a concentration is not a graduation requirement.

With the appropriate permissions, students can substitute an application area course with an independent study (DSCC 491) and/or 1-2 internship credits (DSCC 494). No more than six credits of research or internships may be used to substitute for application area courses. Research cannot serve as a substitute for the practicum course (DSCC 483).

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, review the Course Description/Course Schedule database.

Computational Methods
  • DSCC 401: Tools for Data Science (fall/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 
  • CSC 412: Human Computer Interaction (fall)
  • CSC 442: Artificial Intelligence (fall/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 410/CSC 413/BCSC 570/BME 410/CVSC 534/NSCI 415/OPT 410-1 Introduction to Augmented and Virtual Reality
  • ECE 417: Introduction to Dip Using Python
  • 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
Statistical Methodology
  • 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)
  • 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
  • BST 467: Applied Statistics in the Biomedical Sciences (spring)
  • BCSC 547: Introduction to Computational Neurosciences (every other spring, offered in spring 2021)
  • BCSC 512: Computational Methods in Cog Sci (every other fall)
  • BCSC 513: Introduction to fMRI (fall)
  • 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
  • DSCC 530: Methods in Data-Enabled Research into Human Behavior and its Cognitive and Neural Mechanisms (NRT students only) (fall - instructor permission required)
  • DSCC 531: Methods in Data-Enabled Research into Human Behavior and its Cognitive and Neural Mechanisms Practicum (NRT students only) (spring - by instructor permission required)
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)

*Courses in the business and social science application area that is housed in the Simon Business School do not run on the full semester system and are offered at a different credit hour rate than of Arts, Sciences & Engineering courses.*

MS Program Outcomes

Where are MS graduates employed after graduation?
Large Tech
  • Amazon.com
  • Google
  • IBM
  • Microsoft Corporation
  • SAP
Other Tech
  • 1010data
  • Blizzard Entertainment
  • MacroXStudio
  • Rochester Data Science Consortium (RDSC)
  • Udacity
Healthcare
  • Blue Shield of California
  • Cigna
  • HCA Healthcare
  • MediSked, LLC
  • Oscar Health
  • The Ohio State University, Wexner Medical Center
  • University of Rochester Medical Center
  • Rx Saving Solutions
Education/Research
  • Massachusetts Institute of Technology (MIT)
  • Science and Technology Policy Institute
  • University of Rochester
Finance
  • Accenture
  • American Express
  • Capitol One
  • China Merchants Bank
  • McKinsey & Company
  • UBS
Manufacturing/Services
  • Criteo
  • Corning
  • Delta Airlines
  • ForeFlight, a Boeing Company
  • Lockheed Martin
  • Samsung
  • Schlumberger, Ltd.
  • Tencent Holdings, Ltd.
  • Thomas Reuters Corporation
  • Variant Perception
  • Wegmans Food Markets, Inc.
  • Wyndham Destinations
What position titles did MS graduates obtain after graduation?
  • Business Intelligence Engineer
  • Cognitive Consultant
  • Data Analyst
  • Data Developer
  • Data Engineer
  • Data Integration Engineer
  • Data Scientist
  • Junior Full Stack Developer
  • Lead Data Scientist
  • Machine Learning Modeling Engineer
  • Presales Associate
  • Product Data Analyst
  • Quantitative Strategist
  • Research Associate
  • Researcher
  • Risk Manager - Credit Strategy
  • Senior Business Analyst & Data Science Translator
  • Senior Data Scientist
  • Software Developer
  • Software Engineer
  • Talent Analyst
  • Technical Consultant
  • Technology Analyst
  • TECDP (Technology Early Career Development Program) Senior Analyst
  • Video Game Product Manager
Which academic programs did MS graduates attend after graduation?
  • California Institute of Technology (CalTech)
    • PhD in Social and Decision NeuroScience
  • Cornell Tech
    • Master's program
  • Columbia University
    • MA in Biomedical Informatics
  • Johns Hopkins University
    • Master's program
  • University of Massachusetts, Amherst
    • PhD in Statistics
  • University of Rochester
    • PhD in Computer Science
  • Stanford Graduate School of Business
    • PhD program
Where did MS graduates complete internships?
  • Amazon.com
  • Autodesk, Inc.
  • Corning
  • CuraCall, Inc.
  • Google
  • Kwai, Inc.
  • Red Horse Corporation
  • Rochester Data Science Consortium (RDSC)
  • Simon School Venture Capital Fund
  • STEM-Away
  • System, Inc.
  • Tencent Holdings Ltd.
  • WealthEngine, Inc.
  • Wegmans Food Markets, Inc.
  • WW International (formerly Weight Watchers)

Contact Us

For additional information on the MS program, contact gids-ms@rochester.edu or visit our Frequently Asked Questions (FAQs) page.