The program supports both a graduate’s need to move between a multitude of roles and the specific competencies associated with project management, data management, analysis, model selection, statistics, and many of the technical tools (R studio, SQL, etc.).
Credits Breakdown
12 credits from four required core courses (All core courses will be taught using the Python or R programming languages)
3 credits from required Master’s Thesis or Master’s Capstone Project
At least9 credits from elective courses (with at least one 3-credit course from each domain area; see below)
3 optional credits of practicum or internship
3 optional credits of independent study
30 CREDITS TOTAL
Required Courses
12 credits from four required core courses (All core courses will be taught using the Python or R programming languages)
56:219:500 Foundations of Data Science: Programming and Reasoning
56:219:511 Statistical Methods for Data Science
56:219:521 Data Visualization
56:219:531 Applied Data Mining and Machine Learning
Required Thesis or Capstone Project
3 credits from either a Thesis or a Capstone Project.
56:219:603 Master’s Project
56:219:701 Thesis in Data Science
Elective Courses
At least9 credits from elective courses with at least one 3-credit course from each domain area below: Mathematics and Statistics, Computer Science, and Social Sciences/Humanities/Business School. Additional courses not specifically listed here may be applied towards the elective requirement contingent on approval by the program graduate director. Note that students cannot take more than two courses outside School 56 (The Graduate School).
Mathematics and Statistics:
56:645:549 Linear Algebra and Applications
56:645:567 Statistical Models
56:645:563 Statistical Reasoning
56:219:513 Regression and Time Series Forecasting
56:219:512 Probability and Stochastic Processes for Data Science
Computer Science:
56:219:501 Algorithmic Problem Solving in Data Science
56:198:541 Distributed and Cloud Computing
56:198:551 Database Systems
56:198:562 Big Data Algorithms
56:219:567 Applied Probability
56:219:593 Special Topics in Data Science
Social Sciences/Humanities/Business School:
56:219:523 Geographic Information Systems for Data Science
56:219:522 Data Management
56:202:600 Research Methods in Criminal Justice
56:202:601 Data Analysis in Criminal Justice
Special Topics: Data-Motivated Storytelling
Quantitative Methods I
Quantitative Methods II
56:834:608 GIS in the Public Sector
56:163:661 Quantitative Methods
56:163:615 Using Archival Data to Study Children
Distant Reading with Text Analysis
56:209:520 Emerging Experimental Media
Information security management (aligned with CISSP certification)
53:623:510 IT Strategy and Project Management
Optional Credits
3 credits each from 56:219:600 Data Science Internship and/or 56:219:601 Independent Study
