Curriculum Overview
Coursework
The M.S. in Data Science curriculum features seven required courses (18 credits), as well as 12 credits of restricted elective courses. Students may also need to take a required prerequisite (three additional credits) in their first semester.
The required coursework includes the Capstone Experience, a team project which is completed in the second year of study.
The suggested plan of study for the accelerated degree is available below (the plan of study for the standard two-year degree, which will launch in Fall 2026, will be available at a later time). Students must submit a proposed plan of study, including electives, to their graduate advisor early in the first semester of their senior year.
Required Courses
Students must take the following courses to earn the M.S. in Data Science. Note: CS 5045 is a required prerequisite that may be waived pending review of an undergraduate's coursework.
- ADS 5064: Foundations of Data Science (3 credits)
Fundamental principles and concepts in data science in the context of the end-to-end data science project life cycle. - ADS 5224: Communication in Team-Based Data Science (3)
Oral, written, and visual communication skills required to plan, oversee, and execute end-to-end data science projects. - ADS 5525/5526: Statistical Learning (6)
Methods of supervised statistical and machine learning for regression and classification. - ADS 5804: Capstone Experience I: Data & Definition (1)
Application of underlying principles and initial phases of the data science process to a specific project. - ADS 5814: Capstone Experience II: Implementation (2)
Application of latter phases of the data science process to a specific project. - CS 5045: Computation for Data Sciences* (3)
Covers fundamentals of computer science and background in data sciences needed by graduate students without a computer science background. - CS 5054: Programming Models for Big Data (3)
Survey of computer science concepts and tools that enable efficient computational science and data analytics with big data.
Students in the M.S. in Data Science program can choose to pursue a concentration, which consists of four approved electives (12 credits) in an applied disciplinary area. There are currently four concentrations offered within the M.S. in Data Science:
- Applied Data Science
- Economics
- Environmental Data Sciences
- Geosciences
Additional concentrations are under development.
Restricted Electives
The courses listed below are currently approved for students to fulfill their restricted electives requirement. Please note that this list is subject to change.
Courses are included in Applied Data Science concentration
- ADS 5984: SS: Data Science Business Solutions (3)
Courses are included in Applied Data Science concentration
- CMDA 4634: Scalable Computing for Computational Modeling and Data Analytics (3)
Courses are included in Applied Data Science concentration
- CS 5644: Machine Learning with Big Data (3)
- CS 5664: Social Media Analytics (3)
- CS 5764: Information Visualization (3)
- CS 5834: Introduction to Urban Computing (3)
- ECON 4084: Industry Structure (3)
- ECON 4514: Applied Analysis of Banking and Financial Markets (3)
- ECON 5134: Managerial Economics (3)
- ECON 5134G: Advanced Big Data Economics (3)
- ECON 5945: Econometric Theory and Practice (3)
- ECON 5154: Empirical Industrial Organization (3)
Courses are included in Environmental Data Sciences concentration
- FREC/BIOL 5034: Ecosystems Dynamics (4)
- FREC 5114G: Advanced Information Technology for Natural Resources Management (3)
- FREC/GEOG 5154: Hyperspectral Remote Sensing for Natural Resources (3)
- FREC 5254: Remote Sensing of Natural Resources (3)
- FREC 5244: Advanced Hydroinformatics (3)
- FREC/AAEC/GEOG 5544: Remote Sensing in the Social Sciences (3)
- FREC 5224: Forest Biometry (3)
- FREC/GEOG 6214: Forestry Lidar Applications (3)
- FREC 5174: Ecological Modeling and Forecasting (3)
- GEOS/MATH 5144: Inverse Theory and Geoscience Applications (3)
- GEOS 5184: Advanced Geodesy in the Earth Sciences (3)
- GEOS 5314: Advanced Coastal Hazards (3)
- GEOS 5984: Data Science in the Geosciences (3)
- GEOS 6104: Advanced Topics in Geosciences (3)
Courses are included in Applied Data Science concentration
- MATH 5424: Numerical Linear Algebra (3)
- MATH 5544: Mathematical Optimization for Machine Learning (3)
- MATH 5564: Model Reduction: System-Theoretic Methods (3)
Courses are included in Applied Data Science concentration
- STAT 5054: Introduction to Statistical Computing (3)
- STAT 5154: Statistical Computing for Data Analytics (3)
- STAT 5234: Experimental Design for Data Science (3)
- STAT 6554: Advanced Statistical Computing (3)
Accelerated 4+1
Standard (starting Fall 2026)
Semester 1: Fall (Senior Year)
- ADS 5064: Foundations of Data Science (3)
- CS 5045: Computation for Data Sciences* (3) *May be waived pending review of student’s undergraduate coursework
Semester 1: Fall (Year One)
- ADS 5064: Foundations of Data Science (3)
- CS 5045: Computation for Data Sciences* (3) *May be waived pending review of student’s undergraduate coursework
- Elective 1 (3)
Semester 2: Spring (Senior Year)
- ADS 5224: Communication in Team-Based Data Science (3)
- ADS 5525: Statistical Learning (3)
Semester 2: Spring (Year One)
- ADS 5224: Communication in Team-Based Data Science (3)
- ADS 5525: Statistical Learning (3)
- Elective 2 (3)
Semester 3: Fall (Year Two)
- ADS 5526: Statistical Learning (3)
- ADS 5804: Capstone Experience I: Data & Definition (1)
- Electives 1 & 2 (6)
Semester 3: Fall (Year Two)
- ADS 5526: Statistical Learning (3)
- ADS 5804: Capstone Experience I: Data & Definition (1)
- Elective 3 (3)
Semester 4: Spring (Year Two)
- CS 5054: Programming Models for Big Data (3)
- ADS 5814: Capstone Experience II (2)
- Electives 3 & 4 (6)
Semester 4: Spring (Year Two)
- CS 5054: Programming Models for Big Data (3)
- ADS 5814: Capstone Experience II (2)
- Elective 4 (3)