M.S. in Data Science and Quantitative Economics Curriculum
Economics Core
Three (3) Courses
- ECON 6010 - Microeconomics Theory I
- ECON 6020 - Macroeconomics Theory I
- ECON 6910 - Applied Econometrics or ECON 6950 - Financial Econometrics
Data Science Core
Two (2) Courses
- CISC 5790 - Data Mining
- CISC 5800 - Machine Learning
Math Core
One (1) course from either of the following
- ECON 5710 - Math Analysis
- CISC 5450 - Mathematics for Data Analysis
One of the following:
Capstone Seminar
Internship
M.S. Thesis (6 credits)1
- CISC 6080 or ECON 5XXX - Capstone Seminar (cross-listed)
- CISC 6081
- CISC 6085 or CISC 6086
Electives
Data Science
One (1) course from the following:
- CISC 5352 - Machine Learning in Finance
- CISC 5500 - Data Analysis Tools and Scripting
- CISC 5835 - Algorithms for Data Science
- CISC 5950 - Big Data Programming
- CISC 5900 - Information Fusion
- CISC 5550 - Cloud Computing
- CISC 5640 - NoSQL Database
- CISC 6000 - Deep Learning
- CISC 6210 - Natural Language Processing
- CISC 6325 - Data Visualization
- CISC 6352 - Advanced Computational Finance
- CISC 6525 - Artificial Intelligence
- CISC 6735 - Database Systems
Economics
One (1) elective from any of the following areas:
- Applied Microeconomics
- ECON 5590 - Health Economics
- ECON 6440 - Development Economics
- ECON 5260 - Resource/Environmental Economics
- ECON 5280 - Urban Economics
- Finance
- ECON 6240 - Financial Economics
- ECON 6340 - Financial Theory
- Specialized Topics
- ECON 6970 - Applied Microeconometrics
- ECON 5760 - Computational Economics
- ECON 5450 - Crisis Adjustment and Poverty
1 Students completing two semesters of thesis (6 credits) may complete one fewer 3-credit elective.