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.