Become your Organization's Data Superhero
This is where advanced data science, machine learning, and AI live in the program, but courses go far beyond LLMs. Rather, this concentration builds durable analytical foundations that extend beyond any single technology. Coursework emphasizes advanced econometrics, forecasting, statistical learning, simulation, nonparametric methods, machine learning, model evaluation, data visualization, and responsible use of AI. Having a deep understanding of all of these techniques, along with domain knowledge, allows our graduates to select the right tool for the job.
Projects come from local organizations, and hands-on labs (even for online students), realistic projects and data set the program apart from data-analytics offerings that are online-only. Graduates are prepared to lead analytical work, not just by operating tools but by determining which methods are appropriate and translating the results into decisions.
Concentration Learning Goals
- Advanced econometrics
- Nonparametric methods (e.g., kernel density estimation, bootstrapping, simulations)
- Statistical learning and machine learning (random forests, neural networks [what LLMs are built on], etc.)
- Data visualization and communication
- Responsible application of analytical and AI methods to real-world problems
Students learn to apply these methods to real-world problems, using real-world data.
Concentration Courses
| Course Number | Course Title | Description | Credits |
|---|---|---|---|
| ECON 8320 | Tools for Data Analysis |
Covers basic principles of programming languages, as well as libraries useful in collecting, cleaning and analyzing data to answer research questions. While the course uses Python, the student should be able to move to other languages frequently used in data analysis using the principles taught in this course. |
3 |
| ECON 8310 | Business Forecasting |
The course will cover forecasting tools and applications applied to business settings using Python. Traditional Econometric foresting methods as well as predictive analytics and machine learning approaches are covered in the course. |
3 |
| ECON 8330 | Data Analysis from Scratch |
This class trains the student to build all estimators from scratch. Additionally, it introduces numerous non-parametric, machine learning, and simulation techniques. This approach to econometrics results in a stronger understanding of statistical assumptions and methods, a better understanding of when a method is appropriate, and stronger programming abilities. |
3 |