Data Analysis and Financial Econometrics
Due to the advent of digital data acquisition and storage technology, the financial world is faced with enormous amounts of data. It has been a great challenge to the financial community as to how to extract critical business knowledge from this data. The certificate in financial econometrics and data analysis is designed to address this problem. This certificate will enhance one's career prospects by providing the analytical and programming skills needed to analyze the large data sets commonly found in business.
This certificate leverages the graduate programs in computer science and in economics at Fordham University, and it provides industry professionals with state-of-the-art, rigorous training in quantitative analysis. This unique advanced certificate combines the strengths of both disciplines.
- Econometric techniques, beginning with least squares estimation, method of moments, maximum likelihood, and culminating in forecasting and modeling of financial variables
- Statistical diagnostics and corrections for data, taught using an industry standard data analysis tool (e.g., SAS)
- Exploratory data-analysis (data mining) techniques for dealing with the large data sets. Classification algorithms will be covered (e.g., decision trees and neural networks), as well as clustering and association rule mining algorithms.
- Exploratory data analysis systems (for example, SAS Enterprise Miner), used to build hands-on experience.
The courses taken for the advanced certificate can also be counted towards graduate degrees in economics or in computer science.
The certificate in financial econometrics and data analysis is earned by completing two courses, one from the computer and information science department and one from the economics department. The student must take:
- CISC 6930 Data Mining or CISC 5500 Data Analytic Tools and Scripting
- ECON 6910 - Applied Econometrics or ECON 6950 - Financial Econometrics
The student must have a cumulative grade point average of 3.0 (B) or better.