Graduate School of Arts and Sciences
Advanced Certificate in Financial Econometrics and Data Analysis
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
knoweldge 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 provides industry professionals with a
state-of-the-art, rigorous training in quantitative analysis. This unique
advanced certificate combines the strengths of both disciplines.
The topics covered by the Advanced Certificate will include:
The courses taken for the advanced certificate can also be counted towards
graduate degrees in Economics or in Computer Science.
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 Certificate in Financial Econometrics and Data Analysis is earned by
completing 2 courses, one from the Computer and Information Science department
and one from the Economics department. The student must take:
CISC 6950: Algorithms and Data Analysis, and
ECON 6910: Applied Econometrics or ECON 6950: Financial Econometrics
The student must have a cumulative grade point average of 3.0 (B) or better.
Dr. Yanjun Li