Due to the advent of digital data acquisition and storage technology, the financial world has, in the past decade, been faced with large daily sets of data. It has been a great challenge to the financial community as to how to tap these data and to extract from them information of critical business value.
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 graduate certificate combines the strengths of both disciplines in a year-long program.
The topics covered by the Graduate Certificate will include:
Requirements for the certificate include two courses: CISC 6950: Algorithms and Data Analysis, and ECON 6910: Applied Econometrics or ECON 6950: Financial Econometrics
- 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 techniques: for dealing with the large data sets stored in local or remote data base systems, including searching and sorting, decision trees, dynamic programming, neural networks, genetic algorithms, classification, clustering, association-rule mining, sequence prediction, various applications in financial markets, and decision support systems.
- Exploratory data analysis systems: (for example, SAS Enterprise Miner), used to build hands-on experience.
The courses taken for the graduate certificate can also be counted towards graduate degrees in Economics or in Computer Science.
|CISC 6950 Algorithms & Data Analysis (3) This course will cover the fundamental and practical aspect of computing algorithms and exploratory data-analysis techniques for dealing with the large data sets stored in local or remote data base systems. Topics will include searching and sorting, decision trees, dynamic programming, neutral networks, genetic algorithms, classification, clustering, association-rule mining, sequence prediction, various applications in financial markets, and decision support systems. Exploratory data analysis (eg. SAS) will be used throughout the course.
|ECON 6910 Applied Econometrics (3) Basic techniques of econometric theory, including applications in consumer theory, theory of the firm, and in macroeconomics, as well as a review of statistical methods. Some computer work is assigned.
ECON-6950 Financial Econometrics (3) Hypothesis testing, and modeling, with respect to financial data.