Please note: The Advanced Certificate in Financial Computing is available only to current Fordham University students.
With the surge of high frequency trading and complex investing models, more and more sophisticated computing techniques and skills are required in the finance industry besides traditional quantitative models. For example, strong C++ based software development and machine-learning skills are essential for a financial engineer in automated trading.
To meet such an urgent strong demand for knowledge in advanced financial computing, the Department of Computer and Information Science in partnership with the Gabelli School of Business offers an advanced certificate in financial computing. This certificate program aims at training and sharpening students' computing skills in handling real world large-scale financial data in addition to financial software development. The state-of-the-art, rigorous, and personalized course work/projects guarantee students' future success in the job market. Our alumni have won high-salary "Quant" and financial data analytics positions in Wall Street.
- MSQF (Master of Science in Quantitative Finance) in Graduate School of Business Administration
- MSCS (Master of Science in Computer Science) in Graduate School of Arts and Sciences
- MSBA (Master of Science in Business Analytics) in Graduate School of Business Administration
- MSGF (Master of Global Finance) in Graduate School of Business Administration
- Other qualified graduate students.
This advanced certificate in financial computing consists of 15 credits plus a practicum: Projects and Internships in Financial Computing. Students take the following five courses (or equivalent with the approval of advisor).
CISC 6950 - Algorithms and Data Analysis (Fall semester, 3 credits)
A course on machine learning and data mining algorithms for analyzing large scale data sets. It covers clustering (K-means, hierarchical clustering), associative rule mining, classification and predication algorithms (k-NN, naive Bayes, ensemble learning methods, neural networks, genetic algorithms, support vector machines), in addition to data mining software, and its applications in economics and finance.
QFGB 8915 - Introduction to Stochastic Calculus (Fall semester, 2 credits)
Focuses on the practical applications of stochastic differential equations subject to appropriate boundary conditions, solving valuation problems, and using measure-transformations as required in advanced financial engineering practice to value assets within a risk-neutral framework. Builds a theoretical foundation for continuous-time models that are essential for the pricing and hedging of financial derivatives.
QFGB 8925 - Simulation Applications (Fall semester, 2 credits)
A course on simulation techniques in implementing financial models. It covers Monte Carlo basics, acceptance-rejection methods, sampling techniques, finite difference approximation, Delta-Gamma approximation, logistic regression, and their application in finance.
QFGB 8943 - Large Scale Data Modeling (Spring semester, 2 credits)
A course on the asset prices and the econometrics of financial markets. It covers SAS programming CAPM models, Time series models, Market efficiency models, etc.
CISC 6930 - Data Mining (Spring semester, 3 credits)
A course on data mining theory, algorithm, and applications. It covers data representation and visualization, analysis of large data set using information fusion and statistical combinatorial, and computational techniques; data mining methods (e.g., dimensional reduction, clustering, classification), descriptive vs. predictive modeling; and management of large diversified database systems. Applications are drawn from a variety of areas, including information retrieval, market analysis and CRM, e-commerce, financial computing, and economic forecasting, etc.
CISC/QFGB 8999 - Project and Internships in Financial Computing (3 credits)