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Financial Analytics

Program Details

Degree

Master of Science

Department

School of Business Graduate Program

Available

On Campus & Online

Contact

Office of Graduate Admissions1-888.511.1306[email protected]
Apply Now

The School of Business is redesigning and enhancing the Financial Analytics Program with the Financial Technology and Analytics program. The last semester we will accept applications to the FA program will be for Fall 2023.

The Master’s in Financial Analytics teaches professionals to apply their physics, statistics or engineering skills to the lucrative finance industry. You’ll learn from faculty whose insights are changing how the industry is thinking about concepts like machine learning, data modeling and optimization in finance. You’ll take courses in one-of-a-kind financial labs, home to the data analysis and visualization tools used on Wall Street.

The Financial Analytics program is structured around a core of courses covering data science, risk management and time series, and their applications in finance. The core concludes with a capstone consulting or research experience. You will customize your degree through a set of electives, including the chance to pursue a structured specialization tailored to your career interests. A close relationship between you and your faculty advisor will help you select the right classes for your future.

Core courses

Courses in this block go beyond important foundational concepts for financial analysts, immersing you in new programming languages, data sets and analytical technologies. 

FA 541 Applied Statistics with Applications in Finance
This course prepares students to employ essential ideas and reasoning of applied statistics. The course provides students with a solid foundation for solving empirical problems with the ability to summarize observed uni- and multivariate data, and to calibrate statistical models. While financial applications are emphasized, the course may also serve areas of science and engineering where statistical concepts are needed. The course will familiarize students with the use of R for statistical data analysis.

FA 582 Foundations of Financial Data Science (2 credits)
This course provides an overview of issues and trends in data quality, data storage, data scrubbing and data flows. Topics include data abstractions and integration, enterprise-level data issues, data management issues, similarity and distances, clustering methods, classification methods, text mining, and time series. Furthermore, the Hadoop-based programming framework for big data issues will be introduced, along with any governance and policy issues. 
Corequisite: FE 513

FE 513 Practical Aspects of Database Design (1 credit)
The course introduces required techniques and fundamental knowledge in data science techniques. It familiarizes students with database and data analysis tools, teaching them to manage databases and solve financial problems using R. 

FE 535 Introduction to Financial Risk Management
The course reviews different topics related to risk modeling and management, specifically in line with Financial Risk Manager (FRM) Parts I and II. The class begins with basic topics related to quantitative analysis and financial market products before moving on to advanced topics related to market and credit risk management. Project work and weekly tutorials help reinforce lessons from class.

FA 590 Statistical Learning in Finance
This course provides an applied overview of both classical linear approaches to statistical learning and more modern statistical methods. The classical linear approaches include logistic regression, linear discriminant analysis, k-means clustering and nearest neighbors, while modern approaches include generalized additive models, decision trees, boosting, bagging and support vector machines.

FA 542 Time Series with Applications to Finance
In this course, students learn how to estimate financial data models and predict using time series models. The course covers linear time series (ARIMA) models, conditional heteroskedastic models (ARCH type models), nonlinear models (TAR, STAR, MSA), nonparametric models (kernel regression, local regression, neural networks) and nonparametric methods of evaluating fit. The course also introduces multivariate time series models, such as VAR.
Prerequisite: Any of FE 541, MA 331, MA 541 or MA 612

Concentrations

There are endless career opportunities for professionals with the skills taught in this program. Four elective courses allow you to further explore a particular career path. Three popular tracks are outlined below.

Ideal candidates for the program will have a strong background in statistics, mathematics, physics, engineering or a related discipline, or will have several years' experience working in finance with an interest in exploring these disciplines in great depth. 

Application deadlines

Full-time applications to the Financial Analytics program are accepted in three distinct cycles. To be considered for admission, all materials must be submitted by the deadline.

Application cycle

Application deadline

Application decision

Acceptance deadline

Priority

Oct. 15

Dec. 1

Jan. 15

Standard

Jan. 15

March 1

Included in admission offer

Final

April 15

May 1

June 1

Part-time applications to this program are accepted on a rolling basis.