Online Financial Technology & Analytics Master's Program

FA 582 Foundations of Financial Data Science (2) & FE 513 Financial Lab (1)

FA 582 Foundations of Financial Data Science (2)

This course will provide an overview of issues and trends in data quality, data storage, data scrubbing, and data flows. Topics will include data abstractions and integration, data management issues with collection, warehousing, pre-processing, and querying, similarity and distances, clustering methods, classification methods, text mining, and time series. Case studies will be presented in support of the theoretical concepts. Furthermore, the Hadoop based programming framework for big data issues will be introduced along with any governance and policy issues. These concepts will be applied to areas such as real estate, social media and social networks, and capital markets financial data. A one credit Hanlon lab course.

FE 513 Financial Lab: Database Design (1) 

The course aims to introduce the required techniques and fundamental knowledge in data science techniques. It helps students to be familiar with database and data analysis tools. Students will be able to manage data in database and solve financial problems using R program packages. This course is designed for graduate students in the Financial Engineering program at the School of Business.

FE 540 Probability Theory for Financial Engineering (3)*

Topics include discrete and continuous distributions, multivariate probability, transformations, pattern appearance, moment generating functions, Laws of large numbers, Markov chains and diffusion processes, prices in markets as random variables and processes, filtrations and information. Applications target financial engineering examples.

Note: this course is required for students without strong mathematical/engineering background and may be waived for students who possess this background.

FA 541 Applied Statistics with Applications in Finance (3)

This course prepares students to employ essential ideas and reasoning of applied statistics. It teaches theoretical statistical concepts and tests the student’s understanding of them. 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 is designed to familiarize students with the use of R for statistical data analysis (familiarity with programming in R is assumed.

FE 535 Introduction to Financial Risk Management (3)  

The course will review different topics related to risk modeling and management, specifically in line with Financial Risk Manager (FRM) Parts I and II (mainly Part I). The class will begin with basic topics related to Quantitative Analysis and Financial Market Products, covering derivatives and options. The first part of the class will be dedicated to Market Risk and how to use derivatives to manage risk. Later in the class, we will cover topics related to Credit Risk, which will relate to measuring default risk and managing credit risk. Additionally, the students will have the opportunity to apply their knowledge in two mini-projects over the semester. Past tutorials are available to further assist the students to meet their goals and equip them with the necessary tools needed to tackle real data risk management problems. 

FA 590 Statistical Learning in Finance (3)  

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

Prerequisite: Knowledge of R (or willingness to learn) Prob/Stat background 

 FE 512 Database Engineering (3)  

This course will introduce a variety of software used to interact with data storage systems and manipulate data. The focus will be on how to perform common data engineering tasks, with a focus on the financial services industry, in both on-prem and cloud environments. 

FA 542 Times Series with Applications to Finance (3) or FIN 620 Financial Econometrics (3)  

FA 542 Times Series with Applications to Finance (3)

In this course the students will learn how to estimate financial data model and predict using time series models. The course will cover linear time series (ARIMA) models, conditional heteroskedastic models (ARCH type models), non-linear models (TAR, STAR, MSA), non-parametric models (kernel regression, local regression, neural networks), non-parametric methods of evaluating fit such as bootstrap, parametric bootstrap and cross-validation. The course will also introduce multivariate time series models such as VAR.

Prerequisite: FE 541 or MA 331 or MA 541 or MA 612 

FIN 620 Financial Econometrics (3) 

This course will cover the main topics of the analysis of time series to evaluate risk and return of the main products of capital markets. Students will work with historical databases, conduct their analysis, and conduct tests based on the techniques reviewed during the class. The significant amount of historical information available for most financial instruments requires a systematic and analytical approach to select an optimal portfolio. Time series analysis facilitates this process understanding, modeling, and forecasting the behavior of financial assets.  This course reviews the most important techniques used by investors, risk managers, and also by finance managers of non-financial service companies to analyze time series of their most relevant financial variables. Even though the methodologies reviewed during this course could also be applied to other domains such as marketing, the main emphasis of this class is on financial applications with special consideration to risk management.

Prerequisites: BIA 652 Multivariate data analytics or MGT 700 Econometrics 

FA 550 Data Visualization Application (3)

Effective visualization of complex data allows for useful insights, more effective communication, and making decisions. This course investigates methods for visualizing financial datasets from a variety of perspectives in order to best identify the right tool for a given task. Students will use a number of tools to refine their data and create visualizations, including: R and associated visualization libraries, Ruby on Rails visualization tools, ManyEyes, HTML5 & CSS 3, D3.js and related javascript libraries, Google Chart Tools, Google Refine, and image-editing programs.

FA 800 Project in Financial Analytics  (3)  

Three credits for the degree of Master of Science (Financial Analytics). This course is typically conducted as a one-on-one course between a faculty member and a student. A student may take up to two special problems courses in a master's degree program. A department technical report is required as the final product for this course.

FA 691 Deep Learning for Finance (1.5) and FA 692 Natural Language Processing for Financial Applications (1.5)  

FA 691 Deep Learning for Finance (1.5)

This course focuses on neural network models and their applications to finance. Building on fundamental statistical learning theory, the course covers advanced topics in deep learning and big-data analytics for classification and prediction. Learning and building from financial data sets, the lectures will introduce machine learning models in quantitative investing, portfolio management, algorithmic trading, risk management, client-relationship management, and beyond. A final project on related topics is required. 

Prerequisite: Students must have taken FA 590 or comparable introduction to machine learning methods 

FA 692 Natural Language Processing for Financial Applications (1.5)

This course focuses on natural language processing (NLP) models and their applications to finance. Building on fundamental machine learning theory and practice, the course covers advanced topics in natural language processing for analyzing financial reports and news. Learning and building from financial data sets, the lectures will introduce machine learning models in quantitative investing, portfolio management, algorithmic trading, risk management, client-relationship management, and beyond. A final project on related topics is required. 

Prerequisite: Students must have taken FA 590 or comparable introduction to machine learning methods