Certificate in Financial Services Analytics
Harness the Power of Data-Driven Business Insights and Advanced Financial Analytics
The Financial Services Analytics Certificate teaches you the science and technology of creating data-driven insights and analytics decision-making for the financial services industry. Use these insights to increase the effectiveness of business operations, enhance customer relationships, improve product offerings and improve risk analysis and risk management. You will learn various statistical learning methods and database skills to create end-to-end business decision-making data analytic tools from an enterprise-level systems approach.
The certificate is 15 credits.
Curriculum
FA 541 - Applied Statistics with Applications in Finance
The course prepares students to employ essential ideas and reasoning of applied statistics. Topics include data analysis, data production, maximum likelihood, method of moments, Bayesian estimators, hypothesis testing, tests of population, multivariate analysis, categorical data analysis, multiple regression, analysis of variance, nonlinear regression, risk measures, bootstrap methods and permutation tests. The course is designed to familiarize students with statistical software needed for analysis of the data. Financial applications are emphasized but the course serves areas of science and engineering where statistical concepts are needed. This course is a graduate course and is covering topics for a deeper understanding than undergraduate courses such as MA331 and BT221. Furthermore, the course will cover fundamental statistical topics which are the basis of any advanced course applying statistical notions such as MGT718, BT652 as well as courses on machine learning, knowledge discovery, big data, time series, etc.
FA 582 - Foundations of Financial Data Science
This course will provide an overview of issues and trends in data quality, data storage, data scrubbing, data flows, and data encryption. Topics will include data abstractions and integration, enterprise level data issues, data management issues with collection, warehousing, preprocessing and querying. 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 digital marketing and computational advertising, energy and healthcare analytics, social media and social networks, and capital markets financial data. A one credit Hanlon lab course, FE 513: Practical Aspects of Database Design will be attached to this course in order to facilitate learning of the practical side of data management.
FA 590 - Statistical Learning
This course offers a comprehensive overview of both classical and modern statistical learning methods with a strong emphasis on their application in finance. The classical approaches covered include linear regression, logistic regression, and k-Nearest Neighbors (k-NN), providing foundational tools for prediction and classification. The course will also explore modern methods such as decision trees, ensemble techniques (boosting and bagging), support vector machines, and neural networks, as well as advanced topics like model assessment, feature selection, and unsupervised learning techniques like clustering. Throughout the course, students will apply these methods to real-world financial datasets, gaining hands-on experience in statistical learning as it pertains to asset pricing, portfolio optimization, and other key areas in finance.
FA 595 - Financial Technology
This course deals with financial technology underlying activities of markets, institutions and participants. The overriding purpose is to develop end-to-end business decision making data analytics tools along with enterprise level systems thinking. Statistical learning algorithms will be connected to financial objects identification and authentication along with the appropriate databases to create enterprise level financial services analytics systems.
FE 513 - Financial Lab: Practical Aspects of Database Design
The course provides a practical introduction to fundamental data science techniques. Students will become familiar with databases and working with data analysis tools. Students will be able to manage data in various databases and solve financial problems using R program packages. This course is designed for graduate students in the Finance programs at the School of Business.
FA 550 - Data Visualization Application
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.