Financial Analytics Certificate
DepartmentSchool of Business Graduate Program
ContactOffice of Graduate Admissions1-888.511.1306[email protected]
Get the most in-demand skills
The Financial Analytics certificate provides students with an array of statistical learning methods and database skills, preparing them to create specific tools to manage enterprise-level challenges, and includes heavy emphasis on optimization of financial data visualization techniques. The program was co-developed with Accenture, ensuring the skills taught in classes reflect the most in-demand techniques in the workplace. A hallmark of the program is its emphasis on practical education, which empowers graduates to apply what they've learned in solving real problems at work.
This certificate is ideal for finance professionals who wish to improve their skills in this rapidly developing field, as well as traditional financial engineers, quantitative analysts and others who wish to broaden their horizons from the traditional banking and capital markets disciplines.
Through completion of this program, students will master the following skills:
Address data quality, composition, extraction, storage and retrieval issues.
Apply analytical methods, including statistical learning methods and data mining techniques, to solve problems in the finance discipline.
Organize and execute end-to-end business analytics solutions at the enterprise level.
Communicate and analyze data and insights through financial data visualization.
FA 582 Foundations of Financial Data Science (2 credits)
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, and preprocessing and querying. Furthermore, the Hadoop-based programming framework for big data issues will be introduced, along with any governance and policy issues.
FE 513 Practical Aspects of Database Design (1-credit lab)
The course provides a practical introduction to SQL databases and Hadoop cluster systems as available in the Hanlon Financial Systems Lab. Students will receive hands-on instruction about setting up and working with databases. Most of the software will be introduced using case studies or demonstrations, followed by a lecture of related fundamental knowledge. The course covers SQL, NoSQL and database management systems. The course will cover accessing databases using API.
FA 590 Statistical Learning in Finance
This course covers a range of topics in statistics, including an introduction to information theory, data conditioning, pattern recognition-based modeling and data mining, and adaptive learning systems and genetic algorithms. Case studies emphasizing financial applications — handling financial, economic, market, and demographic data; and time series analysis and leading indicator identification.
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.
FA 550 Data Visualization Applications
FE 541 Applied Statistics with Applications in Finance
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.
Prerequisite courses and skills
To keep up with high-level coursework, students must have completed coursework or demonstrate industry experience in each of the following areas:
Calculus and differential equations.
Probability and statistics.
Programming (R, Python, Java or C++).
If desired, the above courses can be applied to a full graduate degree from the School of Business, such as the master's programs in Financial Analytics or Financial Engineering.