Online Financial Technology & Analytics Master's Program

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.