FNCE 5353 - Financial Modeling with C# (3 credits)
The goal of this course is to introduce the student to financial models within the framework of a C# deployment. It is meant to fully prepare the student for a work environment. This course will cover the gamut of C#, giving the student fluency in programming financial applications. It will introduce modelling and data structures as well. Financial applications begin with simple interest rate calculations and progress through option pricing models. Applications focus on numerical methods. All code will be written in C#. (Pre-req. FNCE 5710, OPIM 5604)

FNCE 5712 - Fintech Economics & Business Models (3 credits)
This course addresses the economics within the Fintech ecosystem, its various business models, and value creation with emphasis on the competitive landscape in Payments, Wealth management, Crowdfunding and Lending. Topics include contract theory and game theory. (pre-req. FNCE 5710 and 5711)

FNCE 5721 - Blockchain Applications (3 credits)
This course expands on PKC, data structures, Consensus algorithms, data structures – Merkle trees Consensus Algorithms. Explores uses of blockchain as a GPT technology. (pre-req. FNCE 5711, 5720, OPIM 5513).

OPIM 5501 - Visual Analytics (3 credits)
Explores techniques and best practices in visualizing data. From simple cross tabs to more complex multi-dimensional analysis, explores why particular data visualizations can better illustrate patterns and correlations inherent in the data itself. Examines cognitive function and its role in data visualization designs; showing that data visualization can reveal answers and questions alike. Utilizing state of the art software, the use of parameters, filters, calculated variables, color, space and motion to visually articulate the data are surveyed. The use of dashboards to quickly reveal data-driven information that has daily relevance to executives, managers, supervisors and line personnel are investigated. Common pitfalls in visualization design and why less is often more are considered.

OPIM 5603 - Statistics in Business Analytics (3 credits)
Advanced level exploration of statistical techniques for data analysis. Students study the concepts of population and sample; discuss the difference between population parameters and sample statistics, and how to draw an inference from known sample statistics to usually unknown population parameters. Topics will focus on rigorous statistical estimation and testing. Prepares students with the skills needed to work with data using analytics software. (no pre-req.)

OPIM 5604 - Predictive Modeling (3 credits)
Introduces the techniques of predictive modeling in a data-rich business environment. Covers the process of formulating business objectives, data selection, preparation, and partition to successfully design, build, evaluate and implement predictive models for a variety of practical business applications. Predictive models such as neural networks, decision trees, Bayesian classification, and others will be studied. The course emphasizes the relationship of each step to a company's specific business needs, goals and objectives. The focus on the business goal highlights how the process is both powerful and practical. (Co-req. OPIM 5603)