MS-BA fintech track

Ensuring the foundational understanding of contextualized analytics within the business enterprise continuum by covering how data flows and is managed across the landscape of enterprise business processes.

Provides a foundation in programming fundamentals, the skills to combine and manipulate structured and unstructured data, and the ability to summarize, visualize, and draw insights from the data. This course may be waived with demonstrated Python proficiency.

Provides a solid foundation and deeper understanding of the use of quantitative modeling tools and techniques to solve problems faced in modern supply chains. Uses Excel workbooks to implement the appropriate quantitative methods, including forecasting demand, capacity planning of a manufacturing line and the line cycle time as it pertains to parts inventory management.

Addresses the use of analytics tools and techniques to enhance the ability of quality management approaches to improve processes. Introduces modern quality management approaches including six sigma and design for six sigma. Covers the define, measure, analyze, improve and control (DMAIC) improvement cycle: the core process used to drive six sigma projects. DMAIC refers to a data-driven improvement cycle used for improving, optimizing and stabilizing business processes and designs. Provides an analytics roadmap to help users work through the DMAIC problem-solving process.

Charting a roadmap for data-driven decision making and getting a practical understanding of how IT tools and techniques can allow managers to extract predictive analytics and patterns from primarily numeric data.

Uses tools and techniques to analyze unstructured data that are applied to business problems to support informed decision making and the extraction of predictive analytics and patterns from primarily nonnumeric data.

Addresses the skills and knowledge necessary to model situations where uncertainty is an important factor. Covers models including decision trees, queuing theory, Monte Carlo simulation, discrete event simulation and stochastic optimization. Uses these models for a variety of common business problems and requires implementation of these models using Excel and standalone software. Studies how to ensure that these solutions work in a wide variety of situations (what-if analysis). Describes each of these methods in detail.

Involves formulating critical marketing problems, developing relevant testable hypotheses, collecting and analyzing data and, most importantly, drawing inferences and suggesting actionable implications.

Focuses on a system designed to help students take an active role in their career development through self-reflection, skills and values assessment, market research and identifying potential roadblocks to obtaining an internship or full-time role after graduation. Also introduces the concept of a personal narrative and provides experiential learning opportunities to refine their own personal narrative and understand how to fine-tune and tailor it for a variety of career applications.

Develops analytical techniques and financial theories used to make optimal decisions in a corporate setting.

Deep learning applications have become an integral part of our lives over the last decade. Alexa, Amazon Go, Waymo, Apple Face ID, and Facebook's face recognition applications are all powered by deep learning networks. Applications based on deep learning models cover a wide spectrum of industries including retail, automotive, manufacturing, health care, banking, insurance, agriculture, security and surveillance. Hands-on look at the latest models, trends and challenges of deep learning applications in business.

A small class emphasizing discussion, presentations by students, and written research papers.

Contemporary financial accounting and reporting systems; emphasizes the interpretation and evaluation of a company's external financial reports.

Empirically investigates properties of financial data such as basic probability theory, matrix algebra, ordinary least squares, and maximum likelihood estimation. Explores these methods both through algebraic derivation and programmed implementation in PYTHON. Provides the basis for portfolio optimization by focusing on the estimation and testing of financial factor models.

A small class emphasizing discussion, presentations by students, and written research papers.

Investigates financial data by using techniques such autoregressive and vector-autoregressive models, dimension-reduction techniques motivated by latent factor models and machine learning dimension-reducing techniques. Explores these methods using algebraic derivation and implementation in PYTHON. Builds on the statistical and programming skills developed in FIN 509 and emphasizes forecasting for the optimization of portfolios.

Intermediate- to advanced-level course in derivative assets such as options, forward and futures contracts, swaps and financial engineering.

Presents the fundamental principles of risk and return, portfolio diversification, asset allocation, efficient markets, active portfolio management, portfolio evaluation. Reviews selected alternative investment strategies such as hedge fund investments.

Preparation of a supervised applied project that is a graduation requirement in some professional majors.