This course, led by Professor Michael McDonald, introduces the fundamentals of algorithmic trading and how modern financial markets operate. It covers the development of rules-based, back-tested trading strategies and guides you through programming a basic trading algorithm. Topics include market instruments like stocks, bonds, and derivatives, as well as regression-based value prediction and practical examples of automated trading in real-world scenarios.

This course provides a foundational understanding of corporate finance and its influence on business decisions and growth. Taught by professors Jim and Kay Stice, it covers key concepts such as risk management, diversification, capital structure, and the use of debt vs. equity. It also introduces the Capital Asset Pricing Model (CAPM) and explains how financial decisions affect a company's performance and valuation.

In this course, Professor Michael McDonald demonstrates how to use Microsoft Excel for predictive analytics to forecast key business metrics like sales, cash flow, and pricing. You'll learn to apply regression analysis, calculate confidence intervals, and perform stress testing using Excel’s built-in tools—equipping you to generate practical, data-driven forecasts for real-world business needs.

Validates foundational knowledge of core programming concepts, software development practices, and essential object-oriented programming principles. Designed for aspiring software developers and students, it serves as an entry-level credential that introduced key technical skills across multiple development environments.

The basic concepts and analytical tools of finance in both corporate finance and investments. Topics include risk and return, financial institutions, efficient markets, valuation theory, capital budgeting, portfolio theory, cost of capital, and international finance.

Development of a framework that is useful for understanding a broad range of important corporate financial decisions. Substantial emphasis will be placed on discussion of the determinants of corporate financing and payout policies, alternative methods of security issuance, and mergers and acquisitions.

An introduction to current practices in financial modeling. Students will learn how to apply financial models with financial data to perform analysis. This hands-on course provides the skills to apply the theories, concepts, and spreadsheet tools to develop effective financial analysis and decision-making.

Software development techniques in an object-oriented computer language. An intermediate programming course emphasizing systems methods, top-down design, testing, modularity, and structured techniques. Applications from areas of numeric and non-numeric processing and data structures.

A survey of the fundamental mathematical tools used in Computer Science: sets, relations, and functions; propositional and predicate logic; proof writing strategies such as direct, contradiction, and induction; summations and recurrences; elementary asymptotics and timing analysis; counting and discrete probability with applications in computer science.

The design, analysis, and implementation of data structures and their associated algorithms. Lists, stacks, queues and priority queues, trees, graphs, dictionaries, time and space complexity, sorting and searching, advanced problem-solving, and algorithm design strategies.

Analysis of business data through appropriate statistical techniques and software. Develop aptitude for data modeling through data exploration and visualization. Apply proper sampling methods to minimize bias associated with sampling. Employ statistical inference as a tool for decision making. Utilize linear regression models and time series models to analyze and inform business decisions.