Online MS in Finance and Analytics Courses

The objective of this course is to provide a foundation in the basic concepts of corporate finance, particularly the role of the financial manager and the goal of financial management. For this purpose, the course focuses on agency conflicts, business ethics and corporate governance, capital structure, payout policy, financial distress, options (real and executive), derivatives/hedging, and international issues. The application of these techniques has gone beyond the simple corporate budgeting context and has extended to mergers and acquisitions (M&A), private equity transactions such as leveraged buyouts (LBOs), investment banking, and commercial real estate and infrastructure transactions.

The objective of this course is to provide a comprehensive background in the mathematical topics required for learning quantitative finance (QF) and business analytics and data science (BADS). The mathematical topics covered include calculus, linear algebra, and probability theory. Applications of these topics in a variety of business contexts will be learned with R.

This course introduces database management and database management systems (DBMS). Teaches technical and managerial skills in database planning, analysis, logical design, physical design, implementation, and maintenance. Features hands-on training in database design, development, and implementation using relational DBMS software. Emphasizes designing and developing reliable databases to support organizational management.

This course introduces a broad set of econometric tools to analyze large-scale, real-world company data to make data-driven business decisions. Topics include the ordinary least squares (OLS), model selection, generalized least squares (GLS), instrumental-variables regression, quantile regression, count data models, binary outcome models, and selection models.

Data analytics involves the application of scientific methodologies to extract, understand, and make predictions based on data sets from a broad range of sources. Data analytics requires knowledge and skills from three areas: (i) programming, (ii) math/statistics, and (iii) domain-specific expertise. The objective of this course is to teach the programming skills relevant to data science. Students will learn to use a complete set of open source tools for data science in Python, including the Jupyter Notebook, NumPy, Pandas, Seaborn, scikit-learn, Colab, and many others. Students will learn skills that cover the various phases of exploratory data analysis: importing data, cleaning and transforming data, algorithmic thinking, grouping, aggregation, reshaping, visualization, time series, statistical modeling, and data exploration and communication of results. The course will utilize data from a wide range of sources and will culminate with a final project and presentation.

This course explains the foundation blocks of the investments industry, key stakeholders in the industry and drivers for their actions including any ethical aspects, the evolution of the industry, its growth in the global setting, regulations, the industry’s current state, and key trends likely to shape the future. It explains rational and normal behavior, standard and behavioral portfolios, standard and behavioral life-cycles of saving and spending, standard and behavioral asset pricing, and standard and behavioral market efficiency. It combines the theoretical underpinnings of finance with real-world examples. Before taking the course, students should understand the time value of money (discounting), capital budgeting, and evaluation of two-stock portfolios.

Practicum projects are defined by external partners who provide a dataset and the question of interest. Starting from a real-world problem and using input from the partner, students will refine the problem to scope the project, apply analytical tools to generate insights, interpret the findings, summarize the findings in a report, and present them to the partner or faculty supervisor. Practicums occur over two quarters and are worth 2 units of course credit each quarter or 4 total units.

Capstone projects use data and examine analysis questions provided by faculty over the course of one quarter. Students refine the problem to scope the project, apply analytical tools to generate insights, interpret the findings, and summarize the findings in a final report. In certain situations, students with full- or part-time jobs or with an internship can approach their employers to find a suitable project if desired. In those instances, the employer and faculty would provide supervision.

This course is designed to provide a comprehensive introduction to forecasting methods used in time series analysis. The class covers a range of topics in time series forecasting. The class will provide you with a language to describe time series data and ultimately cover modeling techniques such as ARIMA, SARIMA, and GARCH to produce forecasts.

This course covers key issues in panel data analysis, with an emphasis on their applications in empirical research, especially empirical corporate finance. The course aims to introduce various econometric methods for analyzing panel data and develop core techniques to identify causal relations in the data. We will begin with the standard linear regressions, and extend to pooled, fixed effect, and random effect regression models; instrumental variables; differences-in-differences; selection models; and regression discontinuity. Students will be exposed to a broad range of applications in finance through reading academic papers and conducting their own empirical analysis.

Examines corporate governance and corporate restructurings. Emphasizes how corporate ownership, control, and organizational structures affect firm value. Other topics include valuing merger candidates, agency theory, and takeover regulation. Places a heavy emphasis on case projects and/or class presentations.

FinTech has rapidly become a prevalent part of our vernacular, and an understanding of the evolution of traditional finance methods is an important part of a finance major’s arsenal. This course covers the evolution of traditional finance methods—namely, the disruptions and innovations that have transformed: (i) how we access capital, (ii) how we allocate or invest capital, (iii) how we settle or transfer capital, and (iv) how we monitor and maintain the integrity of financial institutions and transactions.

Alternative investments contrast to widely-held investments like stocks, bonds, and mutual funds. This course covers how these investments are generally structured along with a closer study of a particular category, venture capital.

Examines the design, valuation, and risk management of derivative securities (futures, options, etc.), including structured products. Includes topics on arbitrage theory, futures, equity options, bond options, credit derivatives, swaps, and currency derivatives. Mathematical modeling of derivatives, including implementation and applications in investments, corporate finance, and risk management.

Discusses implementing finance theory for valuation problems. Provides practical valuation tools for valuing a company and its securities. Covers valuation techniques including discounted cash-flow analysis, estimated cost of capital, market multiples, free-cash flow, and pro forma models.

Prepares managers to (1) identify the competitive advantages that come from leveraged analytics, (2) apply the tools and evaluate the advantages and limitations of each, (3) implement these tools and ask relevant business questions that could be solved with them, and (4) interpret the input and communicate the output from such tools and models to achieve more profitable business decisions.

This course introduces participants to quantitative techniques and algorithms that are based on big data (numerical and textual) or are theoretical models of big systems or optimization that are currently being used widely in business. It introduces topics that are often qualitative but that are now amenable to quantitative treatment. The course will prepare participants for more rigorous analysis of large data sets as well as introduce machine learning models and data analytics for business intelligence.

TBD