This course covers basic statistical techniques in a managerial setting, and features case studies and conceptual exercises. Statistical topics include effective use of numerical and graphical summaries, estimation and confidence intervals, hypothesis testing and regression. More advanced topics such as data mining, the Bayesian paradigm and principles of model building, may be encountered during projects.

This course examines managerial decision-making from an economic standpoint. The first half (microeconomics) explores how prices, wages and profits are determined in market economies; the advantages and disadvantages of unfettered competition; and the impact of government intervention on market outcomes. The second half (macroeconomics) investigates the factors influencing gross domestic product, interest rates, unemployment, inflation and growth; the causes of the business cycle; the role of the federal government and the Federal Reserve in stabilizing the economy; the impact of technology on productivity and growth; and the influence of international trade and finance on economic activity.

This course highlights how managers use cost, cash flow and financial reporting information in their decisions. It will introduce the student to (a) purpose of accounting and its role in making business decisions, (b) accounting principles, procedures and judgments underlying corporate financial statements, (c) use, interpretation and limitations of financial statements, (d) use and interpretation of cost accounting data in managerial decision-making, and (e) approaches to identify problems, analyze their financial and managerial implications, and evaluate alternative solutions.

The course presents differential and integral calculus in a single variable, with connections to further study in continuous probability, multivariate calculus and differential equations. Specific emphasis is placed on tools relevant to later study of computational finance. Topics include limits, continuity, differentiation of single-variable and multivariate functions, implicit differentiation, optimization, integration by substitution and by parts, numerical integration, and introductions to ordinary differential equations, continuous probability, and Taylor series.

This course provides fundamental knowledge in key areas of investments. In particular, the course will focus on portfolio theory, asset pricing, equity valuation, fixed income valuation and risk, and option pricing and strategies.

The course extends the basic understanding of financial concepts and tools by emphasizing the modern fundamentals of the theory of finance. It develops the ability to apply financial analysis, planning and valuation techniques to solving financial problems. The course covers issues related to how managers manage the assets in place, identify and evaluate future investment opportunities, and analyze sources and costs of capital necessary to fund these projects. Topics are presented in an environment that includes strategic, global and technological issues where appropriate and relevant.

Financial Modeling is focused on applying sophisticated Excel techniques to the most common modeling problems in finance. The skill set is expanded to include advanced features of Excel including TVM and statistical functions, array manipulation, text and date usage, regression, conditionals, Boolean operators, data tables and random number generation. Subsequently the course will cover macro recording as well as custom subroutine and function construction in the Visual Basic for Applications (VBA) development environment. All techniques learned will be applied to the most common financial modeling problems of the day including financial statement forecasting, present value, cost of capital, and valuation.

This course deals with the international aspects of corporate finance and investing. Areas covered include foreign exchange with emphasis on exchange rate determination, exchange risk, hedging and interest arbitrage, international money and capital markets and international financing, multinational capital budgeting, cost of capital and international portfolio management.

Provides students with an in depth coverage of simple and multiple linear regression methods and, as time permits, an introduction to the analysis of time series data. Simple and multiple linear regression techniques are covered including the use of transformations such as squares and logarithms, the modeling of interactions, and how to handle problems resulting from heteroscedasticy and multicollinearity . Issues surrounding outlying and influential observations are also covered. The art and science of model building are demonstrated with the help of cases. Autocorrelation is then considered, and an introduction to the ARIMA modeling of times series is provided. Makes use of statistical packages such as SAS, JMP, R or SPSS.

This course teaches foundational data management,retrieval, and manipulation skills with an emphasis on enabling the students to form a strong foundation for analytical processes. It builds a foundation for understanding various domains of practice with conceptual data modeling and demonstrates how the same conceptual needs can be served with different data management technologies. The course covers relational technologies for both operational databases and data warehouses and non-relational data management infrastructures for analytics. The course will help the students develop strong skills in the use of the SQL language for database definition and data manipulation.

This course provides fundamental knowledge in key areas of investments. In particular, the course will focus on portfolio theory, asset pricing, equity valuation, fixed income valuation and risk, and option pricing and strategies.

The course extends the basic understanding of financial concepts and tools by emphasizing the modern fundamentals of the theory of finance. It develops the ability to apply financial analysis, planning and valuation techniques to solving financial problems. The course covers issues related to how managers manage the assets in place, identify and evaluate future investment opportunities, and analyze sources and costs of capital necessary to fund these projects. Topics are presented in an environment that includes strategic, global and technological issues where appropriate and relevant.

Financial Modeling is focused on applying sophisticated Excel techniques to the most common modeling problems in finance. The skill set is expanded to include advanced features of Excel including TVM and statistical functions, array manipulation, text and date usage, regression, conditionals, Boolean operators, data tables and random number generation. Subsequently the course will cover macro recording as well as custom subroutine and function construction in the Visual Basic for Applications (VBA) development environment. All techniques learned will be applied to the most common financial modeling problems of the day including financial statement forecasting, present value, cost of capital, and valuation.

Provides materials and projects that will allow students to develop a detailed understanding of the design, mechanics and pricing of derivative securities in risk management. The concept of the law of one price will be stressed and includes the application of the tools and inputs (quantitative techniques as developed in ST625) necessary to value derivative securities. The mathematical requirements of the course are primarily algebraic, but the student will also need to rely on statistical methods and some calculus. Please note that this is not a survey course. It is an intensive introduction to derivative securities pricing and market mechanics.

The course covers the application of advanced mathematical methods to solve complex problems in Finance. Mathematical topics will include computational methods, simulation methods, numerical methods and stochastic processes. The course presumes an advanced understanding of financial markets and instruments and will use software such as MATLAB extensively for a series of application projects.

The course will provide a business-focused perspective on analytics in organizations, with emphasis on business fundamentals for analytics professionals (including how businesses are structured, functional areas, core business processes and associated performance metrics, and types of business decisions), the value of analytics in organizations (including organizational-level perspectives on value, managing with analytics, and constraints and consequences of analytics processes such as information security, privacy and ethics), and the practice of analytics (understanding and framing ill-defined business problems in various functional business areas, exploring and visualizing problem-related data, identifying actionable insights, and communicating the results at different organizational levels). The course will feature hands-on exercises with real-world data and analytics applications.

Provides students with an in depth coverage of simple and multiple linear regression methods and, as time permits, an introduction to the analysis of time series data. Simple and multiple linear regression techniques are covered including the use of transformations such as squares and logarithms, the modeling of interactions, and how to handle problems resulting from heteroscedasticy and multicollinearity . Issues surrounding outlying and influential observations are also covered. The art and science of model building are demonstrated with the help of cases. Autocorrelation is then considered, and an introduction to the ARIMA modeling of times series is provided. Makes use of statistical packages such as SAS, JMP, R or SPSS.

 Full-Time & Part-Time

 Full-Time & Part-Time