Business Analytics

Units: 1

Business cases and problems where data analysis is part of the decision-making process. Applications to finance, management, marketing, and operations. Proficiency in Excel methods commonly used in management. Completion of a project where students follow a business problem from formulation to solution using data analysis. Restricted to MBA students.

Offered in Fall Spring Summer

Units: 1

Continuation of a series of business cases and problems where data analysis is part of the decision making process. Estimation of linear relationships among variables, with applications to finance, management, marketing, and operations. Proficiency with Excel methods commonly used for estimation. Completion of a project where students follow a business problem from formulation to solution using the methods covered the course. Restricted to MBA students.

Offered in Fall and Spring

Units: 3

This course is designed around the full analytics lifecycle which encompasses the business problem, the data, the analysis, and the decision. Students will learn to identify and clearly explain business problems that can be addressed with analytics. They will learn to determine which analytic methods are best suited to solve particular problems and clearly explain the results of an analytic model and how those results might impact the business bottom line. Analytical methods to be covered include data, visualization, a review of regression analysis; logistic regression; classification and regression trees [including boosting and bagging methodologies]; and clustering [segmentation] methods. Students will also develop at least a beginning proficiency with several statistical software packages including Tableau, JMP, R, and SAS Enterprise Miner. Emphasis will be placed on analyzing real data and understanding how analytical thinking can be applied to solve big data problems.

Offered in Fall and Spring

Units: 3

This course examines how to collect and process data to make it useful, how to validate, protect, and process data to make it available, and how to create a place to properly store data.

Offered in Fall and Summer

Units: 3

This course focuses on solving a real-world business problem that includes a heavy data analytic component. The business problem will vary according to the client but could include problems from finance, human resources, marketing, finance, supply chain, or other management areas.

Offered in Fall and Spring

Units: 3

Applied approach to managing the risks that can prevent an organization from achieving its objectives, both financial and nonfinancial, by working in teams to address real problems in real organizations.

Offered in Spring Only

Units: 3

Advanced quantitative course on applied equity valuation. Students conduct stock valuation analysis which is then used to select stocks for the student-managed SunTrust MBA fund. Topics include the investment decision making process, empirical evidence on securities returns, forecasting financial statements, industry and macro-economic analysis, valuation models, portfolio performance evaluation and performance attribution. Students will also learn how to write computer programs using SAS software in order to generate statistical tests of investment strategies using "big financial data."

Offered in Spring and Summer

Units: 3

Research project examining supply chain management issues at an organization, usually a member of the Supply Chain Resource Cooperative. Projects will typically focus on procurement, logistics, materials management, operations, or integrated supply chain issues.

Offered in Fall and Spring

Units: 3

This class provides the opportunity to learn about business consulting and be part of a consulting team, helping real clients with real business challenges and market opportunities. Students will help their client organization by understanding a problem, conducting analyses, and suggesting relevant, actionable steps that clients can take to become more competitive or achieve important goals. Projects will deal with creative, complex, risky, and ambiguous issues involved in developing new products/services, serving new markets, achieving quality standards, or creating new business models in an enterprise setting.

Offered in Fall Spring Summer

Units: 3

Introduction and application of econometrics methods for analyzing cross-sectional data in economics, and other social science disciplines, such as OLS, IV regressions, and simultaneous equations models. Students should have had a statistical methods course at the 300 level or above as well as Calculus I and II.

Offered in Fall Only

Units: 3

This course is a continuation of Applied Econometrics I [ECG 561]. After a review of probability and statistics, and simple and multiple regression models, we explore the following topics: regression using panel [longitudinal] data, instrumental variables regression, regression with a binary dependent variable, prediction with many regressors and ``Big Data'' methods, and time series regression. The emphasis is on recognizing the conditions in which it is appropriate to apply the various techniques, formulating a relevant model, estimating the model and interpreting the results. This course will also provide the students practical experience in applied econometrics using STATA.

Offered in Spring Only

Units: 3

The goal of this course is to introduce students to a wide range of methods, which are designed to tackle commonly seen real-world problems, and are intensively used in the current literature. These methods include linear regression, logistic regression, bootstrapping, cross validation, bagging, boosting, splines, random forests, neural networks, and support vector machines. This course is application oriented. We will emphasize the intuition behind each method and touch on a little bit of theory.

Offered in Fall Only

Units: 3

This course focuses on building a framework for understanding how operations decisions are made and how those decisions shape the firm's ability to effectively utilize its physical and human resources. It further explores how the physical and human resources help meet customer requirements through processes that convert diverse inputs into customer-valued outputs. Key topics include metrics for flow rate, flow time, and work in process, and the influence of resource decisions, uncertainty, buffering, batching, and control policies like "push" and "pull." Excel-based simulations and case studies are used to illustrate the principles and concepts listed above.

Offered in Fall and Spring

Units: 3

Structured framework for modeling and analyzing business decisions in the presence of uncertainty and complex interactions among decision parameters. Topics include decision models, value of information and control, risk attitude, spreadsheet applications, and decision analysis cycle. Interactive case study.

Offered in Fall Spring Summer

Units: 3

The objective of the course is to build an understanding of how to manage and improve the performance [efficiency and responsiveness] of operations and supply chains through decision making that is based on analysis and facts, rather than intuition. The course introduces fundamental aspects of operations and supply chain management as well as analytical modeling tools and techniques that can be used to support decision making [e.g., optimization, regression analysis, simulation]. The approach taken in the course is entirely example-based and hands-on, since all these techniques will be implemented in Excel, either with Excel's built-in tools or with Excel add-ins.

Offered in Fall Only

Units: 3

Analytical techniques to convert a wealth of data on customers and markets into insights to guide business decisions. Taking a hands-on and systematic approach on the steps involved in harnessing knowledge from data, the course covers the various data techniques and steps involved in data- and model-driven management decisions. Techniques include market response models, conjoint analysis, discrete choice models.

Offered in Fall and Spring

Units: 3

This course covers the basics of digital marketing from an analytics perspective. Each channel of digital marketing, such as search engine optimization, social, mobile, web, email, and video, are examined and their relationship to overall firm marketing strategy is explored.

Offered in Fall and Spring

Units: 1

This course considers the use of analytics in decision-making in a variety of settings [e.g., business, public policy, personal]. Students will discuss the importance of properly identifying causal relationships when using data to make well inform decisions and be able to identify potential threats to reliable causal inference that commonly arise.

Offered in Spring Only

Units: 1 - 6

Presentation of material not normally available in regular courses offerings or offering of new courses on a trial basis.