Evening MBA

Analytic Management

Technological innovations continue to increase the types and amount of data businesses collect and analyze. As a result, companies are using more sophisticated quantitative and statistical tools to analyze a wide set of business issues and processes, and leading businesses are increasingly turning to quantitative analysis to underpin the development of their competitive strategies.

Data-driven analysis lies at the heart of learning at Chicago Booth. Our emphasis on and excellence in quantitative and statistical analysis for decision making is unmatched. These analytic methods are applied to a wide range of industries and functions such as understanding advertising and consumer behavior; analyzing risk and incorporating uncertainty; identifying profitable customers and determining optimal pricing policies; accelerating product innovation; optimizing supply chains; identifying the drivers of financial performance; and allocating resources.

Co-curricular-ActivitiesYou'll have the chance to explore activities outside the classroom in numerous ways that will also allow you to build new skills, relationships, and networks. These include:




  • Booth Consulting Club - The mission of the Booth Consulting Club (BCC) is to provide experienced working professionals with frequent and meaningful opportunities to enhance their learning and career potential in the consulting industry. While the BCC is open to all members of the Booth community, including students and alumni of the Full-Time and Executive MBA programs, it primarily serves the needs of Evening and Weekend MBA students. With this in mind, the BCC provides a forum for its members to share experiences and exchange information, and provides opportunities for industry leaders, faculty, Booth alumni, and firms to present information about the industry.
  • Corporate Strategy and Management Group - The CSMG provides a forum for students who have an interest in corporate strategy and general management.

You’ll have the option of taking courses that address your individual career choices. Samples include: 

  • Managerial Decision Modeling - Successful decision-making requires the ability to structure complex problems, to analyze available options in an uncertain world, and to finally make the best decision, given the information available. You'll learn how to apply analytical tools including optimization, simulation, and decision trees to examine managerial decision models. Business applications include resource allocation, risk analysis, and sequential decision-making through time.
  • Data Mining - In the age of the internet, businesses must process vast amounts of data. For example, firms maintain large databases on their customers. What can be learned from this data to help serve customers? How does a business use its information for customer-relationship management? Modern statistical methods have been developed to help us deal with large and complex data. The term "data-mining" refers to this collection of methods. This course is designed to familiarize MBA students with relevant and helpful data-mining tools. The course is not mathematical in nature. Attention is focused on interpretation of results and how to obtain them using software. While the primary emphasis of the course will be on the data-mining or statistical methods, we will relate them to management issues through readings and examples. Topics include graphics, cluster analysis, multidimensional scaling, discriminant analysis, logit models, regression and classification trees, neural networks, and issues in data collection and management.
  • Strategic Investment Decisions - This course integrates advanced analytical techniques with intuitive economic (strategic) analysis - with an eye on how organizations really make decisions. You will learn to develop and apply a variety of tools to achieve greater understanding and sophistication in all aspects of the processes by which companies make strategic investment decisions. The main goal will be for students to learn to use option pricing, dynamic programming, decision trees, simulation techniques, scenario analysis, and game theory to value investment opportunities. The focus will be to both incorporate the value from flexibility, delay, strategic responses, and learning into the analysis and to develop the tools to model and evaluate the full range of environmental and strategic uncertainty that companies face. We will also study the organizational processes required for effective, strategic investment decisions and resource allocation. Among the issues we will discuss are decentralization/centralization, incentives, measurement, and communication processes.
  • Data-Driven Marketing - The availability of data on actual market behavior of consumers is revolutionizing the way marketing is conducted, as well as the way in which marketing activities are planned and evaluated. This course introduces students to these new data sources and provides a set of innovative analytic tools.
  • Financial Engineering - Cases in Financial Risk Management - Financial risk management is reported to be the main reason for the use of financial derivatives by non-financial institutions. This course has two main objectives. The first is to cover techniques to identify, measure, and manage corporate financial risk, as modern financial markets and regulation require. Specifically, topics of discussion will include dynamic hedging and portfolio replication, the development of value-at-risk, the management of exchange-rate risk, interest-rate risk, credit risk and operation risk. The second main objective is to build a framework to integrate financial risk management solutions with long-term corporate strategy. We will discuss cases where the use of financial engineering was vital for the success of a business strategy. Typical applications in this case include privatizations, mergers and acquisitions, and financing strategies, among others. However, the course will focus more on the uses of derivative securities rather than their technical aspects.

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