Flexible MBA Specialization in Business Analytics and Risk Management

Add a specialization in Business Analytics and Risk Management.

How do you make tough decisions when the stakes are high and you have limited data or precedent to benchmark against?  Bolster your ability to make decisions when facing the unknown with the latest tools from the emerging field of analytics. The business analytics and risk management specialization is a quantitative program with a focus on business decisions and applications, rather than code writing. Cover the essentials of statistical analysis, spreadsheet models, and data analysis, and add electives to delve deeper into analytical tools or focus on the use of those tools in marketing or operations management. Graduate with the expertise to harness data and navigate uncertainty with confidence.

Business analytics and risk management curriculum highlights

Required courses

BU.520.620 Advanced Business Analytics (2 Credits)

Learn to lead in the face of multi-dimensional uncertainty by developing and using optimization models. Create mathematical abstractions that deal with issues including resource allocation, scheduling, pricing, and other responses to the realization of a variety of “known unknowns.” Topics include linear programming, dynamic programming, multi-criteria optimization, and non-linear optimization.

BU.510.650 Data Analytics (2 Credits)

Gather, describe, and analyze data, using advanced statistical tools to support operations, risk management, and responses to disruptions. Analysis is done by targeting economic and financial decisions in complex systems that involve multiple partners. Topics include probability, statistics, hypothesis testing, regression, clustering, decision trees, and forecasting.


Electives

BU.450.760 Customer Analytics (2 Credits)

Immerse yourself in the modern practice of customer analytics. And learn how marketing practitioners can improve decision making by leveraging scientific approaches in the analysis of big data. Leading analytical techniques and data structures are illustrated in the context of their most prominent applications – for example, predicting customer responses to marketing campaigns and managing customer churn. The class has a strong hands-on component, enabled by several in-class examples and group assignments (implemented on Microsoft Excel and the statistical language “R”). You are not expected to become expert programmers or statisticians, but you will acquire basic skills and knowledge to orchestrate an effective analytics strategy.

BU.520.650 Data Visualization (2 Credits)

This course prepares you to make informed decisions based on data, using descriptive analytical techniques. View examples from real business cases where data visualization helps the decision makers to visualize, discover, and decode the hidden information from within the data, and to exploit such information for making educated decisions. Topics include cognition and visual perception; design principles; fundamental charts; interactive visualizations; storytelling and dashboards; advanced visualizations methods for: temporal, spatial, networks, trees, textual, high dimensional data, and advanced data visualization tools.

BU.520.701 Enterprise Risk Management Frameworks (2 Credits)

This course provides an introduction to the formal principles and practices of modern COSO- and ISO-style enterprise risk management. The course provides a framework that integrates the core, foundational, and elective courses in the school’s enterprise risk management curriculum. A combination of didactic lectures, group conversation, and student presentations will be used to impart the material and bring it alive.                                                                                      

BU.230.730 Managing Financial Risk (2 Credits) 

Gain a foundation in financial risk management, the complex process of identifying, measuring, and controlling risk exposure. The course will balance theory and practical application. Topics include market and credit risks, liquidity, and operational and legal risks, including volatility modeling, and derivatives as tools for controlling risk. Using modern econometric models, such as ARCH and GARCH, along with widely used quantitative methods (Monte Carlo simulation and Filtered Historical simulation), the course will describe how to measure and control risk exposure towards various types of risks, especially market and credit risk.

BU.450.740 Retail Analytics (2 Credits)

The retail and service sector is the largest of all economic activities and evolving rapidly in the age of big data and artificial intelligence. This course will leverage data-driven tools and theoretical models to analyze decisions of retail firms. We will cover a wide range of topics in strategic decisions in retailing: pricing, location, franchising, and omni-channel retailing. Using the real data in retailing, we will demonstrate and implement a wide range of statistical methods in econometrics and machine learning: single and multi-variate linear regressions, logistic regressions, classification trees, random forest, and multi-layer neural network. The focus is on predicting the effects of marketing decisions on profitability, although we will touch on causality as well. The questions this course will explore include: How is the landscape of retailing changing in the age of artificial intelligence and big data? What is the right price and promotion in the presence of competitors? How should a retailer choose a store location? How does omni-channel retailing influence the way shoppers move through all channels in their search and buying process? This class is practical and hands-on. All strategic decisions in business require a quantitative assessment of cause and effect. Each week we will introduce a new data set and data-driven tool that are valuable in the context of data scientists in retailing. You will learn how to perform convincing data analyses to answer specific questions. We will use R and ArcGIS for analyzing data. We do not assume that you have used R or ArcGIS, software for statistical and geographical analyses, respectively, in a previous class. For potential overlaps with other courses, we will cover them at a faster pace and emphasize techniques that are not covered in other courses.

BU.450.765 Social Media Analytics (2 Credits)

The rapid growth of social media has given mass consumers a powerful tool to create knowledge and propagate opinions. At the same time, social media has created an unprecedented opportunity for companies to engage in real-time interactions with consumers. In addition, the size and richness of social media data have provided companies an unusually deep reservoir of consumer insight to transform their business and marketing operations. The social media analytics course will enable students to grasp the analytics tools to leverage social media data. The course will introduce tools such as engagement analytics, sentiment analysis, topic modeling, social network analysis, identification of influencers, and evaluation of social media strategy. It will involve many hands-on exercises.

BU.610.750 Supply Chain Analytics (2 Credits)

In this course, we show applications of inventory theory to global supply chain management. In addition, we discuss several related issues in supply chain management, including distribution, coordination, global sourcing, and mass customization. We will take an analytical and detailed approach to model development. The presentation is designed to refine intuitions developed from models and case studies to build managerial insights.