The MS in Business Analytics modules are spread out over a period of 12 months. Between modules, students complete approximately 20 hours of work per week on pre- and post-module tasks.
Module I: New York
Digital Analytics and Strategy: An Introduction
We are in the early stages of an information revolution where information technologies are redefining business models, transforming industries, creating new markets, and generating a whole new “space” where new human communities, behaviors, norms, and regulation are just beginning to emerge. Information technologies are an increasingly becoming an integral part of developing new products and services, of integrating business functions, and of managing customer relationships. IT-driven disruptions in business models are frequent.
This two day course is designed to survey how digitization is revolutionizing strategy. The topics we discuss will cover some of the most fundamental and far reaching strategic shifts affecting the digital economy. The objective is to end up with frameworks that you will find useful in generalizing across contexts in which information technologies and digitization are changing the nature of business and the world. Considerable emphasis is placed on new emerging disruptions including social networks, platform competition, the long tail and the economic impact of search.
Dealing with Big Data
This course is focused on how one deals with data, from its initial acquisition, its storage, to its final analysis. In particular, the course will focus on how to deal with the “big data” that is becoming increasingly prevalent due to the large amount of web, social media, advertising and high-frequency financial data being captured in modern organizations.
The lessons and exercises are designed so that students will be able to use big data tools and approaches with the analytical and visualization tools that they will encounter in later modules. There will be many data sets available for students to use both in this course as well as subsequent ones. These will include public government data sets, several financial data sets, social network data sets and web log data sets.
Data Mining for Business Analytics
This course will change the way you think about data and its role in business. Businesses, governments, and individuals create massive collections of data as a byproduct of their activity. Increasingly, decision-makers and systems rely on intelligent technology to analyze data systematically to improve decision-making. In many cases automating analytical and decision-making processes is necessary because of the volume of data and the speed with which new data are generated.
We will examine how data analysis technologies can be used to improve decision making. We will study the fundamental principles and techniques of data mining, and we will examine real-world examples and cases to place data-mining techniques in context, to develop data-analytic thinking, and to illustrate that proper application is as much an art as it is a science. In addition, we will work “hands-on” with data mining software.
Probabilistic Models for Finance
This course will cover various mathematical models for the financial markets in discrete and continuous time. Models for stocks and their derivatives will be discussed, with the main focus on pricing and hedging such derivatives. We shall pay particular attention to the binomial and the Black-Scholes model. In both cases we shall study the formula for pricing European call options. In the case of the Black-Scholes model we shall also study how the changes of the parameters effect the price of the call option. We shall calculate the price of American options using backwards induction. Finally, forward and futures contracts will be discussed.
In the philosophy of science, prediction is often considered to be an essential test of a theory or a model: how well does it predict future phenomena?
Prediction is becoming increasingly central to business, driven by the explosion in available data: how will a customer respond to an offer? Will a customer leave? What is the probability distribution of what will happen to the market tomorrow? How likely is it that someone will become sick in the near future? What strategy should a sports coach adopt given the game data available at the moment? What products should a business produce given the social media chatter about related product? Will demand for a product go up or down in the next period?
Increasingly, questions such as the above are becoming answerable because of the very large volumes of data that are becoming available and the ability to predict outcomes based on the data. New types of observations are becoming possible that were unthinkable just a few years ago, giving rise to new and powerful predictive analytics driving emerging business models.
This course focuses squarely on prediction and data-driven predictive analytics.
Module 2: New York
"Every two days we now create as much information as we did from the dawn of civilization up until 2003." -Eric Schmidt (CEO Google)
"Data are widely available; what is scarce is the ability to extract wisdom from them." -Hal Varian (UC Berkeley and Chief Economist, Google)
The two quotes above summarize the main theme of this course. In every aspect of our daily lives, from the way we work, shop, communicate, or socialize; we are both consuming and creating vast amounts of information. More often than not, these daily activities create a trail of digitized data that is being stored, mined, and analyzed by firms hoping to create valuable business intelligence. With technological advances and developments in customer databases, firms have access to vast amounts of high-quality data which allows them to understand customer behavior, and customize business tactics to increasingly fine segments or even segments of one. However, much of the promise of such data-driven policies has failed to materialize because managers find it difficult to translate customer data into actionable policies. The general objective of this course is to fill this gap by providing students with tools and techniques that can be utilized for making business decisions. Note that this is not a statistics or mathematics course. The emphasis of the class will be on applications and interpretation of the results for making real life business decisions.
Social and Digital Media Analytics
The emergence of the Internet has drastically changed marketing. Some traditional marketing strategies are now completely outdated, others have been deeply transformed, and new digital marketing strategies are continuously emerging based on the unprecedented access to vast amounts of information about products, firms, and consumer behavior. The Internet is now encroaching core business activities such as new product design, advertising, marketing and sales, creation of word-of-mouth, new start-up funding, and customer service.
Our goal in this class is to discuss the new business models in electronic commerce that have been enabled by Internet-based social media and advertising technologies, and to analyze the impact these technologies and business models have on industries, firms and people. We will inform our discussions with insights from data and metrics that can guide us for measurement. To recognize how businesses can successfully leverage these technologies, we will therefore go beyond the technology itself and investigate some key questions.
Managing for Quality
Participants in this course will be introduced to the major elements of Dr. Deming's theory of management, as well as “Six Sigma” theory, tools, and methods. Dr. Deming’s theory of management, called the System of Profound Knowledge, provides an overarching theory for the practice of management. It promotes “joy in work” as the vehicle to transformation an organization from the currently and commonly practiced “Management by Objectives – Performance Appraisal System” management style to the “Continuous Improvement” management style. “Six Sigma” management operates within the environment established by the System of Profound Knowledge. Its purpose is the relentless and rigorous reduction of variation in all critical processes to achieve continuous and breakthrough improvements that impact the bottom-line and / or top-line of the organization, and increase customer satisfaction. This course is critical to managing big data because big data requires trust between the subjects of big data and the users of big data.
Module 3: Shanghai
Operations and supply management use analytical thinking to deal with real-world problems. – F. Robert Jacobs
This course is an introduction to the principles and techniques for operations analytics. Operations and supply management is defined as the design, operation, and improvement of the systems that create and deliver the firm’s primary products and services.
A critical success factor in gaining competitive advantage is the ability to apply the right analytics at the right time, to the right people, at the right place and under the right situation. --Joseph Chan
In this course, students will learn operations models and techniques that work with large data sources. Operations management has dealt with applying analytics for many years. Recently, however, due to big data, many older models and software are incapable of running the analyses. This course will demonstrate the application of Operations models that are currently being used in industry incorporating big data.
Advanced Decision Models
Analytics is “the scientific process of transforming data into insight for making better decisions."
This course focuses on decision making under uncertainty. Students will learn how to build optimization models that incorporate random parameters (e.g., random demand, price, etc.). The modeling tools covered in this course include: static stochastic optimization, two-stage optimization with recourse, chance-constrained optimization, and sequential decision making. We explore their applications in marketing, finance, inventory management, revenue management, supply chain management, project management, among others. Students will also learn how these models can be solved using Risk Solver Platform for Excel. The emphasis throughout the course will be model formulation, solution methods, and managerial interpretation of the results, rather than on the mathematical algorithms used to solve models.
Visualization is a kind of narrative, providing a clear answer to a question without extraneous details. -Ben Fry
This course is an introduction to the principles and techniques for data visualization. Visualizations are graphical depictions of data that can improve comprehension, communication, and decision making. In this course, students will learn visual representation methods and techniques that increase the understanding of complex data and models. Emphasis is placed on the identification of patterns, trends and differences from datasets across categories, space, and time. This is a hands-on course. Students will use several tools to refine their data and create visualizations. These include: R, Python, ManyEyes, HTML/CSS, D3.js, Google Charts, Adobe Illustrator, and Excel.
Module 4: Shanghai
Revenue management and Pricing (RMP) focuses on how firms should manage their pricing and product availability policies across different selling channels in order to maximize performance and profitability. One of the best-known applications of PRM is yield management whereby airlines, hotels and other companies seek to maximize operating contribution by dynamically managing capacity over time.
Building on a combination of lectures and case studies the course develops a set of methodologies that students can use to identify and develop opportunities for revenue optimization in different business contexts, including the transportation and hospitality industries, retail, media and entertainment, financial services, health care and manufacturing, among others. The course places particular emphasis on discussing quantitative models needed to tackle a number of important business problems including capacity allocation, markdown management, dynamic pricing for e-commerce, customized pricing, and demand forecasts under market uncertainty, to name a few.
Strategy, Change and Analytics
This course focuses on significant strategic decisions, such as the introduction of new products or the acquisition of another firm, and explores how data-driven and analytical approaches can be used to inform these decisions from a senior management perspective. A case-based approach allows us to discuss details of significant strategic decisions. We will cover some core aspects of business strategy, including external analysis, competitor analysis, and opportunity analysis. We will also look more deeply at different aspects of the decision-making process within organizations, both to understand the process and to think about implementation. The goal is to understand the role of analytics and analytical approaches in the broader organization.
Today’s global economy is characterized by increasing competition, eroding brand equity, increasing price pressures and shrinking margins, all coupled with the constant need to grow and satisfy investors. The effective allocation of scarce resources is paramount. This course will address this essential concern, marrying competitive marketing strategy with financial budgeting through data analysis and market modeling. Using research, data, decision models, and a comprehensive strategic framework we will develop the methodologies for focusing scarce company resources. This will result in creating differential advantage while balancing marketing and financial risk.
The course will concentrate on building a foundation of competitive strategy and tactics to develop this differential advantage in the marketplace; it will then shift to execution and how to allocate scarce resources effectively with decision-making rooted in valuable data and powerful analytical decision-models. The result will be a comprehensive tutorial in effective strategic decision-making and planning, with clear analytical takeaways.
Module 5: Closing - New York
The Business Analytics Strategic Capstone presented at the culmination of the program gives students an opportunity to review and interpret data through statistical and operational analysis with the use of predictive models and the application of optimization techniques. The result is a unified and practical case presentation on a topic of the student's choosing.
The integrative projects should not take the form of formal dissertations or narrative papers. Rather, they should take the form of “reports to management,” emphasizing substance over length and the forest over the trees. Where possible, they should be action-oriented and framed in terms of business policy and competitive strategy. Given this format, they should be easily convertible into PowerPoint presentations.