NYU Stern
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Course Index

The MS in Business Analytics modules are spread out over two fiscal years and a period of 12 months. Between modules, students complete approximately 20-25 hours of work per week on pre- and post-module tasks.

Module I: NYU Stern - New York

Social Media and Digital Marketing 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 Some traditional marketing strategies are now completely outdated, others have been deeply transformed, and new digital marketing strategies are continuously emerging based on the unprecedented access to vast amounts of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.

Foundations of Statistics Using R

The purpose of this course is to ensure that students are prepared to use R as a statistical tool and understand the fundamental statistical concepts. This course is divided into two parts: 1) Getting Started with R and 2) Statistics and R.

Part 1: Getting started with R : The R portion of the course will prepare students with the skills needed to work with data using the R statistical computing application. This begins with developing a basic understanding of the R working environment. Second, students will be introduced the necessary arithmetic and logical operators, salient functions for manipulating data, and getting help using R. Next, students will be introduced to the common data structures, variables, and data types used in R. Students will learn how to develop their own R scripts and utilize the various packages available in R for visualization, manipulation, and statistical analysis. Students will learn how to import data sets and transform and manipulate those datasets for various analytical purposes such as dealing with missing data. Finally, students will learn how to create control structures, such as loops and conditional statements to traverse, sort, merge, and evaluate data.

Part 2: Statistics and R: In the second part of the class basic concepts of probability and statistics will be introduced. We shall study the concepts of population and sample, discuss the difference between population parameters and sample statistics, and how to draw inference from known sample statistics to usually unknown population parameters. We shall study discrete distributions, their means and standard deviations, paying particular attention to the binomial distribution. We shall also study continuous distributions and their probability density functions, paying special attention to the most central of the continuous distributions, the normal distribution. The Central Limit Theorem will be introduced, and confidence intervals and statistical tests will be discussed. We shall then study the simple and multiple linear regression, and their applications to prediction and forecast.

Practical Data Science
Data is the new oil. Data is a new class of economic asset. Those were the conclusions of the reports issued by the World Economic Forum at Davos in January 2011 and January 2012. Research published in 2011 by MIT economists shows that companies adopting “data-driven decision-making” achieved significant productivity gains over other firms. In industry, the hottest job these days is the Data Scientist. Data scientists combine technical and statistical skills, analytical thinking, and business acumen. One of the complaints about the data scientists trained in computer science departments is that they're “just technical”, understanding algorithms well, but lacking important skills in problem formulation, evaluation, and analysis generally. On the other hand, those trained in math and statistics departments, in addition to those trained in business schools tend to have underdeveloped technical skills. This course will cover all of these aspects of being a data scientist.

This class is an introduction to the practice of data science. The student will leave the class with a broad set of practical data analytic skills based on building real analytic applications on real data. These skills include accessing and transferring data, applying various analytical frameworks, applying methods from machine learning and data mining, conducting large-scale rigorous evaluations with business goals in mind, and the understanding, visualization, and presentation of results. Students will get their hands dirty, programming real applications using a general programming language, interact with databases, perform efficient data analysis, discuss the processing of “big data”, and construct predictive models.


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.

Data Science 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.

Data Driven Decision Making

"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.

Please note, all courses and topics are subject to change.

Module 2: Rotterdam

Network Analytics

Social media and mobile commerce create massive connected data sets that contain a wealth of business and social insights. This course will translate cutting-edge network science research into actionable analytics strategies for dealing with big data that is networked, text-intensive and unstructured, with applications from viral marketing, A/B testing and media planning.

Decision Models

This course trains students to turn real-world problems into mathematical and spreadsheet models and to use such models to make better managerial decisions. This is a hands-on course that focuses on modeling business problems, turning them into Excel spreadsheet models and using tools like Solver and Crystal Ball to obtain solutions to these managerial problems. The course focuses on two classes of models: optimization and simulation. The application areas are diverse and they originate from problems in finance, marketing and operations. We cover problems such as how to optimize a supply chain, how to price products when faced with demand uncertainty and how to price exotic financial options using Monte Carlo simulation.

Data Mining in R

The goal of this course is to provide hands-on experience on key data mining technologies using one particular tool – the R environment. R is a fast growing technology that has been witnessing widespread acceptance both in academia and industry. Recent surveys have even put it in the top regarding usage by professional data miners (Rexer Analytics survey, 2013). There are many factors contributing for this acceptance but clearly these include the price (free), being open source (trustworthy software that can be easily inspected/checked for flaws), the extension of available methods (exponential growth of the set of available methods for different application areas), and the available support from the community (an extremely large community of knowledgeable experts proving top-notch support for free). This course illustrates the use of R for several key data mining processes. This illustration will be driven by concrete case studies that we will “solve” using R. The course can be regarded as a hands-on complement of the Data Science for Business Analytics.

Please note, all courses and topics are subject to change.

Module 3: Shanghai

Operations Analytics

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.

Data Visualization

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.

Please note, all courses and topics are subject to change.

Module 4: NYU Stern - New York

Special Topics in Analytics: Revenue Management & Pricing

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.

Please note, all courses and topics are subject to change.

Module 5: Closing - NYU Stern - New York

Strategic Capstone

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 group's choosing.  This is a team based project with approximately 4-5 students per group.


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.