MS in Data Analytics & Business Computing Curriculum

The Master of Science in Data Analytics & Business Computing consists of a 12-month full-time course of study, taught entirely in English, that commences with a Summer term at NYU Stern in New York City followed by Fall and Spring terms at NYU Shanghai.

The three-semester curriculum is 36 credits, including a capstone project that culminates the program and connects students with real-world practice. During the capstone, students work in small teams to apply the analytical techniques they’ve learned in class to solve a case situation presented by a corporate client.

In the classroom, leading faculty from both NYU Shanghai and NYU Stern help students to delve into complex material and attain mastery of principal concepts and methodologies. Integrated throughout are topically relevant discussions, exercises and simulations that serve to further illuminate course content.

The following is a representative sample curriculum. In a given year, individual courses could vary.

SUMMER  15 credits

Business Communication (1.5 credits)

Persuasive communication is a vital component to many aspects of business life. This course introduces the basics of communication strategy and persuasion: audience analysis, communicator credibility, and message construction and delivery. Written and oral presentation assignments derive from cases that focus on communication strategy. Students receive feedback to improve presentation effectiveness. Additional coaching is available for students who want to work on professional written communication.

Introduction to Python Programming (3 credits)

This course provides an introduction to programming languages and to the software design methods. The programming language of choice is Python. However, the course will introduce the students to the fundamental programming concepts appearing in various other programming languages, including Java and C, that go well beyond the specifics of Python. Upon completion of this course, the students will be able to acquire practical programming skills in Python and understand the principles of structured software development. They will also understand the principles of designing large software systems and what it takes to plan, analyze, design, implement and support large Information Systems throughout their entire System Development Lifecycle.

Statistics and Data Analysis (3 credits)

This course is designed to achieve an understanding of fundamental notions of data presentation and data analysis and to use statistical thinking in the context of business problems. The course deals with modern methods of data exploration (designed to reveal unusual or problematic aspects of databases), the uses and abuses of the basic techniques of inference, and the use of regression as a tool for management and for financial analysis.

Stochastic Modeling and Simulation (3 credits)

Stochastic modeling and simulation plays an important role in making decisions related to the overall health of the firm. In this course we will develop both spreadsheet models and discrete event simulations to help firms predict the range of likely future scenarios that they may face and also to analyze the effects of decisions that they make. Data analysis on simulation outputs will also be taught. Applications covered in the course include the analysis of several investment opportunities, project management, inventory theory and the analysis of queueing models.

Decision Models and Analytics (3 credits)

This course trains students to turn real-world problems into mathematical optimization models and to use such models to make better managerial decisions. Students will be introduced to the theory, algorithms, and applications of optimization. It covers both deterministic and stochastic optimization models. Students will develop and solve various models with optimization software. The application areas are diverse and they originate from problems in finance, marketing and operations.

Topics covered:

Linear and linear integer programming
Network Flow
Nonlinear programming
Duality and sensitivity analysis
Stochastic programming
Robust optimization
Dynamic programming

Operations Management (1.5 credits)

This course serves as an introduction to operations, viewed from the perspective of the general manager, rather than from that of the operations specialist. The coverage is very selective; the course concentrates on a small number of themes from the areas of operations management and information technology that have emerged as the central building blocks of world-class operations. It also presents a sample of key tools and techniques that have proven extremely useful. The topics covered are equally relevant to the manufacturing and service sectors.

FALL   12 credits 

Data-Driven Decision Making (3 credits)

The specific objectives of this course are to: 1. Help you understand how analytical techniques and statistical models can help enhance decision making by converting data to information and insights for decision-making; 2. Provide intuition for data driven decision making by using practical examples from a wide spectrum of fields; 3. Provide insight into how to choose and use the most effective statistical tool based on the problem at hand; 4. Provide you with a software tool kit that will enable you to apply statistical models to real decision problems; 5. Most importantly, remove any fear of data analysis and increase your comfort level with analyzing databases most commonly used in the business world.

Data Science for Business Analytics (3 credits)

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.

Topics covered:

Data mining and data mining processes
Introduction to predictive modeling
Data fitting and over fitting
Model testing
Cross-validation and learning curves
Model performance analytics
Unsupervised learning and clustering
Bayesian reasoning and text classification

Fundamentals of FinTech (3 credits)

This class explores how FinTech changes the practice of risk management in financial firms. Risk management requires understanding, measuring, and managing market risk, credit risk, liquidity risk, and operational risk. The class presents the technology behind enterprise risk systems and shows how to manage risk using quantitative models. We consider how recent FinTech innovations such as Blockchains, mobile technologies, etc., can change the way these risk systems operate, and create a new demand for talents in risk departments. We also study the specific risk management and regulatory challenges faced by FinTech firms. The class has two main objectives. The first objective is to introduce the principles of risk management that anyone working for a financial firm needs to understand. The second objective is to discuss specific opportunities and challenges created by the use of new technologies in finance. Financial technology has gone through three major stages. In 1960s, back office paper based processes migrated to mainframe computers, using standard CUSIP’s and equity clearing houses and depositories. The second stage used PCs, communications networks to address the front office, FIX standards brought online banking, trading and electronic markets. The third, and the subject of our class, is “fin-tech”, where innovative use of technology disrupts existing financial processes and businesses.

Network Analytics (3 credits)

This is a course on how the social, technological, and natural worlds are connected, and how the study of networks sheds light on these connections. The “social network” has captured popular imagination because of the spread of social media, however we have always been creatures of our networks—whether those networks involve family, villages, tribes, or Facebook. The topics we will cover include: social network structure and its effects on business and culture; understanding how the structural properties of networks help us understand social capital, power, ties and closure; the propagation through networks of information, fads and disease; power laws, network effects, and "rich-get-richer" phenomena; using networks for prediction; leveraging information networks for web search; networks and social revolutions, and the melding of economics, machine learning, and technology into new markets, such as "prediction markets" or markets for on-line advertisements.

SPRING 9 credits

Capstone Project (3 credits)

The Capstone Project is a for-credit experiential learning course that integrates and weaves together concepts learned from the other constituent courses that comprise the curriculum and links them to practical applications. In small groups, students will work together to solve cases presented by companies.

Revenue Management and Pricing (3 credits)

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, and 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.

Topics covered:

Demand segmentation
Price differentiation
Constrained pricing
Marginal value of capacity
Network revenue management
Pricing policies in action
Demand forecasting and data analysis

Marketing Analytics (3 credits)

This course aims to help students understand marketing innovations as well as develop and implement marketing innovations in the rapidly changing environment of technology, competition and consumer behavior.  The focus is on how new information technology and data science have driven the innovations in marketing strategies, tactics and methodologies; and how they may change the future of marketing.  The topics covered in the course include (but not limited to): (1) the theory of innovation and common approaches towards innovations in marketing, (2) marketing innovations and trends in consumer behavior, (3) innovations in marketing strategies and tactics with examples from U.S., China and around the world, (4) innovations in marketing research and analytics, (5) special topics on marketing innovations (e.g., big data and marketing innovations, mobile technology and marketing innovations, AI and marketing innovations, IoT and marketing innovations, smart retailing), etc.