Online Master of Science in Business Analytics

The objective of this course is to give you an understanding of the analytical side of the decision making cycle, focusing on programming as the element that “glues” the collection, transformation, visualization, and analysis of data. We will see how to get data from common sources (APIs, web scraping), examine the rudiments of data visualization (charts, maps), and get an intuitive understanding of the types of analytical tools in use today (machine learning, deep learning, generative AI).

The course is very hands on, and you should expect a lot of programming work, all of it fairly intense. You will learn how to get, clean, and process data from a variety of sources, how to effectively visualize data using maps and charts, the basics of machine learning techniques, the fundamental principles behind generative AI, and how to use machine learning and AI to analyze data for business problems. Finally, you will work with your fellow students on a group project that will require you to gather, store, analyze a data set and to present the results of your analysis.

This course introduces some of the main deterministic optimization models and methods used in operations research and business analytics, including linear, nonlinear, integer programming, and network optimization. Students will learn how to analyze real-world problems across sectors such as logistics, transportation, healthcare, finance, and supply chain by formulating them as optimization models.

Unlike a traditional lecture-based course, this class emphasizes a hands-on, data-driven, and applied approach. Students will identify decision variables, objective functions, and constraints; express them mathematically; and solve the resulting models to optimality using optimization software. They will also perform sensitivity analysis and interpret results to generate actionable managerial insights.

Coursework includes both individual assignments and group projects, encouraging collaboration and practical application of concepts.

With every year that passes, businesses are becoming more and more data driven. Recent advances in computing and technology have made an abundance of information readily available to be exploited as part of the decision-making process: the so-called Big Data revolution. These techniques are applied in operations, marketing, finance, and strategic planning, among other functions. The ability to use data effectively to drive rapid, precise, and profitable decisions has been a critical strategic advantage for companies as diverse as Walmart, Google, Capital One, and Disney. In addition, many current and recent startups are based on the application of analytics to large databases. With the increasing availability of broad and deep sources of information – so-called “Big Data” – business analytics are becoming an even more critical capability for enterprises of all types and all sizes.

There are many taxonomies that can be used to divide the broad field of Business Analytics. This class will roughly follow the explain/predict/optimize framework, and be split accordingly: In the first part of the class, we will look at how analytics can be used to explain the world. These methods can be used to answer questions such as “do vaccines work?”, “is temperature more predictable in the summer or winter?”, “which ad results in the greatest sales bump for my product?” In the second part of the class, we will look at how analytics can be used to predict the future. These applications of business analytics are some of the most famous, and are used by Netflix, Spotify, Amazon, and many others to predict what content or product a user might want to see next.

This course covers basic probability theory, including independence and conditioning, discrete and continuous random variables, law of large numbers, central limit theorem, and stochastic simulation. Basic statistics, including point and interval estimation, hypothesis testing, and regression are also explored. There will be examples from business applications such as inventory management, medical treatments, and finance.

Marketing activity is the core of operating a business. Marketing analytics supports marketing activity by providing the tools and frameworks for identifying, delivering, and capturing value from customers. The course is organized around three modules. Each module features a different set of quantitative tools and programs that ensure students are exposed to a range of resources utilized by marketing professionals. Python and Excel will be used in the course. The first module focuses on segmentation-targeting-positioning (STP) analytics. To make effective strategic decisions, managers must identify, and measure customers’ wants, assess the competitive environment, select the most appropriate customer targets and then develop marketing programs that satisfy customers’ needs better than the competition. The second module focuses on customer analytics. To deliver value to customers, managers must understand how to acquire new customers and retain existing ones, and especially the right customers. The third module focuses on 4P (product, place, promotion, price) analytics. To optimize marketing decisions and programs, managers must be able to quantify the impact of the marketing mix on the value captured from customers.

Groups of students will work on real-world projects in analytics, focusing on the following aspects: identifying client analytical requirements; assembling, cleaning, and organizing data; identifying and implementing analytical techniques (statistics, OR, machine learning, artificial intelligence); and delivering results in a client-friendly format. Each project has a well-defined goal, comes with sources of data pre-identified, and has been structured so that it can be completed in one semester.

The program offers a range of electives that you can choose from in conjunction with your advisor. Electives are offered by both Columbia Business School as well as Columbia Engineering and cover topics in the areas of Marketing, Finance, Decision making, Machine Learning, Artificial Intelligence, and topics in computer science related to analytics. Students are required to take a minimum of three electives from CBS.