Earn 48 units through coursework and the hands-on Capstone Analytics Project.


Our curriculum is solidly based on the business school model.

Analytics Project

Tackle a practical, real-world business analytics problem.

The UCLA Anderson MSBA curriculum is solidly based on the business school paradigm of merging theory and principle with up-to-the-minute business practice. While MSBA programs are available at many universities, UCLA Anderson is one of the few top-tier business schools worldwide to offer the MSBA degree. Our dynamic curriculum, taught by world-renowned faculty, merges technical and theory-based pedagogy with practical immersion through a summer internship and a corporate sponsored Capstone Analytics Project.


Course Schedule





November - December
January - March
March - June
September - December

Math and Stats for Analytics

Business Fundamentals for Analytics

Customer Analytics

Supervised Project

R Programming Essentials

Data Management

Competitive Analytics

*Internet Customer Analytics

SQL and Basic Data Management

Prescriptive Models

Operations Analytics

*Entertainment Analytics


Industry Seminar – I

Industry Seminar – II

*Healthcare Analytics




Final project presentations and graduation

Foundation courses are online only. Students arrive on campus in January of the winter quarter. The summer is dedicated to a 4-unit internship supervised by an Anderson faculty member and will ultimately lead to the student’s required project that they will continue to work on in the Fall.
*Elective Examples

Capstone Analytics Project

The Capstone Analytics Project (CAP) gives MSBA candidates the opportunity to apply knowledge acquired through MSBA coursework to solve a practical, real-world business analytics problems. By partnering with a corporate client, students develop and showcase their knowledge of business analytics, hone their communication skills and delve more deeply into an area of interest beyond the classroom.

UCLA Anderson GEMBA Asia Management Practicum

CAP projects concentrate on such areas as programming and data management methods, model development and construction, business analytics and industry applications, and are sponsored by top organizations. Students interact directly with clients, gaining valuable exposure to potential employers and broadening their professional networks.

Course Descriptions


Math and Stats for Analytics

Business decisions are made with partial information in an uncertain environment. The objectives of this course are to introduce (i) data analyses that are appropriate for generating information useful in decision making and (ii) a framework for analyzing decisions based on partial information. In order to achieve these objectives, we shall develop a foundation of probability and statistics; this basic grounding is also necessary for subsequent courses and for a career in business analytics.


R Programming Essentials

From small startups to firms like Google and Amazon, R is the language of choice for data analytics. Most R users only scratch the surface of the capabilities of R and do not develop an understanding for basics of the language and its great asset, extendibility. The goal of this course is to provide students with a good working knowledge of the basics of R required to succeed as data scientists. In addition, we will emphasize how to extend the language by function programming and package development. Scientific document creation and reproducible research has become a recent addition to the R environment and we will provide a working introduction to these important topics.


SQL and Basic Data Management

This 2-unit course will introduce students to relational algebra, SQL, and the basic elements of data management. Students are expected to do their own coding. The course will also teach students how to use regular expressions and will help them become familiar with database terminology (e.g., schema, one-to-one, one-to-many, many-to-many, etc.).



The course covers a wide variety of optimization models that can be used to solve business problems. The course emphasizes mastery of spreadsheet modeling as an integral part of business analytic decision making. The managerial models covered include linear programming, network and distribution models, integer programming, and nonlinear programming. The course is interdisciplinary; problems from operations, finance, and marketing are used to achieve the course objectives. This is a hands-on course. In every class, we will work on problems and develop spreadsheet models to facilitate decision making. Optimization using R and Matlab will also be covered.


Business Fundamentals for Analytics

Business Fundamentals for Analytics is concerned with the application of economic, finance and marketing principles to key management decisions within organizations. It provides the analytical tools for a better understanding of the external business environment in which organizations operate. A primary purpose of the course is to develop tools and background useful for the other courses in the M.S. in Business Analytics program.


Data Management

This course is about understanding data, data structures and the technologies that are essential to building analytic frameworks. In this course students will be exposed to various elements of an effective data management framework. The course will deal with both tactics and strategies related to managing, manipulating, storing and delivering data. There will be a number of exercises using R, Python, regular expressions, SQL, and NoSQL, which will help the student understand how to manipulate and manage data in a real world context. The course will also cover advance frameworks for distributed storage and processing, such as Hadoop and Spark.


Prescriptive Models & Data Analytics

The course will teach fundamental tools in data analytics, including experimental design and analysis, regression analysis and model design, as well how to implement these approaches using the R statistical analysis package. However, this is not a course in statistical theory or econometrics. Rather, the course has a strong practical orientation, equaling emphasizing critical thinking skills, the ability to ask the right kinds of questions for data analysis, and the creative aspects of designing a data analytics approach capable of delivering a convincing analysis that would support decision making.


Industry Seminar

This course brings relevant industry practitioners into the classroom to share their experiences, challenges and strategies relating to business data analytics. The speakers will be from a variety of industries and at various levels of seniority so as to offer the student different perspectives on the issues faced by analysts, managers and decision-makers in today's corporate environment. This course is also intended to help students develop strong interpersonal skills, business acumen, and the ability to translate business objectives to an analytical framework and action items.


Operations Analytics

The internal operations of a firm are responsible for executing the firm's business plan to deliver its value proposition. Achieving operational excellence helps firms improve their return on assets in the short term, but also creates a knowledge base that helps building a competitive advantage in the long run. In today's globalized world, it is becoming impossible to excel operationally without the use of quantitative models and data-driven decision making. The purpose of this course is to learn how business data analytics can be used to optimize internal processes and resources. The course is based on applications and data-driven cases that illustrate quantitative techniques and show how to build an operational competitive edge based on business analytics.


Competitive Analytics

This is a course in applying data analytics to examine competitive conditions in an industry or market. The goal is to learn state-of-the-art practical tools that can be utilized to answer the following kinds of questions: How competitive is a given industry?

What role does product differentiation play in determining pricing and margins?

Which specific products are close substitutes (whether from the same firm or from different firms)?

Which markets / products are long-term profitable for a firm, and which are not?

What products should a firm offer, and how should it price them? What markets should a firm enter, and or exit?

How do we expect competitors to respond if we change prices, eliminate products, or introduce new products?


Customer Analytics

This course is about the accumulation, management and analysis of customer data to make better decisions. It introduces students to key analysis tools of customer level data such as clustering methods for segmentation, choice models using both stated preference data (survey/conjoint data) and behavioral data (scanner panel data, attribution data) and marketing mix models. The class uses real-world cases, exercises and projects to help students aggregate the theories, frameworks and methods they have learnt in earlier classes. In addition, the class aims to add to the students’ skill set by introducing students to sophisticated ideas and approaches to analyzing, interpreting and portraying customer and marketing data.


Internship: Fieldwork/Research in Business Analytics

M.S. in Business Analytics students are required to do an internship with a company in the area of business analytics. The 4-unit summer internship provides students with either research or practical experience applying their business analytics knowledge in a real-world setting, strengthening their competitive position in the marketplace upon graduation.


Internet Customer Analytics

If you are starting a new online business or product line, how ought you to go about acquiring new customers? Once you have a core base of good customers, how do you go about finding more customers like the good customers you have? How do you strengthen the relationships with your good customers, build their loyalty and make them heavier buyers from you? How do you prevent your good customers from leaving you for your competitors?


Healthcare Analytics

With healthcare spending in the United States exceeding 17% of GDP and the demand for health services continuing to increase, improvements in the quality and efficiency of healthcare delivery are needed. This course explores opportunities for improvement in the design and management of healthcare systems and operations, through the use of tools such as regression analysis, linear optimization, queuing theory, decision analysis, Monte Carlo simulation, and machine learning techniques.


Entertainment Analytics

The goal of this class is to introduce students to business analytics in the entertainment industry. The course is divided in two parts. The first part focuses on movies studios, television, and online media. A recent study by PwC revealed that 66% of entertainment and media executives have changed the way they approach decision making as a result of big data and analytics. The study lists the top three changes in the last 2 years: (i) executives have made greater use of specialized analytics tools; (ii) they have employed a dedicated data insights team to inform strategic decisions; and (iii) they have relied on enhanced data analytics such as simulation, optimization, or predictive analytics.