No changes have been made to deadlines, decision dates, program start dates, policies or requirements due to COVID-19 at this time. We are updating our applicants via email, our website and are working with them on an individual basis to address any concerns.
All MSBA classes are held on campus at UCLA (Fall #1, Winter, Spring, and Fall #2). The summer is dedicated to a 4-unit internship. During the second quarter, students may continue their internship or begin a new one on a part-time basis, or participate in a capstone consulting project supervised by an Anderson faculty member.
UCLA Anderson MSBA Class of 2023 will begin fall courses on September 23, 2022.
AAP 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.
Machine Learning for Decision Making
Machine Learning is a key component of business analytics. This course will offer a hands-on introduction to some important concepts of machine learning and their usage. The course will cover various theoretical aspects and algorithms, including an introduction to deep learning. The main effort will be to implement machine-learning tools in Python.
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).
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 MSBA program.
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.
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.
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?
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?
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.
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.