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The one-year MSBA program consists of 13 courses – eight required core courses and five electives – for a minimum of 50 units.

The degree incorporates flexible curricular tracks aligned with your career path in three business analytics contexts:

  • Data Analytics – actuaries, database administrators, financial analysts, information security analysts, management analysts, survey researchers
  • Marketing Analytics – market research analysts, marketing specialists, marketing managers, sales managers, survey researchers
  • Operations Analytics – analytics specialist, financial analysts, management analysts, operations research analysts, statisticians, transportation, storage and distribution managers

Within these flexible tracks, you may select electives across subject areas depending on your interests and career aspirations. The program is STEM-certified (36 months of optional practical training). 

A key component of the program is the capstone project where you work on real-world, data-analytics projects from area companies, under the supervision of faculty and industry professionals.

Program Schedule

Summer
  • Foundations of Business Analytics (2 units)
  • Statistics for Data Science (4 units)
  • ProSeminar (0)

Fall
  • Foundations of Marketing (4 units)
  • Data and Programming for Analytics (4 units)
  • Business Intelligence for Analytics (4 units)
  • Elective (4 units)
  • ProSeminar (0 units)

Winter
  • Management Science for Analytics (4 units)
  • Web and Social Analytics (4 units)
  • Capstone Prep (0 units)
  • Elective (8 units)
  • ProSeminar (0 units)

Spring
  • Capstone Project (4 units)
  • Electives (8 units)
  • ProSeminar (0 units)

Required Courses

BANA 200 Foundations of Business Analytics
This course will provide an overview of business analytics and the theory and practice underpinnings of the MSBA curricular tracks in data analytics, marketing analytics, and operations analytics. The course will also provide the students a working knowledge of the R programming language, including a coverage of how how R can be used for data visualization and graphics, data management, and basic statistics.

BANA 201A Statistics for Data Science
Methods of statistical inference, emphasizing applications to administrative and management decision problems. Topics: classical estimation and hypothesis testing, regression, correlation, analysis of variance, decision analysis, and forecasting.

BANA 201B Management Science for Analytics
An introduction to computer-based models for decision-making.  Topics include optimization (linear programming, integer programming, network flows, quadratic programming, goal programming), Monte Carlo simulation, decision analysis, and heuristics for non-convex optimization.  This course makes extensive use of optimization software, including Excel Solver and AMPL.

BANA 205 Foundations of Marketing
Introduction to the field of marketing. Objectives include developing familiarity with fundamental concepts, theories, and techniques in marketing, and acquainting students with the type of decisions made by marketing managers including customer targeting, product, pricing, distribution, promotion, and research.

BANA 211 ProSeminar
The ProSeminar courses are designed to hone career building skills, provide technology workshops and industry panel discussions, and generally prepare the students for a successful career in business analytics. During the Fall and Winter quarters, we will have guest speakers and panelists from various companies involved with business analytics, and in the Spring we focus on preparing for the CAP and aCAP certifications.

BANA 212 Data and Programming for Analytics
The objective of this course is to challenge and teach students how to handle data that come in a variety of forms and sizes. This course guides students through the whole data management process, from initial data acquisition to final data analysis. Topics include: Python programming, data visualization, web crawling, data manipulation and querying, and natural language processing.

BANA 273 Business Intelligence for Analytics
Introduces methods to mine data repositories for business intelligence to facilitate analytical decision-making. Topics include clustering for market segmentation, association rules to discover relationships between different purchase decisions, Naive-Bayes classification techniques for decision making using decision-trees.

BANA 277 Web and Social Analytics
Examines how to create value through web, mobile and social media analytics. Topics include: digital and social strategies; web analytics; search analytics; display and mobile advertising; social networks and social influence; social media and sentiment analysis; social media monetization; and crowdsourcing.

BANA 298A Business Analytics Capstone Prep
During the Winter quarter teams of up to four students will select the topic for their capstone project, conduct background research, and familiarize themselves with relevant data sets and software.

BANA 298B Business Analytics Capstone Project
During the Spring quarter students will do the bulk of their analysis on the capstone project, with two key deliverables due at the end of the quarter: (i) a presentation to MSBA faculty and students, and (ii) a final project report. Prerequisite: BANA 298A.

Sample Electives

  • Big Data and Cloud Computing
  • Operations Analytics
  • Marketing on the Internet
  • Marketing Analytics
  • Advanced Data Analytics
  • Supply Chain Analytics
  • Predictive Analytics
  • Special Topics in Analytics

Capstone Project

The Capstone Sponsor provides students an opportunity to work on real-life data science problems with industry partners. During the winter and spring quarters, students will work in teams led by a faculty advisor and the sponsor organization to create valuable collateral that will ultimately be presented to the Merage School faculty and staff, and the host organization.

Capstone Sponsor Companies:

  • City of Los Angeles
  • Coca-Cola
  • Competitive Analytics
  • Decision Ready
  • Eaton Aerospace
  • Experian
  • IBM
  • Kaiser Permanente
  • Pacific Life

2018 Capstone Projects

Experian – Utilize credit attributes to predict customer performance
Kaiser Permanente – Retention of customers in small-business groups
Eaton – Aftermarket demand analysis
LA / IBM – Determine true costs of benefits in HR contracts        
Competitive Analytics – Competitive pricing for consumer industries
DecisionReady – Predictive modeling and automation of claims processing
Coca Cola – Demand forecasting model for restaurant traffic and sales
Pacific Life – Statistical analysis of life expectancy

Student Testimonials

Javier Orraca Javier Orraca
MSBA '19

Rimo Das, MSBA Rimo Das 
MSBA ’18