The one-year MSBA program consists of 13 courses – eight required core courses and five electives – for a minimum of 52 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

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

  • Foundations of Marketing (4 units)
  • Data and Programming for Analytics (4 units)
  • Machine Learning for Analytics (4 units)
  • Elective (4 units)
  • ProSeminar (0 units)

  • Management Science for Analytics (4 units)
  • Customer and Social Analytics (4 units)
  • Capstone Prep (2 units)
  • Elective (8 units)
  • ProSeminar (0 units)

  • 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 Machine Learning 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 Customer 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

  • Natural Language Processing and Applications
  • Big Data & Cloud Computing
  • Marketing Analytics
  • Business Data Management
  • Advanced Machine Learning
  • Mastering Predictive Analytics
  • Supply Chain Analytics
  • Technologies & Analytics Consulting
  • 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:

  • Kaiser Permanente
  • Pacific Life
  • Competitive Analytics
  • Wells Fargo
  • Inscape
  • Experian
  • Los Angeles Rams
  • Cerius Executives
  • Ingram Micro
  • Paciolan
  • Disney
  • Niagra Bottling
  • Amazon
  • Edwards Lifesciences

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

2019 Capstone Projects

  • Kaiser Permanente – Small Business Group Analysis
  • Pacific Life – Predicting policyholder lapse behavior
  • Competitive Analytics – People analytics
  • Wells Fargo – Build customer level models using advanced techniques to predict customer value, outcomes, and potential services
  • Inscape – Predict Viewing Content of top 100 national networks
  • Experian – Utilize credit attributes to predict consumer behavior
  • Los Angeles Rams – Ticket Sales Lead Score Model
  • Cerius Executives – Marketing engagement and conversion tool creation
  • Ingram Micro – Customer intent and prediction mode
  • Paciolan – Predicting the key drivers for live event attendance, lead score by product offering to drive revenue growth
  • Disney – Advanced analytics for Walt Disney Parks & Resorts, U.S
  • Niagara Bottling – Predictive analysis on carrier failure
  • Amazon – Recommending Alexa skill categories based on skill attributes
  • Edwards Lifesciences – Predicting quality and compliance trends for product lines

Student Testimonials

Javier Orraca Javier Orraca
MSBA '19

Rimo Das, MSBA Rimo Das 
MSBA ’18