Curriculum

UC Davis's Graduate School of Management MSBA curriculum will give you hands-on experience working on and implementing analytic projects that are drawn from real industry data and move through the lifecycle of business problems.

You’ll work with globally ranked faculty and as part of small student teams to build your skills in:

• data modeling
• databases
• regression
• data mining
• machine learning
• operations research methods
• methods for acquiring, storing, handling and representing data
• strategic thinking
• data-driven communication
• project management
• leadership

Students must complete 44 units to meet the graduation requirements, including a practicum project course that spans the duration of the program.

Intersession/summer session prior to fall quarter*

• The outlined schedule and unit requirement is subject to change. Formal instruction occurs on Fridays, Saturdays and most Wednesdays.
• As part of the degree requirements, all students must pass a comprehensive examination administered in the spring quarter. The exam tests students on their coursework, as well as their ability to synthesize learning across key subject areas.

Summer Term

Foundations of Analytics  (4 units)

Focuses on teaching the fundamentals of R and SQL. Introduces the topic of numerical optimization and review the concepts of linear algebra and calculus.

Fall Term

Information, Insight and Impact (3 units)

This course explores the role of analytics in business. It aims to teach the basic principles involved in the applications of analytics to various aspects of business processes. The specific objectives are outlined below:
a. Introduce basic applications of business analytics across multiple sectors
b. Discuss the ethical implications of business analytics and understand its limits
c. Introduce methodologies used in business analytics
d. Understand how to collect, process and quantify data
e. Discuss the role of descriptive, predictive and prescriptive analytics
f. Topics also include modelling uncertainty, dealing with multiple objectives, and consensus building.

Data Management (2 units)

Introduction to the extraction, assembly, storage and organization of data in IT systems.

Data Visualization (2 units)

Extract insights using visualization tools in R, Python, ManyEyes, HTML/CSS, etc. Standard (histograms, boxplots, and dashboards) and specialized (3D, animation, word clouds) formats are covered.

Statistical Exploration and Reasoning (3 units)

Students use statistical reasoning and techniques to draw appropriate inferences regarding the meaning of data. Students learn to obtain preliminary insights and form initial hypotheses through exploratory data analysis (EDA). Topics include descriptive statistics, critical statistical thinking, sampling, probability, and basic statistical methods (e.g. OLS). The course also covers empirical strategies for applied micro-econometric research questions that include econometric applications of regressions and diff-in-diff.

Practicum Initiation (3 units)

Skill development around opportunity assessment, research methods, partner engagement, project management, team performance, needs elicitation, and oral and written communication.   

Winter Term

Organizational Issues in Implementing Analytics (3 units)

The objective of this course is to help students develop skills to analyze and address issues that arise in enabling the use of analytics in organizations. Students will learn strategic and organizational analysis tools, including models and frameworks to diagnose organizational issues and anticipate barriers to smooth implementation. Students will also learn basic theory about why people resist change. Students will become more knowledgeable about where analytics fits into overall organizational objectives and functioning, and about organizational and individual-level factors that can impede or enhance the use of analytics for business performance.

Data Design & Representation (2 units)

Introduction to business applications involving standard, streaming, and network data. Emphasis on scalable technologies for processing and analyzing big data for diverse applications.

Advanced Statistics (3 units)

Continue exploring statistical reasoning using maximum likelihood estimation, Bayesian models, nonparametric models, Monte Carlo Markov Chain, time series, model specification, model selection, and dimension reduction.

Machine Learning (3 units)

Construct algorithms for learning from data and analyze the process for deriving business intelligence. Coverage of supervised and unsupervised learning, neural networks, etc.

Practicum Elaboration (2 units)

Managing an analytics consulting engagement with emphasis on appropriate scope and definitions, data management and data engineering, statistical and technical analysis, and solution engineering. Knowledge development of analytics implementation practices across industries and professional roles.

Spring Term

Big Data Analytics (3 units)

The goal of this class is to equip you with the state-of-the-art big data skills to become an effective data scientist in this evolving data landscape. Topics include Distributed Computing, Streaming, Text/Social Network Analytics, and Deep Learning.

People Analytics (Elective, 3 units)

People Analytics explores data-driven approaches to the management of a firm’s human resources and the consequences of choices managers make for their ability to attract, motivate, and retain top talent. 

Application Domains (Elective, 3 units)

Students explore contemporary and emerging domains for high-yield applications of analytics. Topics: social network analytics, search analytics, health care analytics, internet of things, supply chain/operations analytics, and marketing analytics.

Analytic Decision Making (3 units)

Using spreadsheets and specialized modeling tools, explore structured problem solution through meta-heuristics, Monte Carlo simulation, and mathematical optimization.

Practicum Analysis & Implementation (3 units)

Focus on completing promised project deliverables by polishing statistical and non-statistical quantitative analysis, generating insights for technical and business stakeholders, integrating proposed solutions into partner workflows and organizations, and disseminating the findings and outcomes through presentations and publications. This course culminates in a project presentation to key partner stakeholders.