Master's in Business Analytics Program Overview, Curriculum and Courses


Meet the future of data and decision-making.

Discover the next frontier of business analytics. With a curriculum focused on real-world applications, our Master’s in Business Analytics program prepares you to make data-driven decisions that move businesses and entire industries forward. In just 10-16 months, you’ll build analytics expertise and strategic business skills—so you can harness the power of data to change the business landscape as we know it.


dual-degrees options: MBA, MSA, MSF, MS MIS, MSM


months of immersive learning


million in research funding for MIS faculty

What You’ll Learn

  • Develop advanced analytical methods

  • Hone your data management and statistics expertise

  • Use what you learn to solve real-world problems

  • Immerse yourself in the future of data-centric decision making

The Curriculum

Taught by world-class faculty and built on the foundation of our top-five MIS program, the Master’s in Business Analytics program not only helps you build in-demand analytics skills, it helps turn you into an effective decision maker on a global economic scale.

You’ll start by diving deep into three key areas: data management, statistics and analytical methods. Then you’ll have the opportunity to explore electives in accounting, economics, finance, management, management information systems and marketing.

If you choose one of our dual-degree options, you’ll spend your final semesters rounding out your requirements—and anticipating the job offers that make it all worthwhile.

Students have the option of completing the 33-unit program in two or three semesters (10 or 16 months). A summer internship is possible in the three-semester program.

Sample 10-Month Program of Study (May Graduation)

* Depends on background
Summer Fall Spring
MIS 502 Technical Foundations of Analytics (4 units)

ECON 511A Econometrics (3 units)

MKTG 555E Special Topics in Marketing (8 weeks; 2 units)
BNAD 515C Introduction to Business Analytics (2 units) MKTG 525 Regression Modeling for Analytics (7 weeks; 2 units) BNAD 597A Consulting Project (3 units)
  MIS 545 Data Mining for Business Intelligence (3 units) MIS 563 Statistical Machine Learning with Applications (2 units) OR ECON 511 B Econometrics (3 units)
  MIS 561 Data Visualization (3 units) Elective (focus may include econometrics, marketing, finance, forensic accounting or information technology; 3 units)
  MIS 584 Big Data Technologies (3 units) Elective (focus may include econometrics, marketing, finance, forensic accounting or information technology; 3 units)
  *Foundational: MIS 509 Strategic Communications (3 units)  
Total: 6 units Total: 14 units Total: 14 units

Sample 16-Month Program of Study (December Graduation)

*Depends on background
Summer Fall Spring Summer Fall
MIS 502 Technical Foundations of Analytics (4 units)

ECON 511A Econometrics (3 units)

MKTG 555E Special Topics in Marketing (8 weeks; 2 units) Internship (optional) MIS 561 Data Visualization (3 units)
BNAD 515C Introduction to Business Analytics (2 units) MKTG 525 Regression Modeling for Analytics (7 weeks; 2 units) BNAD 597A Consulting Project (3 units)   MIS 584 Big Data Technologies (3 units)
  MIS 545 Data Mining for Business Intelligence (3 units) MIS 563 Statistical Machine Learning with Applications (2 units) OR ECON 511 B Econometrics (3 units)   Elective (focus may include econometrics, marketing, finance, forensic accounting or information technology; 3 units)
  *Foundational: MIS 509 Strategic Communications (3 units) Elective (focus may include econometrics, marketing, finance, forensic accounting or information technology; 3 units)    
Total: 6 units Total: 8 units  Total: 11 units   Total: 9 units


This course explores various business analytics strategies, current trends, and their application through case study and discussion. Upon completion of the course, students will be able to migrate from episodic customer interactions to connected customer relationships, define the relationship between connected strategy and innovation, explore best practices for digital transformation, and strategically use platforms and ecosystems.

2 units

Deliver a business analytics-centric project to an actively engaged client applying the techniques and using the tools learned in the program. Seek to provide insight and analysis beyond the obvious.

3 units

Econometrics is the art and science of the estimating and testing of economic models. These estimated models can then be used for causal inference and prediction. This course gives a rigorous introduction in econometrics. It covers the linear model, potential outcome model, the average treatment effect, multivariate linear model, nonlinear models with and without endogeneity, LASSO estimation, machine learning, prediction, and the bootstrap. Knowledge of statistics at the level of Economics 510 Masters level is assumed as well as knowledge of calculus at the level of Hansen (2018), appendix A. Computer programming experience is helpful but not required. An important objective of the course is for the student to learn how to conduct and how to critique empirical studies in economics and related fields. The course emphasizes understanding and intuition so that you can adjust the tools to new quantitative problems that you may encounter. This distinguishes the course from an undergraduate course or an econometric cookbook course.

3 units

The objective of this course is to introduce the basic ideas of modern statistical learning and predictive modeling, from a statistical, theoretical and computational perspective, together with applications and analysis of economic data for graduate studies in economics and related fields.

3 units

This course provides students a technical foundation for Business Analytics. The concepts taught in this course, i.e., principles of programming in Python, statistical analysis, and database management will be useful for your other courses as well as in your career in analytics.

Module 1: Python programming: Writing good software is not the same thing as following a set of cookbook instructions, and a good program is more than a collection of statements. Learning a computer language is a lot like learning any other language. Students not only need to study the syntax but immerse themselves in the semantics of Python. Students will learn Python by studying examples and developing their own programs.

Module 2: An Overview of Basic Statistics. The goal of this module will be to "review" basic ideas of applied inferential statistics that you will utilize throughout your later statistics courses in the program. We will focus primarily on getting you comfortable with the common notation used in statistics and building a conceptual understanding of the fundamental topics. Whenever possible, we will use computer output to demonstrate a statistical technique. Theoretical concepts will be taught to the extent that they will help us know when (and when not) to use statistical tests and how to interpret their results.

Module 3: Database management. Database management systems are at the heart of today's business information systems. They facilitate the sharing of data across the organization, and allow for effective business operation and strategic decision making by managers. Data management, including developing an effective database design, and knowledge of how to store and retrieve data, constitutes a core activity for any organization. Specifically, we deal with Relational databases and how to manipulate them using Structured Query Language (SQL). The content covered is technical and will require a significant amount of self-paced practice for successful completion. Our platform of choice is Oracle, a widely used commercial database management system (DBMS).

4 units

Techniques in written and oral presentation of business concepts with a focus on client interaction and significant use of data visualization techniques.

3 units

Introduction to machine learning and predictive modeling, classification, clustering and association rule mining.

3 units

The goal of this class is to introduce students to principles and tools of data visualizations, and create visualizations using appropriate tools for two different but related purposes: (1) exploration and (2) presentation. The first part is about trying to understand the data and test hypotheses that drive the data visualization effort, and formulate a story; the second part is to convey that finding to others in a convincing manner.

3 units

This course introduces advanced statistical machine learning methods for business applications. Real-world examples are drawn from marketing, finance, and other areas for illustration.

2 units

Techniques in acquiring data from internal and external sources including social media and text data; Hadoop/Apache distributed processing scalable for business applications; Introduction to network analysis and large scale data analytics.

3 units

This course is designed to introduce students to the advance models of research in marketing decision making. Students are exposed to a variety of econometrics models including linear and nonlinear models; dichotomous, dummies, count models, panel regression, and mixed models. The students become familiarized with the casual inferences and experimental design (AB testing). Students are given opportunities to combine what they have learned in previous statistics courses and the methods in this course to analyze data sets related to marketing research and decision making. The course offers development of skills to understand and choose the appropriate method to analyze data and communicate the findings effectively.

2 units

Course addresses special topics in marketing with current managerial relevance. Such topics could include marketing strategy, marketing decision models, marketing and electronic commerce, etc.
2 units


The following elective possibilities are subject to change:

This course provides an overview of forensic and investigative accounting topics. It concentrates on concepts involved in understanding and differentiating the various types of forensic and investigative accounting methods. Instruction and application of basic forensic and investigative accounting techniques will be a focus of this course.

3 units

*Department Consent Required

Course with a combined introduction to financial and managerial accounting.

3 units

This course introduces basic concepts in probability theory and univariate statistics, which are necessary for courses in microeconomics and econometrics. Probability topics include: probability models, random variables and joint distributions, expectations and large-sample results. Statistical topics include: descriptive statistics, point estimators, confidence intervals, hypothesis tests, maximum likelihood methods, method of moments and Bayesian methods. Calculus will be used extensively in this course.

3 units

This course gives tools that are helpful for empirical analysis. It is focused on applications and it covers the potential outcome model, the average treatment effect, the linear model, the linear model with endogeneity, machine learning, prediction and the bootstrap. Computer programming experience is helpful but not required. Applications include finance models, IO models and labor economic models and real datasets are used to estimate these models. The usefulness of random experiments and instrumental variables for causal inference is emphasized and so is the need for a parsimonious model for prediction.

3 units

Introduction to decision theory and game theory and their application to various economic situations under conditions of complete and incomplete information. Graduate-level requirements include a research paper.

3 units

Structure, conduct, and performance of American industry; governmental institutions and policies affecting business. Graduate-level requirements include an applied research project that examines the impact of public policy on industry performance.

3 units

This course is designed to develop your ability to analyze financial statements for the purposes of investment management and will cover the materials in the CFA Level 1 and Level 2 exam curricula. (Fall - Required for Investment Track, Elective for Corporate Track)

3 units

Portfolio theory with applications to the markets for equities, fixed income securities and options. Risk analysis and investment strategies.

3 units

This course is designed to familiarize students with database and various statistical methods needed to undertake practitioner-type research in finance. Heavy exposure to SAS.

3 units

Introduction to the fundamentals of database analysis, design and implementation.

3 units

The objective of this course is to give students a broad overview of managerial, strategic and technical issues associated with Business Intelligence and Data Warehouse design, implementation, and utilization. Topics covered will include the principles of dimensional data modeling, techniques for extraction of data from source systems, data transformation methods, data staging and quality, data warehouse architecture and infrastructure, and the various methods for information delivery. Critical issues in planning, physical design process, deployment and ongoing maintenance will also be examined. Students will learn how data warehouses are used to help managers successfully gather, analyze, understand and act on information stored in data warehouses. The components and design issues related to data warehouses and business intelligence techniques for extracting meaningful information from data warehouses will be emphasized. The course will use state-of-the-art data warehouse and OLAP software tools to provide hands-on experience in designing and using Data Warehouses and Data Marts.  Students will also learn how to gather strategic decision making requirements from businesses, develop key performance indicators (KPIs) and corporate performance management metrics using the Balanced Scorecard, and design and implement business dashboards.
3 units

This course provides a broad introduction to the concepts, techniques, applications, and tools (mainly Python-based ones) of deep learning (DL). The course will cover a variety of DL methods that have been developed to address various modeling and learning challenges across many applications such as image classification, text data analysis, online user modeling, and recommender systems. Specifically, the course content will include the following modules: deep learning (DL) foundation, multilayer neural networks and linear models, training and optimization, Radial basis function (RBF) networks, convolutional neural networks (CNN), recurrent neural networks (RNN), graph neural networks (GNN) and representation learning, attention mechanism, neural networks for adversarial learning, DL language models, DL for user modeling and recommendations, and reinforcement learning and DL. The goal of this course is to help master-level graduate students understand necessary concepts and knowledge about deep learning and develop critical skills and abilities of applying deep learning for real-world problems.

Prerequisite for this course is MIS545.

3 units

Strategic approaches in customer relationship management to include customer identification, acquisition, development, attrition and retention.  Analytical tools are used to explore customer databases, lifetime value of customers, and return on marketing investment.

3 units

This course will provide you with an overview of the issues involved in acquiring, analyzing and interpreting marketing research data.

3 units

Decision Support Systems can be defined as 'computer based systems that use data and quantitative models to solve problems and to help managers make decisions.' This is a course on making quantitative decisions. The course introduces the student to optimization methods (linear, integer, nonlinear programming and network models) used in business, decision support via Monte Carlo simulation, and decision making under uncertainty/risk. These concepts are studied in the context of applications in strategic planning, operations/supply chain management, information systems, and other areas of business. Spreadsheets are intuitive and user-friendly platforms for organizing information. Hence, spreadsheets have become indispensable tools of modern business analysis. This course focuses on structuring, analyzing, and solving managerial decision problems using Excel spreadsheets. Specifically, we address problems from operations management (e.g., resource allocation, revenue management, transportation and logistics, outsourcing production) and information systems (e.g., advertising response, media selection model), along with and several other business problems from finance and marketing. In each area, we consider specific managerial decision problems, model them on Excel spreadsheets, analyze and solve the models, and then interpret the solutions obtained. As an added benefit of this course, we will learn to use advanced features of Excel. This includes some of the built-in functions, named ranges, pivot tables, charts, conditional formatting, and some simple macros.
3 units

Dual Degrees

Earned in just another semester or two, our dual degrees take you from impressive to unstoppable. Dive into the rapidly growing tech industry with a degree in Master’s in MIS. Or broaden your business perspective with a degree in business, finance, marketing, accounting or economics.

  • MSBA/Master of Business Administration (MBA)
    Earn an MBA as well as a Master of Science in Business Analytics—you’ll need to apply to both programs separately and complete a total of 73 units, taking about six semesters.

  • MSBA/Master of Science in Accounting (MSA)
    Students can pursue a career at the intersection of accounting and business analytics, using two skill sets that are increasingly in demand together, by pursuing a dual Master of Science in Accounting and in Business Analytics. This can usually be completed in three semesters, or four if prerequisites are needed.

  • MSBA/Master of Science in Finance (MSF)
    Go far in a career in finance and analytics, with mastery in all the critical areas of finance, data management, statistics and analytic methods. You’ll apply separately to each degree program and can complete the two master’s degrees together in 21 months.

  • MSBA/Master of Science in Management Information Systems (MS MIS)

  • MSBA/Master of Science in Marketing (MSM)
    Earn a dual master in Business Analytics as well as Marketing and climb to the forefront of marketing and analytics. You’ll apply separately to each degree program and complete 51 total units, which can be done in about four semesters.

Your Future is Calling

You’ve heard the “why.” If you think you’re a good fit, it’s time for the “how.” Let’s get you on the road to your Eller degree. Contact us or begin your application now:

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