MS Business Analytics Curriculum

From data visualization, data mining to econometrics, our curriculum embraces the latest approaches and developments in the data analytics field. We also offer a number of cutting-edge elective classes that allow students to explore concepts in more detail or develop category-specific skills and knowledge.

Core Courses

Take all of the following:

Business Analytics Core Courses
Course Number & DescriptionCourse TitleUnits
GSB 510

This course prepares future business analytics managers to understand critical concepts and principals of data visualization and effective storytelling to communicate technical results at various levels and with different audiences. Students will learn (a) data visualizations tools for different types of data in the context of business analytics and (b) communication of results for business actionable insights through storytelling. Software use includes Excel, Tableau and R.

Data Visualization and Communication in Business4
GSE 518

Students will establish a probability and statistics background needed for the graduate programs in Business Analytics and Economics, including the principles of statistical reasoning, methods used in statistical inference and probability theory. Become familiar with the programming language R with a special focus on performing basic data manipulation and statistical analysis with actual data sets. Software use includes STATA and R.

Essential Statistics for Econometrics4
GSE 520

This course provides the econometric foundation to model and evaluate economic behavior through the construction, estimation, and testing of a classical linear regression model. Estimation methods include ordinary least squares, maximum likelihood, and instrumental variable. Consequences of a misspecified regression model are studied and the appropriate remedial measures are suggested. Further topics include functional forms, dummy variables, tests of structural break, and binary choice models. The emphasis is on the use and application of various statistical methods rather than on their thorough theoretical investigation. Software use includes STATA and R.

Advanced Econometrics I4
GSE 524

This course provides an introduction to computer programming using R. Students will be able to apply general rules and best practices for programming to other programming languages. The first part of the course will cover general programming concepts including data structures, flow control, functions, recursion, and debugging. The second part of the course will apply this general programming knowledge to a variety of problems that economists face. Some of the problems include data collection and management, where we will cover string manipulation and regular expressions, web scraping, and database interfacing using SQL. Other problems include specific economic and statistical methods that rely on computers for implementation. These include optimization and maximum likelihood estimation, Monte Carlo simulations and bootstrapping, and dynamic programming.

Computing and Machine Learning for Economics4
GSB 520

Explore the various facets of how data are organized, delving into relational database management systems, data warehouses and data marts, and distributed data environments such as NOSQL databases. You will survey the means of creating these data sources through data modeling techniques and review retrieving data working with standard data management languages such as SQL. In doing so, you will address other issues such as data quality, data integration, and data management. Software use includes MS Access, Oracle XE 11g, Oracle SQL, Microsoft SQL Server, Hortonworks, and MongoDB.

Data Management for Business Analytics4
GSB 530

Explore the major topics in business analytics with an emphasis on the concepts, tools and techniques related to data mining. Through a business case study and hands-on problem-solving approach, students will demonstrate the issues that are key to organizations’ data mining efforts. Among other topics, students will delve into predictive analytics, pattern discovery, forecasting, text mining and data visualization. Software use includes SAS Enterprise Miner, Enterprise Guide, SAS Text Miner, XLMiner.

Data Analytics and Mining for Business4
GSB 503

In this course students will engage in an interdisciplinary project activity, leading to two or more completed projects. Students will review and analyze real world problems and data provided by the Business Analytics Advisory Board and other industry partners. All projects are team based, where students synthesize ideas and techniques learned in the program. Students will also gain valuable experience working effectively in a team and for a client. A faculty team drawn equally from the technical and management disciplines will provide guidance. In addition, there will be regular workshops and seminars led by the faculty team as well as industry partners. The expected output from the final project is a professional level written report and presentation reviewed by industry partners, key program faculty, and the student’s academic advisor.

Collaborative Industry Projects8
Core Subtotal


Approved Electives

Select 13 units from the following:

Business Analytics Approved Electives
Course Number & DescriptionCourse TitleUnits
GSE 522 (prereq: GSE 520)

An introduction to standard methods of time series econometrics, with special emphasis on financial applications. The course deals primarily with modeling and forecasting with time series data. Topics include ARIMA (autoregressive integrated moving average),VAR (vector autoregression) , and GARCH (generalized autoregressive conditional heteroskedasticity) models. Other topics include instrumental variables, generalized method of moments, and maximum likelihood. Software use includes R and others as needed.

Advanced Econometrics II4
GSB 516 (prereq: GSE 518)

Students will learn to analyze customer information using a broad range of visualization and analytic techniques, applying predictive analytic findings to marketing decision- making. It also features integration of data into reporting platforms that emphasize return on marketing investment. This data-driven marketing analytics approach to marketing intelligence transforms the way professionals responds to data sources, data analysis, relationship management and direct marketing as well as reporting and decision making. Software use includes Excel, STATA, Tableau, and Microsoft SQL Server.

Strategic Marketing Analytics4
GSB 573 (prereq: GSE 518)

This course trains students to gather, analyze, and report new information critical for marketing decision making, with a focus on primary data collection and analytical techniques. Topics include survey design, predictive modeling, hypothesis testing, experimental design or A/B testing, and the role of qualitative research and content analysis, especially in social media marketing. Each topic will be addressed theoretically and then applied in projects on real world business applications. Software use includes SAS and others as needed.

Marketing Research4
GSE 544 (prereq: GSE 520)

In this course students integrate data analysis with the study of decision-making under uncertainty. Students learn how to formulate probabilistic forecasts, represent complex decisions as Bayesian decision problems, identify the optimal decisions, and effectively communicate the results of a decision analysis. The main tools covered are risk analysis using Monte Carlo simulations, stochastic programming, and robust optimization with recourse. Possible applications will be in the areas of finance, marketing, operations, and economic policy. The emphasis throughout the course will be model formulation, bridging the gap between data analysis and decision making, and understanding the limitations and the ethical implications of a process of decision analysis. Software use includes R and others as needed.

Evidence-Based Decision Analysis4
GSB 550 (prereq: GSE 520)

Students learn an introduction to Bayesian econometrics, especially as applied to business decision making. Topics include: making appropriate use of prior information; computation of posterior densities using analytic methods, Markov chain Monte Carlo, and sequential Monte Carlo; Bayesian forecasting and policy evaluation; using marginal likelihood and other diagnostic tools for model selection and performance assessment; and making use of alternative loss functions tailored to specific business applications. The course will include projects designed to illustrate the relevance of these methods in substantive practical applications. Software use includes R and others as needed.

Bayesian Econometrics4
GSB 501 (Approval from Associate Dean)

Advanced individual research in accounting topics planned and completed under the direction of a member of the college faculty. Designed to meet the needs of qualified students who wish to pursue investigations in accounting which cannot be followed effectively in regularly offered elective courses.

Individual Research1-4
GSB 570

Directed group study of selected topics for graduate students.

Selected Advanced Topics1-4
GSB 575 (Approval from Associate Dean)

Career development and preparation with specific focus on the impact of organizational structures on the professions of business analytics and data science. Personal marketing in a dynamic technological environment.

Career Readiness in Data Analytics1
Electives Subtotal


Approved Electives (pending course approval)

Business Analytics Approved Electives (pending course approval)
Course Number & DescriptionCourse TitleUnits

Data-driven approaches to human resource practices in organizations, with emphasis on (1) effective question formulation and linkages between HR data and business outcomes, and (2) considerations about the collection and use of human data for the purpose of making decisions about humans.

People Analytics2

Understanding ethical considerations in data collection, the nature and value of privacy, encryption, algorithmic bias (predictive policing, job ads, search results), and corporate activism to influence or overrule the patterns found in data.

Data Ethics2

Details forthcoming

Advanced Machine Learning
(with Python)

Details forthcoming

Deep Learning2

Introduction to cloud computing fundamentals, virtualization, IaaS, PaaS, SaaS comparisons, data migration to/from cloud. Machine learning with distributed systems

Cloud Computing2