M.S. Business Analytics Curriculum

Graduates of the M.S. in Business Analytics Program will be able to:
  1. Perform sophisticated quantitative analysis,
  2. Translate data analytics into actionable business decision-making,
  3. Demonstrate proficiency with programming and software tools for analytics,
  4. Develop reasonable solutions to ethical issues in data analytics, and
  5. Communicate effectively in oral and written forms.

Course Descriptions

This course equips students with the ability to apply and interpret classical statistical methodologies to analyze a business decision setting. Topics include Descriptive Statistics, Probability and Probability Distributions, Interval Estimation, Hypothesis Testing, Experimental Design, Analysis of Variance (ANOVA), and Linear Regression. R will also be introduced in this course.

In this course students will learn how to construct arguments using quantitative data for a variety of audiences. It provides essential concepts in the formal study of argument theory, including enthymemes and syllogisms, audience analysis, and organization.

This course prepares students to create compelling narratives to effectively transmit the results of their analysis. Students learn various techniques and tools to present analytical results visually, communicate information clearly, and articulate the business insights revealed by analytics effectively. Topics will include data visualization software packages (e.g. Tableau) to present data dynamically, visual querying linked multi-dimensional visualization, dashboards, geographical information system (GIS), animation, personalization, and actionable alerts.

This course prepares students to build well-designed code modules that follow basic programming concepts and logic flow.  It covers the fundamental concepts of computer programming using a standard programming language such as Python. Topics include data structures, control structures, data input/output, object-oriented programming, exception handling, and debugging. Concepts and methods introduced in the course are illustrated with simple data analysis examples.

This course is designed to equip students with advanced techniques for data analysis. It builds on the Data Analysis course. Topics include Regression, Logistic Regression, Non-linear Regression, Time Series Analysis, Nonparametric Methods, Bayesian Probability Updating, and Decision Analysis. Students will continue learning R in this course.

This course equips students with the insight necessary to be an ethical analyst aware of the legal, policy, and ethical implications of data. Central to this is that students develop a perspective on the ethical dilemmas surrounding the data life-cycle including collection, storage, processing, analysis, and use. Topics include: an introduction to the dominant ethical traditions, privacy, data security, data property rights, data accuracy, fraud, negligence, and unanticipated outcomes as well as codes of conduct. It includes constraints and considerations for specific industry and institutional domains, data-types, and collection methods.

This course prepares students to effectively manage data through database theory and tools. Topics include relational database structure, database queries and reports, and database management issues such as concurrency control, data security and integrity. Structured Query Language (SQL) and a structured database software package will be used in the course.

Cloud Computing covers the fundamental topics and concepts of cloud infrastructure in order to solve large data analysis problems.  Topics covered in this course include cloud architectures such as Amazon Web Services, cloud programming, cloud transport using Docker Containers, mobile cloud applications for Internet of Things (IoT), social network analysis using cloud services, cloud performance, and cloud security.

This course prepares students to develop systems to measure, monitor and predict the evolution of key enterprise variables and performance indicators and present them in the form of usable information supporting the business decision-making process. Topics include project management, data warehousing, business reporting and performance management, data mining, text mining, and big data strategies.

This course focuses on the applications of analytics in finance and related fields through econometric analysis and financial modeling. Concepts of financial analytics will be introduced via exercises such as: Creating a portfolio and evaluate its performance; Estimating asset pricing models using econometric techniques; Understanding and applying portfolio optimization methods; and Explore the fundamentals of options markets, and options trading. Topics will include the Capital Asset Pricing Model (CAPM), Markowitz efficient frontier, binomial trees and the Black-Scholes option pricing model.

This course prepares students to address problems and opportunities in supply chain management and business process optimization. Students will learn how to translate business scenarios into mathematical models and how to use linear programming to identify optimal solutions. Topics include Process Analysis, Linear Programming, Integer Linear Programming, Queuing Models, Inventory Models, and Simulation. A programming language (AMPL) will be introduced in this course.

This course prepares students to model and analyze market and customer data.  Topics will include applications of analytics in key marketing areas, including market segmentation, new product and service design, revenue optimization, customer relationship management (CRM), pricing and yield management, and distribution decisions. Essential marketing concepts and theories are introduced as needed, as well as cases and projects optimal solutions. Topics include Process Analysis, Linear Programming, Integer Linear Programming, Queuing Models, Inventory Models, and Simulation. A programming language (AMPL) will be introduced in this course.


This is the capstone course of the program. Armed with knowledge and skills they learn through the program, student teams take on real life analytics projects and will present and defend their findings and recommendations to faculty and analytics experts.