Master of Science in Business Analytics

The MS in Business Analytics requires successful completion of 30 credits of study, which includes 10 courses at 3 credits each. Applicants with a bachelor’s degree in a field other than mathematics, business or information systems must take two pre-requisite or bridge courses.

Our graduates can:

• Demonstrate comprehensive skills in research methodology, specifically quantitative analysis.
• Identify and apply the fundamental concepts of statistics, data analysis, quantitative modeling, simulation, and optimization to solve unique problems.
• Analyze data comprehensively and apply it to managerial decision-making.
• Learn data handling and database management in practical applications.
• Apply big data modeling using current business software, tools, and techniques.
• Gain skills with modern data mining using simulations and reporting.
• Apply and understand the need for legal governance and ethics as it applies to business decision-making.
• Produce quality products to influence and inform business practices, including reports, demonstrations, and presentations.

• Applied Regression with Research Methods

  BA500 3 credit hours

  This course is designed to be a practical introduction to regression application and analysis, one of the most powerful and useful tools in the field of statistics. Although regression is commonly used among statisticians, social scientists, and data scientists, it is also extremely useful for a variety of other business settings. Understanding regression will teach students how to think carefully about causality and how to use data to make better decisions. Exploring proper research methods, such as quantitative, analytical and statistical skills, students will learn to use these research strategies in a wider application of regression. Topics include simple linear regression, multiple regression, decision analysis and linear programming.

• Problem Solving in Applied Data Science

  BA501 3 credit hours

  This course introduces several important modeling approaches for decision-making. The course focuses on deterministic optimization problems including linear, integer and possibly dynamic programming. Additionally, the course addresses decision-making under uncertainty. The intent throughout is on understanding the problem, formulating a suitable model, finding a solution, interpreting it and performing sensitivity analysis. The course seeks to provide an intuition for how different techniques work, along with experience in applying them to real problems and in presenting results and recommendations in a clear and persuasive manner to specialists and non-specialists alike.

• Predictive Analytics, Relational Database and Data Transformation

  BA510 3 credit hours

  The use of predictive analytics includes a variety of statistical and machine learning techniques and applications in a business environment. The primary goal of predictive analytics is to discover and apply relationships found in datasets in order to make predictions about the future or otherwise unknown events. In this hands-on course, students will be introduced to concepts related to constructing, testing, and applying quantitative models in various business settings. From this perspective, students will utilize major software tools in order to conduct an analysis of continuous, classification and clustering models. Upon completion of this course, students will gain insight into how models are constructed and how predictive models can improve business. Pre-req: successful completion of BA 500 and BA 501.

• Prescriptive Analytics, Non-Relational Database and Location Based Data

  BA511 3 credit hours

  The use of prescriptive analytics includes multiple techniques and applications that recommend which course of action a decision maker should take within a business environment. The primary goal of prescriptive analytics is to use these techniques in order to determine optimal strategies that can improve the results related to business decisions. In this hands-on course, students will be introduced to concepts related to developing various linear and non-linear models using major software tools that are commonly used by business professionals. From this perspective, students will conduct an analysis of the assignment, transportation, and network models as well as investigate business scenarios that require additional theories such as integer and goal programming. In addition, students will also develop simulation models and utilize decision analysis techniques and strategies in order to explore situations when future events are uncertain. Upon completion of this course, students will gain insight into how various models are constructed, and also gain insight into how prescriptive models can improve business decision making. Pre-req: successful completion of BA 500 and BA 501.

• Data Mining for Predictive Decision Making

  BA512 3 credit hours

  This course introduces data mining or knowledge discovery to provide business intelligence by analyzing massive amounts of data to find interesting patterns that can be used to assist decision making or provide predictions. Topics include decision trees, Bayesian classification, clustering, sequence clustering, association rules, time series analysis, neural networks, and others.  Students are expected to analyze real-world data in business using data mining software. Pre-req: successful completion of BA 500 and BA 501.

• Big Data, Business Process and Enterprise Analytics

  BA513 3 credit hours

  Big data analytics is the application of analytic techniques to very large, diverse data sets that often include varied data types and streaming data. Students will learn how to define big data and big data analytic techniques and review big data use cases. Big data analytics explores business and customer interactions from data that seldom finds its way into a data warehouse or standard report.  This data is often unstructured data coming from sensors, devices, third parties, internet applications, and social media; much of it sourced in real time on a large scale. Using advanced analytics techniques such as predictive analytics, data mining, statistics, and natural language processing, businesses can study big data to understand the current state of the business and can track evolving aspects such as customer behavior. Pre-req: successful completion of BA 500 and BA 501.

• Information Visualization and Communication

  BA514 3 credit hours

  This course teaches the skills necessary to communicate information about data clearly and effectively through written, oral and visual means. Students will learn and practice with advanced graphical tools to effectively communicate. The course will build from the understanding of data to the presentation of the analysis. Data storytelling will provide tools to effectively communicate ideas, summarize, influence, explain, persuade and provide evidence to an audience. Visualization can convey patterns, meaning and results extracted from multivariate, geospatial, textual, temporal, hierarchical and network data. During the course students will deliver presentations using these techniques and will also learn to critically evaluate other presentations. Pre-req: successful completion of BA 500 and BA 501.

• Governance, Security, Risks and Ethics in Business Analytics

  BA515 3 credit hours

  This course will examine the role and impact of data analytics and the use of intelligence in business and society, with emphasis on legal, professional and public policy issues. The course will include an examination of data analytics to facilitate learning about legal standards for private organizations using data analytics techniques in various strategic business contexts. Students will consider legal issues across the stages of the data life-cycle from collection to analysis to decision-making to review. This is a dynamic course due to the constantly evolving national and global regulatory landscape which presents a rich basis for student contemplation and discussion. Specific topics in the course curriculum include information privacy law such as U.S. tort law, federal statutory and administrative law and constitutional protection of civil liberties; European Union data privacy regulation; cyber-intelligence and cyber-security regulation; intellectual property rights with respect to ownership of input and output data; contractual liability, specifically with respect to third-party reliance on data analysis; competition law and competitive intelligence. Pre-req: successful completion of BA 500 and BA 501.