Master of Science in Business Analytics

Business Analytics refers to the skills, technologies, applications and practices for continuous iterative exploration and investigation of past business performance to gain insight and drive business planning by using data and statistical methods. A variety of industries are in need of professionals who can take on positions of responsibility for collecting, analyzing and interpreting data in order to make sound strategic business decisions.

The MSBA program seeks to fill the talent gap in the area and to prepare graduates for this fast-growing field by developing students' critical skills in data- and model-driven management decision-making in the context of a firm's strategic vision. The program, which begins annually in September, consists of 10 three-credit courses (seven are required and three are electives). The program is designed to be completed either in one year's time (full-time) or over two years (part-time). Students may opt to use their elective courses to earn a specialization that is tailored to their career interests. Available specializations include: Marketing Analytics, Healthcare, and, for those who have an undergraduate degree in Accounting or the equivalent, Accounting.

Requirements

BA 0500Business Analytics 13
BA 0505Python for Business Analytics3
BA 0510Databases for Business Analytics3
BA 0540Business Intelligence3
BA 0545Data Mining3
BA 0590Capstone: Business Analytics Applications3
QA 0500Business Forecasting and Predictive Analytics 13
Electives
Select three elective courses in Business Analytics 29
Total Credits30

Accounting Specialization

To be eligible to pursue this specialization, students must have an undergraduate degree (BS or BA) with a major in accounting or the equivalent. The equivalent of an undergraduate degree in accounting includes the successful completion of: intermediate accounting (six credits), advanced accounting (three credits), cost accounting (three credits), auditing (three credits), and U.S. taxation (three credits). Deficiencies will be handled on a case-by-case basis.

To complete a specialization in Accounting, students take any three graduate Accounting or Taxation courses at the 500 level for their required electives. Students trying to meet educational requirements for CPA certification are encouraged to consult with the Coordinator of Graduate Accounting Programs in selecting their graduate Accounting or Taxation courses.

Healthcare Specialization

To complete a specialization in Healthcare, students will select three of the following courses as their required electives:

NS 0602Healthcare Economics and Marketing3
NS 0605Advanced Healthcare Policy3
NS 0613Finance and Quality Management in Healthcare Organizations3
NS 0614Information Technology for Healthcare Improvement3

Marketing Analytics Specialization

To complete a specialization in Marketing Analytics, students take the following courses as their required electives:

MK 0520Marketing Research3
MK 0580Multivariate Data Analysis for Decision Making3
MK 0585Seminar: Contemporary Topics in Marketing3
or MK 0590 Experimental Research in Marketing

Note: MK 0400 or its equivalent is a required prerequisite for all upper-level marketing courses.

MBA and MS in Business Analytics Overlap

The MBA with a concentration in Information Systems/Business Analytics (MBA-IS/BA) is a generalist degree that covers all relevant topical areas for a business professional, and gives the students the opportunity to concentration on, but not major in, Information Systems. Immediately after completion of the MBA-IS/BA, students sometimes wish to further their graduate study in Business Analytics. Interested students then may apply for admission to the MS in Business Analytics and, once accepted, can earn the degree by completing an additional 6 courses drawn from the MS in Business Analytics curriculum. Courses are selected in advisement with the Director of Graduate Programs. Students are encouraged to seek individualized advisement well before completing the MBA-IS/BA.

BA 0500 Business Analytics3 Credits

Prerequisite: OM 0400.

This course introduces basic skills necessary for business analytics such as data analysis using basic statistics, data visualization and summarization, descriptive and inferential statistics, spreadsheet modeling for prediction, linear regression, risk analysis using Monte-Carlo simulation, linear and nonlinear optimization, and decision analysis. Microsoft Excel is used as the platform for conducting analyses and performing statistical calculations.

BA 0505 Python for Business Analytics3 Credits

In this course, we introduce Python as a language and tool for collecting, preprocessing, and visualizing data for business analytics. Since Python is one of the most popular programming languages, along with R, in data mining and business analytics, its fundamental programming logic and knowledge is essential for students to apply in data mining and to succeed in the job market. Specifically, this course focuses on the data-engineering phase, which includes collecting, preprocessing, and visualizing data, with respect to applications in business modeling, optimization, and statistical analysis. In addition, a number of mini projects will be used as vehicles to cover the main applications of data analytics, including recommender systems, text analytics, and web analytics.

BA 0510 Databases for Business Analytics3 Credits

This course introduces databases and data management in three parts. The first part covers basic database fundamentals. The second part is a hands-on introduction to Structured Query Language (SQL) for defining, manipulating, accessing, and managing data, accompanied by the basics of data modeling and normalization needed to ensure data integrity. The course concludes with a comprehensive database project that gives each student the opportunity to integrate and apply the new knowledge and skills learned from this class. Advanced topics such as distributed database systems, data services, and NoSQL databases are also discussed.

BA 0540 Business Intelligence3 Credits

Prerequisites: BA 0500, BA 0510, QA 0500.

This course will change the way students think about data and its role in business. Increasingly, managers rely on intelligent technology to systematically analyze data to improve their decision-making. In many cases, automating analytical and decision-making processes is necessary because of the large volume of data and the speed with which new data are generated. In this course, we will examine how data warehousing, modeling, and visualization can be used to improve managerial decision making.

BA 0545 Data Mining3 Credits

Prerequisites: BA 0500, BA 0505.

Businesses, governments, and individuals create massive collections of data as a byproduct of their activity. In this course, we will study the fundamental principles and techniques of data mining through real-world examples and cases to place data mining techniques in context, to develop data-analytic thinking, and to illustrate that proper application of these techniques is as much an art as it is a science. In addition, we will work "hands-on" with contemporary data mining software.

BA 0590 Capstone: Business Analytics Applications3 Credits

Prerequisites: BA 0540, BA 0545.

This capstone course for the MS Business Analytics program is to be taken in the last term before graduation. The purpose is to apply and integrate knowledge and skills learned in the program (statistics, modeling, data management, data mining, etc.) to a live data analytics project. The course is project-based, with students collaborating on their work under the guidance of faculty members. Application areas and format of the projects may vary, depending on faculty, dataset, and budget availability. However, the work should be rich enough to demonstrate mastery of business modeling and technology, with each student making a unique, demonstrable contribution to completion of the work.

IS 0500 Information Systems and Database Management3 Credits

This course introduces the basic concepts and tools relevant to information systems and database management, and their enabling roles in business strategies and operations. Case studies are used to facilitate discussions of practical applications and issues involving strategic alignments of organizations, resource allocation, integration, planning, and analysis of cost, benefit and performance in light of the big data challenges. Specific emphases involve database design and implementation and emerging strategies and technologies such as business intelligence, big data management, web security, and online business analytics.

IS 0520 Project Management3 Credits

Prerequisite: IS 0500 or OM 0400.

This course explores the process and practice of project management. Topics to be covered include project lifecycle and organizations, teambuilding and productivity, task scheduling and resource allocation, and progress tracking and control. Cases will be used to consider the implications for change management, consulting, IT implementation, and other related disciplines. Small team projects and experiential exercises will also be used to provide an active learning environment. This course is designed to count toward professional project management certification.

IS 0550 Business Analytics and Big Data Management3 Credits

Prerequisites: BA 0540, BA 0545.

This course will survey state-of-the-art topics in Big Data, looking at data collection (via smartphones, sensors, the Web), data storage and processing (scalable relational databases, Hadoop, Spark, etc.), extracting structured data from unstructured databases, systems issues (exploiting multicore, security), analytics (machine learning, data compression, efficient algorithms), data visualization, and a range of applications. Each of these five modules will introduce broad concepts as well as provide the most recent developments in the area.

IS 0585 Contemporary Topics in Information Systems and Operations Management3 Credits

Prerequisite: IS 0500.

This course draws from current literature and practice on information systems and/or operations management. The topics change from semester to semester, depending on student and faculty interest and may include: project management, e-business, management science with spreadsheets, e-procurement, executive information systems, ethics, and other socio-economic factors in the use of information technology.

IS 0598 Independent Study in Information Systems and Operations Management3 Credits

This course provides an opportunity for students to complete a project or perform research under the direction of an Information Systems and Operations Management (ISOM) faculty member who has expertise in the topic being investigated. Students are expected to complete a significant project or research paper as the primary requirement of this course. Enrollment by permission of the ISOM Department Chair only.

OM 0400 Business Operations3 Credits

This course introduces basic concepts and tools relevant to operations and supply chain management, including process mapping, quality management, decision analysis, capacity planning, supply chain management, project management, and operations strategy. Case studies are used to link the concepts and models to real-world business applications.

QA 0400 Applied Business Statistics3 Credits

Using spreadsheet software, this hands-on course teaches a variety of quantitative methods for analyzing data to help make decisions. Topics include: data presentation and communication, probability distributions, sampling, hypothesis testing and regression, and time series analysis. This course uses numerous case studies and examples from finance, marketing, operations, accounting, and other areas of business to illustrate the realistic use of statistical methods.

QA 0500 Business Forecasting and Predictive Analytics3 Credits

Prerequisite: QA 0400.

This course introduces analytical techniques used for decision-making under uncertainty. Topics include time series and other forecasting techniques, such as Monte Carlo simulation, to assess the risk associated with managerial decisions. Specifically, we will cover data collection methods, time dependent models and analysis, advanced solver, time series techniques, exponential smoothing, moving averages, and Box-Jenkins (ARIMA) models. Application examples include financial models - stock prices, risk management - bond ratings, behavior models - customer attrition, customer likes/dislikes, buying patterns - propensity to buy, politics - identify swing voters, and sales.