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 is designed to be completed either in one year's time (full time) or over two years (part time). The program may be taken either asynchronously online or in-person, and students can choose either one for any course. Students may opt to use their elective courses to earn a specialization that is tailored to their career interests. Available specializations include: Artificial Intelligence, Financial Planning and Analysis, Healthcare, Marketing Analytics, Quantitative Finance, and, for those who have an undergraduate degree in Accounting or the equivalent, Accounting.


DATA 6500Business Analytics 13
DATA 6505Python for Business Analytics3
DATA 6510Databases for Business Analytics3
DATA 6530Business Forecasting and Predictive Analytics 13
DATA 6540Business Intelligence3
DATA 6545Machine Learning for Predictive Analytics3
DATA 6999Capstone: Business Analytics Applications3
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 will take any three graduate Accounting or Taxation courses at the 6000-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.

Artificial Intelligence Specialization

To complete a specialization in Artificial Intelligence, students will take three courses involving any combination of ISOM 6550 and/or ISOM 6900 courses. ISOM 6550 may be taken, at most, once for credit for this specialization. The topics course ISOM 6900 may be taken multiple times for credit, up to three times if ISOM 6550 is not taken, or up to two times if ISOM 6550 is taken, as long as the topics offered are different. Topics courses are expected to be offered on sports analytics, deep learning, classic AI, and more.

ISOM 6550Business Analytics and Big Data Management3
or ISOM 6900 Contemporary Topics Seminar
ISOM 6900Contemporary Topics Seminar3
ISOM 6900Contemporary Topics Seminar3
Total Credits9

Financial Planning and Analysis Specialization

To complete a specialization in Financial Planning and Analysis, students will complete the following courses as their required electives:

ACCT 6500Accounting Information for Decision-Making3
FNCE 6500Stakeholder Value3
FNCE 6530Corporate Finance3
Total Credits9

Healthcare Specialization

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

Select three courses from the following:9
Healthcare Economics and Marketing
Advanced Healthcare Policy
Finance and Quality Management in Healthcare Organizations
Information Technology for Healthcare Improvement
Total Credits9

Marketing Analytics Specialization

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

MKTG 6520Research for Marketing Insights and Decisions3
MKTG 6580Multivariate Analysis for Consumer Insights3
Select one course from the following:3
Customer Experience
Category Management and Shopper Insights
Digital Marketing and Analytics
Pricing Strategies and Analytics
Experimental Research
Contemporary Topics
Total Credits9

Note: MKTG 5400 or its equivalent is a required prerequisite for all upper-level marketing courses.

Quantitative Finance Specialization

To complete a specialization in Quantitative Finance, students will complete the following courses as their required electives:

FNCE 6540Investment Analysis 3
Select two courses from the following:6
Portfolio Management
Derivative Securities
Fixed Income Securities
Financial Risk Management
Research Methods in Finance
Total Credits9

Dual Degree MBA and MS in Business Analytics

Students may pursue dual degrees, earning both a Master in Business Administration and a Master of Science in Business Analytics, in less time and with fewer credits than if they were to complete both degrees separately. Please see the Dual Degree MBA/MSBA section of this catalog for details.

DATA 5400 Applied Business Statistics    3 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. Previously QA 0400, BUAN 5400.

DATA 5405 Python Fundamentals    3 Credits

This course is an introduction to Python, with an emphasis on general programming concepts (structure, logic, data, etc.) that apply to just about any general purpose programming language. Starting with a review of fundamental programming concepts, the course uses short lessons, quizzes, and coding challenges to cover the basics of how Python is used in a professional Business Analytics setting. The course concludes with a final project designed to demonstrate proficiency. Previously BA 0405, BUAN 5405.

DATA 5410 Analytics Programming for Business    1.5 Credits

This course focuses on quantitative modeling and analyzing business problems using spreadsheet software, such as Excel and its add-ins. Topics include descriptive analytics, visualizing and exploring data, predictive modeling, regression analysis, time series analysis, portfolio decisions, risk management, and simulation. Business models relevant to finance, accounting, marketing, and operations management are set up and solved, with managerial interpretations and "what if" analyses to provide further insight into real business problems and solutions. Open to MS Management students only. Previously BA 0410, BUAN 5410.

DATA 6100 Fundamentals of Analytics    3 Credits

This is an introductory level graduate course focusing on spreadsheet modeling to analyze and solve business problems. Topics include descriptive analytics, data visualization, predictive modeling, time series analysis, and data mining. Contemporary analytical models utilized in finance, marketing, accounting, and management are set up and solved through case studies. Previously IS 0500, ISOM 6500.

DATA 6500 Business Analytics    3 Credits

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. Previously BA 0500, BUAN 6500.

DATA 6505 Python for Business Analytics    3 Credits

Prerequisite: DATA 5405 or placement exam.

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. Previously BA 0505, BUAN 6505.

DATA 6510 Databases for Business Analytics    3 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. Previously BA 0510, BUAN 6510.

DATA 6530 Business Forecasting and Predictive Analytics    3 Credits

Prerequisite: DATA 5400 or placement exam.

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. Previously QA 0500, BUAN 6530.

DATA 6535 Advanced Sports Analytics    3 Credits

Sports analytics is transforming the way teams, leagues, players, coaches, referees, and fans perceive and appreciate their favorite pastimes and games, including major team sports such as baseball, basketball, football, soccer, cricket, and rugby, more individualized sports like tennis and golf, and brand-new innovations such as e-sports. In this course, students will gain experience in framing analytical questions in sports, discover and evaluate cutting-edge research and findings in sports analytics, develop hands-on skills in using and implementing sports analytics solutions, and learn how to communicate findings to a non-analytical audience in an impactful and actionable way. This course culminates in a scholarly sports analytics research paper.

DATA 6540 Business Intelligence    3 Credits

Prerequisites: DATA 6500, DATA 6510.

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. Previously BA 0540, BUAN 6540.

DATA 6545 Machine Learning for Predictive Analytics    3 Credits

Prerequisites: DATA 6505, DATA 6530.

This course provides an advanced understanding of the practices of machine learning techniques, with a special focus on business applications. To assure practical relevance, the emphasis of this course is on the applications of techniques and tools realizing machine learning interms of business analytics. The course is organized following the Cross-Industry Standard Process for Data Mining (CRISP-DM) and all learned techniques are applied in a semester-wide project. Python is introduced and illustrated through a series of tutorials and case studies. Students are expected to actively participate in the course deliverables through independent assignments, lab work, and group projects. Previously BA 0545, BUAN 6545.

DATA 6999 Capstone: Business Analytics Applications    3 Credits

Prerequisites: DATA 6530, DATA 6540, DATA 6545.

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. Previously BA 0590, BUAN 6999.

The Dolan Career Development Center provides professional development services that enrich graduate students’ academic experiences and inspire tomorrow’s business leaders. For more information, reference the Career Development section of this catalog.