M.S. in Business Analytics

In our program, you’ll complete 30 credit hours: 24 core credits and 6 elective credits through our AACSB-accredited College of Business. Courses are delivered in eight-week sessions through a mobile-optimized Canvas format with guided video walkthroughs and a consistent class setup.

Required courses:

• GRBA 809 Financial Accounting: Build a foundation in financial accounting and how accounting information supports business decisions.
• GRBA 815 Supply Chain Management: Understand how operations and supply chain management connect with other parts of an organization.
• GRBA 851 Business Analytics: Learn technical procedures used to describe data, make predictions and use those predictions to support decision-making.
• SCMA 851 Predictive Analytics: Explore analytical methods and statistical programming languages such as R that are commonly used for predictive analytics in business.
• SCMA 852 Data Management and Organization: Build skills in database design and SQL to organize and work with business data.
• SCMA 853 Data Mining and Descriptive Analytics: Use statistical and artificial intelligence tools to identify patterns and summarize information in business data.
• SCMA 837 Risk and Simulation Modeling: Design, implement and analyze a full simulation to evaluate different business decision scenarios.
• SCMA 855 Prescriptive Analytics: Learn analytical methods and optimization software used to evaluate options and recommend business decisions.

Elective courses:

• MRKT 845 Digital Marketing Analytics: Learn to analyze web, social media and consumer text data, including large data streams from digital platforms.
• MRKT 850 Data-Driven Decision Making: Use marketing data to support strategic and tactical decisions in areas like pricing, advertising, forecasting and resource allocation.
• ECON 817 Introductory Econometrics: Apply basic econometric methods to estimate economic models, analyze business data, test hypotheses and make predictions.
• GRBA 813 Managerial Marketing: Build analytical and decision-making skills for product, distribution, selling, advertising and pricing decisions.
• SCMA 836 Project Management and Implementation: Learn tools and techniques for planning, budgeting, scheduling, risk management and project implementation.
• SCMA 847 Information Technologies for Operations and Innovation: Explore how business leaders use IT to operate, innovate, grow and transform organizations.

You’ll typically take no more than two courses per session, and there are no official full-time or part-time designations. Start dates are available in May, August and January, with application deadlines of April 1, July 1 and November 1. For professionals comparing business analytics master’s programs online, the format keeps the logistics straightforward while you continue working.

Among masters of science in business analytics, University of Nebraska-Lincoln’s required sequence moves from organizing data to predicting outcomes, testing scenarios and recommending a response. Predictive Analytics introduces statistical programming such as R, while Data Management and Organization adds database design and SQL. In Risk and Simulation Modeling, you design and analyze a full business simulation. Prescriptive Analytics introduces optimization modeling. Data Mining and Descriptive Analytics also incorporates statistical and artificial intelligence tools used in industry, giving you exposure to AI in business analytics without separating the technology from the business question.

Skills you’ll develop include:

• Descriptive, predictive and prescriptive analytics: Understand what happened, what may happen next and which actions to consider, a useful progression for roles that turn data into recommendations.
• R and SQL for business analytics and data management: Work with programming and query tools used to analyze information and organize data for decision-making.
• Data mining, database design and organization: Learn how to uncover useful patterns while structuring data so it can be accessed and analyzed effectively.
• Risk, simulation and optimization modeling: Test scenarios, evaluate uncertainty and compare alternatives before recommending a course of action.
• Data-driven decision-making across operational and strategic business questions: Translate analysis into evidence that can support both day-to-day operations and longer-term business decisions.

A bachelor’s degree is required, with a 3.0 GPA recommended. Applicants with a GPA below 3.0 may still be considered when they bring relevant professional experience. GMAT or GRE scores are optional. Applicants should also submit a professional résumé or CV and three references; Excel proficiency is expected before beginning the program. International applicants must meet the program’s English-language proficiency requirements.

Submit the online application, unofficial transcripts, a professional résumé or CV and three references for review. Recommendation letters are not required. Admitted students must provide official transcripts.

Admitted students may apply for College of Business fellowships by June 15 for fall awards. Keep this deadline and any fellowship eligibility details linked to the current fellowship information so prospective students can verify the latest requirements.

Yes, if you want greater analytical responsibility and the curriculum matches where you want your career to go. For prospective students comparing masters of science in business analytics or masters of business analytics, our program combines graduate-level quantitative study with business coursework and career support. The strongest test of value is whether that combination fits the work you want to be trusted to do.

It can help you move toward greater analytical responsibility by adding graduate-level skills in data management, modeling and business decision-making to the experience you already have. For professionals considering online master's in business analytics options, University of Nebraska-Lincoln also lets you keep working while you study, creating opportunities to test new ideas on the job before you finish the degree.

It is quantitatively focused, but the technical work stays tied to business use. You’ll work with R and SQL and study data mining, database organization, simulation, optimization and AI in business analytics, with Excel proficiency expected before you begin. The emphasis is on using those tools to evaluate business questions rather than learning technology in isolation.

A business intelligence analyst typically focuses on dashboards, reporting and turning organizational data into insights for decision-makers. A business analyst more often connects business needs with processes, systems or solutions, while a data analyst works more directly with datasets to identify patterns and answer specific questions. A management analyst may take a broader view of organizational efficiency and performance. Which direction fits best will depend on your prior experience, the business areas that interest you and how you want to use analytics in your work.

You can use the degree to analyze business data, build predictive models, evaluate risk, test alternatives and translate findings into recommendations. If you study at Nebraska, you’ll be able to apply those capabilities across operations, supply chain, marketing, finance and other business functions that rely on data-informed choices.

One route is to combine business knowledge with analytical tools, then build experience applying both to real decisions. If you are researching how to become a business analyst, University of Nebraska-Lincoln’s MSBA offers graduate study in data management, modeling and decision-focused analytics while allowing you to continue working.

Look for a program that teaches prediction, statistical programming and how results inform business choices. For prospective students comparing predictive analytics masters programs, our Predictive Analytics course introduces analytical methods and programming such as R, while the wider curriculum connects that work with descriptive and prescriptive analytics, data mining, simulation and optimization.