Master of Science in Business Analytics

The University of Cincinnati’s Master of Science in Business Analytics program is nationally recognized and has a proven track record with placing students at successful, high-profile companies. Predictive Analytics Today named UC as the No. 1 MS Data Science school in the country four successive years. Financial aid is available in the form of scholarships and assistantships and is awarded on a competitive basis.

Lindner's Master of Science in Business Analytics program provides you with expertise in descriptive, predictive and prescriptive analytics. This program begins in August and classes are offered full- or part-time. Full-time students typically complete the program in nine months or one year. Classes are available in the late afternoon, evening and Saturday for part-time students.

How long does it take to complete the Master of Science in Business Analytics program?

The program can be completed in two semesters but many students use a third semester to complete the capstone project.

Is the Master of Science in Business Analytics a designated STEM Program?

Yes, the Master of Science in Business Analytics program has been officially designated as a STEM (Science, Technology, Engineering, and Mathematics) program.

When is the application deadline?

Please visit the program's admission page for deadlines.

Why earn a Business Analytics master’s degree?

Businesses across the country and world are looking for business analysts with at least a master’s degree. A Master of Science not only puts you ahead of the competition, but it also provides the skills needed to successfully impact business decisions. At the University of Cincinnati, we feature on-campus employer seminars and recruiting. Each week, major employers seeking analysts and technical talent present seminars, recruit, or interview MS Business Analytics students. Some companies include Tata Consultancy Services, Great American Insurance, GE Digital, Teradata, PWC, DMI, 84.51, and Fifth Third Bank.

Our convenient class options allow students to earn a life-changing degree without drastically changing their schedule. Full-time students can earn their degree in a year or less, allowing them to apply their skills in a real-world setting in just a couple of semesters. Recent graduates earn an average starting salary of more than $115,000.

Another benefit of our MS program is the corporate and academic partnership with the Center for Business Analytics which provides real-world data analytics experience and career opportunities for students and promotes industry leadership and research to advance the field of business analytics.

Below is a select list of employers who have recently Lindner Master of Science in Business Analytics graduates:

  • 84.51°
  • Amazon
  • Apple Inc.
  • Barclays PLC
  • Capital One Financial Corporation
  • Cincinnati Bell Inc.
  • Cincinnati Children's Hospital
  • Convergys
  • Dell Inc.
  • Discover
  • eBay
  • EY
  • Facebook
  • Fifth Third Bank
  • Google
  • IBM Corporation
  • Johnson & Johnson, Inc.
  • JP Morgan Chase
  • Macy's, Inc.
  • Procter & Gamble
  • PwC LLP
  • The Walt Disney Company
  • TriHealth
  • Wal-Mart

Paul Bessire

Paul Bessire headshot

Title: Co-founder

Paul is one of the foremost authorities on mathematically modeling and analyzing sports. He has turned his longtime hobby of predicting and writing about sports outcomes into a full-time profession.

Today Paul is an adjunct professor at the University of Cincinnati where he teaches “Bracketology” with Mike Magazine, a guest lecturer for “Sports by the Numbers,” and a frequent guest analyst on fantasy sports and talk shows across the country on networks such as ESPN and CBS.

"The Business Analytics program at the University of Cincinnati is not just a world-class analytics program. It’s a world-class program at the intersection of big data, technology, and efficient decision-making. The applications of the program should continue to grow in importance within businesses in all industries.”

Mark Boone

Mark Boone headshot

Employer: Cincinnati Reds
Title: Business Analytics Developer

Mark leads data visualization development for the Cincinnati Reds using Tableau software. Mark has built a number of meaningful dashboards for the Reds, some of which incorporate a map he created to plot all seats within the ballpark. The granular detail of his map allows leadership to view data a number of ways, including visualizing where certain types of ticket packages are selling, detecting notable pricing differences between the box office and secondary market, and identifying potential sponsorship opportunities.

"Cincinnati's program is second to none in its ability to prepare students for careers in a wide variety of analytical fields. While offering a strong mathematical foundation via exposure to large datasets and the opportunity to solve real-world problems, its focus on logical reasoning and adaptive problem-solving skills result in an overarching strength, preparing you for any scenario you may face in the workplace."

Liberty Holt

Liberty Holt headshot

Employer: TriHealth
Title: Manager of Predictive Analytics

After graduation from the University of Cincinnati business analytics program, Liberty accepted a position as a data scientist at TriHealth in collaboration with Hatton Research where she was responsible for developing and implementing the use of predictive models. Liberty was soon promoted to manager of predictive analytics, where she is able to mentor others and influence the future direction of analytics at TriHealth. She has the opportunity to explore cutting edge analytics  using machine learning in combination with Watson Content Analytics, Hadoop, and other developing technologies. She is also developing a team to ensure that any models or analytics that are developed translate into opportunities to give the best care to the patients of her organization.

“I will forever be grateful for the opportunity to complete the UC analytics program. I gained skills and learned a vast array of analytic techniques, but beyond that it also offered many opportunities to grow both professionally and personally.”


The University of Cincinnati Master of Science in Business Analytics program is designed to provide you with a strong foundation in all areas of business analytics while allowing you flexibility to tailor the program based on your individual interests and career plans. Our program is nationally ranked yet competitively-priced and our business analytics faculty is comprised of both world-class instructors who conduct advanced analytics research and real-world industry practitioners.

Mathematics requirements which must be completed before entering the MS Business Analytics program.
Course number Course title Credits
MATH 1061 Calculus I 4
MATH 1062 Calculus II 4
MATH 2063 Multivariable Calculus 4
MATH 2076 Linear Algebra 3
n/a Fundamental Computing Knowledge n/a

MATH 1061: Calculus I, MATH 1062: Calculus II, and MATH 2063: Multivariable Calculus
These courses are not the applied courses typically taken by business or social science majors. Topics of coverage should include: functions; limits and continuity; derivatives; applications of the derivative; the integral and applications; exponential and logarithmic functions; inverse functions; techniques of integration; polar coordinates; conic sections; Taylor's formula; improper integrals; sequences and series; vectors; lines; planes; vector-valued functions; partial derivatives; multiple integrals; calculus of vector fields.

MATH 2076: Linear Algebra
Topics should include: systems of linear equations; matrices; vector spaces; bases and dimension; orthogonality; linear transformations; determinants; eigenvalues and eigenvectors; diagonalization.

Fundamental Computing Knowledge
This includes facility in a procedural programming language like Ruby, Python, C, C++, Matlab, Java, Visual Basic, Pascal, or FORTRAN. It is also assumed that a student is already comfortable with spreadsheets, word processing, e-mail, web browsers, etc.

Core courses

BANA 6037: Data Visualization
This course provides an introduction as well as hands-on experience in data visualization. It introduces students to design principles for creating meaningful displays of quantitative and qualitative data to facilitate managerial decision-making.

BANA 7020: Optimization
An introduction to modeling, solving with state-of-the-art software, and interpreting the results for real-world linear, integer, and nonlinear optimization applications. Solution techniques and analyses covered include graphical approaches, the simplex method, duality, and sensitivity for linear optimization; branch-and-bound and cutting plane techniques for integer optimization; and Newton’s method and gradient search for nonlinear optimization.

BANA 7025: Data Wrangling
This course provides an intensive, hands-on introduction to data management and data manipulation. You will learn the fundamental skills required to acquire, munge, transform, manipulate, and visualize data in a computing environment that fosters reproducibility.

BANA 7030: Simulation Modeling and Methods
Building and using simulation models of complex static and dynamic, stochastic systems using both spreadsheets and high-level simulation software. Topics include generating random numbers, random variates, and random processes, modeling systems, simulating static models in spreadsheets, modeling complex dynamic stochastic systems with high-level commercial simulation software, basic input modeling and statistical analysis of terminating and steady-state simulation output, and managing simulation projects. Applications in complex queueing and inventory models representing real systems such as manufacturing, supply chains, healthcare, and service operations.

BANA 7031: Probability Modeling
PROBABILITY MODELS: Events, probability spaces and probability functions; Random variables; Distribution and density functions; Joint distributions; Moments of random variables; Special expectations; Moment generating functions; Conditional probability and conditional moments; Probability inequalities; Independence; Special probability distributions including: binomial, negative binomial, multinomial, Poisson, gamma, chisquare, normal, beta, t, F, mixture distributions, multivariate normal; Distribution of functions of random variables; Order statistics; Asymptotic results including: convergence in distribution, central limit theorem, convergence in probability, Slutsky's theorem. STOCHASTIC MODELS: Discrete time Markov processes, Markov pure jump processes, Birth and death processes, Branching processes, Poisson process, Pure birth processes, Yule process; applications in several areas, e.g. queuing models, machine repair models, inventory models, etc.

BANA 7042: Statistical Modeling
Nonlinear regression and generalized linear model. Logistic regression for dichotomous and polytomous responses with a variety of links. Count data regression including Poisson and negative binomial regression. Variable selection methods. Graphical and analytic diagnostic procedures. Overdispersion. Generalized additive models. Limited dependent variable regression models (Tobit), Panel Data models.

BANA 7046 Data Mining I
This is a course in the statistical data mining with emphasis on hands-on case study experience using various data mining/machine learning methods and major software to analyze complex real world data. Topics include: Data Preprocessing. k-Nearest Neighbors. Linear Regression and Generalized Linear Regression. Subset and LASSO Variable Selection. Model Evaluation. Cross Validation. Classification and Regression Trees.

BANA 7047: Data Mining II
This is a course in the statistical data mining with emphasis on hands-on case study experience using various data mining/machine learning methods and major software to analyze complex real world data. Topics include: Advanced Trees: Bagging, Random Forests, Boosting. Nonparametric Smoothing methods. Generalized Additive Models. Data Preprocessing/Scaling. Neural Networks. Deep Learning. Cluster Analysis. Association Rules.

BANA 7051: Applied Statistical Methods
This course covers applied statistical methods, including topics of frequency distributions, estimation, hypothesis testing, point and interval estimation for mean and proportion; comparison of two populations; goodness of fit tests, one factor ANOVA. Major statistical software is used.

BANA 7052: Applied Linear Regression
This course covers applied linear regression, including topics of fitting and drawing inferences from simple and multiple linear regression models; residual diagnostics; model correction procedure for linear regression; variable selection. Major statistical software is used.

BANA 8083: Capstone
This course is associated with the required MS Business Analytics Capstone. The Capstone experience will be described in an essay that is reviewed and approved by two faculty members. The essay can describe: (1) a research project based onan idea proposed independently by the student or with faculty input; (2) an extension of a case analysis or project completed in a class such as BANA7095, Graduate Case Studies in Business Analytics. The essay must describe the student's contribution to the research or case.

IS 6030: Data Management
This course provides an introduction to the use and design of databases to store, manipulate and query data. The course introduces the structured query language (SQL) used to manage data. Students who complete this course should understand how to use SQL for basic data manipulation and queries. This course is intended for users of existing databases to extract needed information and should not be taken by MSIS students or those students who wish to learn detailed database design techniques.

BA 7077: Career Management
All full-time Lindner graduate students are required to register for the course BA7077 Graduate Career Management. This course includes both in-class meetings and deliverables such as resume revision, LinkedIn profiles, mock-interviews, etc. These will be graded on a Pass/Fail basis. This course provides Lindner College of Business graduate students with an advanced set of necessary skills and tools for continuous professional development and/or conducting a strategic job search in his/her field of choice.

BANA electives

BANA 6043: Statistical Computing
This is a course on the use of computer tools for data management and analysis. The focus is on a few popular data management and statistical software packages such as SQL, SAS, SPSS, S Plus, R, and JMP although others may be considered. Data management and manipulation techniques including queries in SQL will be covered. Elementary analyses may include measures of location and spread, correlation, detection of outliers, table creation, graphical displays, comparison of groups, as well as specialized analyses.

BANA 7048: Multivariate Statistical Methods
This is a course in the analysis multivariate data with emphasis on appropriate choice of estimation and testing methods. Vectors and matrices, Multivariate probability distributions and their parameter, Multivariate normal distributions, Maximization and minimization of multivariate functions, The "shape" of multivariate normal data, Correlation, prediction and regression, Sample statistics and their sampling distributions for multivariate normal data; Estimation and tests for correlation, Tests of independence, Estimation and tests for multivariate means and covariance matrices, ower of multivariate tests, multivariate linear models, canonical correlation analysis, Principal components analysis, Factor analysis, Classification and discrimination analysis.

BANA 7050: Forecasting and Time Series Methods
This is a course in the analysis of time series data with emphasis on appropriate choice of forecasting, estimation, and testing methods. Univariate Box-Jenkins methodology for fitting and forecasting time series. ARIMA models, Stationarity non-Stationarity, auto-correlation functions, partial and inverse autocorrelation functions, Estimation and model fitting, Diagnosing time series models, Forecasting: Point and interval forecasts, Seasonal time series models,Transfer function models, Intervention models, Modeling volatility with ARCH, GARCH, and other methods, Modeling time series with trends, Multiequation time series models: Vector Auto Regression (VAR), Cointegration and error correction models, Nonlinear time series models, State space time series models, Bayesian time series and forecasting.

BANA 7075: Machine Learning Design
This course aims to provide a framework for developing real-world machine learning systems that are deployable, reliable, and scalable. Machine learning systems design is the process of defining the software architecture, infrastructure, algorithms, and data for a machine learning system to satisfy specified requirements.

BANA 7095: Graduate Case Studies in Business Analytics
Real organizational problems or challenges will be presented to students by client companies. Students in groups will work with a client to develop a solution or solutions to the problems using advanced analytic techniques. Students will present the solutions to the client in both oral and written reports.

BANA 8090: Special Topics in Business Analytics
This course is used to explore topics of current interest in the BANA domain, that do not fall within the scope of any of the regularly scheduled courses. By the nature of the course, specific topics covered will vary with each offering.

Non-BANA elective options

CS 6052: Intelligent Data Analysis
This course will introduce students to the theoretical and practical aspects of the field of data mining. Algorithms for data mining will be covered and their relationships with statistics, mathematics, and algorithm design foundations will be explored in detail.

ECON 8021: Micro Theory Topics

FIN 7045: Portfolio Management
This course will give students an understanding of the implications of standard asset pricing models, market efficiency, optimization, asset allocation, and portfolio risk management.

IS 7012: Web Development with .Net
This course is an introduction to the development of web-based applications, using Microsoft's Visual Studio and covering ASP.Net using Visual C#. Students will be expected to develop a simple web application that incorporates these technologies. Students will learn how to integrate the frontend (web site) with the back end (database) of an application. The course will cover the implementation of navigational structures, input and validation controls, and data controls in web applications.

IS 7034: Data Warehousing and Business Intelligence
This course is designed for the comprehensive learning of data warehousing technology for business intelligence. Data warehouses are used to store (archive) data from operational information systems. Data warehouses are useful in generating valuable control and decision-support business intelligence for many organizations in adjusting to their competitive environment. This course will introduce students to the design, development and operation of data warehouses. Students will apply and integrate the data warehousing and business intelligence knowledge learned in this course in leading software packages.

IS 7065: Generative AI for Business
This course examines the technology underlying modern generative artificial intelligence / machine learning models from a business perspective, including their uses in coding, professional and artistic applications, and the various controversies and challenges to work and/or society they may pose.

IS 7085: Governance of AI/ML
This course teaches students how to develop, scale-up, and sustainably manage high-performing Artificial Intelligence/Machine Learning systems in business organizations. It introduces concepts and techniques that enable the development of surrogate approaches to explain AI/ML models, build redundancy in AI/ML systems, and calculate and minimize risk of failures while using such approaches

IS 8034: Big Data Integration
This course presents an overview of the principles of data integration, the fundamental basis for developing useful and flexible business intelligence platforms. Modern data integration needs differ from traditional approaches in four main dimensions that parallel differences between big data and traditional data: volume, velocity, variety, and veracity.

IS 8070: Special Topics
This course is used to explore topics of current interest in the IS domain that do not fall within the scope of any of the regularly scheduled courses. By the very nature of the course, specific topics covered will vary with each offering.

MKT 7012: Marketing Research for Managers
Explores the role of marketing research in marketing management. Involves hands-on activities to perfect understanding of methods for collecting, analyzing, and summarizing data pertinent to solving marketing problems.

OM 7061: Managing Project Operations
This course covers detailed issues related to managing product development and projects in organizations. The course covers, in two separate modules:-Concepts of project planning and organization, budgeting and control, and project life cycles and concepts related to organizational workflow including the staffing process, and project planning elements; related concepts of organizational forms, conflict resolution, and issues related to leadership and task management in a project environment.-Advanced concepts of project scheduling, including WBS, CPM, PERT, simulation, project budgeting, earned value analysis, project tracking and resource constrained scheduling. This includes setting up projects on Microsoft Project and using the information for budgeting, resource management, tracking and ongoing communication and evaluation of projects.

OM 7083: Supply Chain Strategy and Analysis
Presents an overview of issues relating to the design and operation of an organization's supply chain. Information is presented as a mix of technical models and applied case studies. Topics may include inventory planning, logistics, sustainability, global operations, supply chain collaboration and contracting.

Master of Science in Business Analytics program requires the completion of a capstone experience. Students will describe their capstone experience in an essay of eight to fifteen pages. The essay will be based on one of the following:

  • Research Project: The content of the essay must be substantive in terms of containing technical, quantitative modeling, analysis, or programming/coding aspects and not a survey or exposition of the work of others. The appendices of the essay may contain supporting figures and tables, computer files that contain the data used, model formulations and computer code.
  • Extension of a Course Case Analysis/Project: This essay is an extension of a case analysis or project completed in a class such as BANA7095, Graduate Case Studies in Business Analytics. Even if the original case analysis/project was a group effort, the essay must still be an individual effort. The content must extend the original work and a copy of the original work must also be submitted. Acceptable extensions can include the application of different modeling methodologies to the same data set to compare results or the use of additional data to generate new insights or to confirm the original findings.
  • Description of an Internship Project: This essay describes the student’s contribution to a project completed during a one or two semester internship taken as part of the student's MS-Business Analytics course work. The content must demonstrate the student’s knowledge of Business Analytics concepts and the student’s ability to implement those concepts. It is understood that for proprietary reasons, the essay may not contain the level of detail expected in a Research Project or Case Analysis essay.

Examples of past Business Analytics capstone projects are available for review.

These are typical MS Business Analytics schedules, and they assume all Basic Business Knowledge (BBK) prerequisites have been fulfilled. The program consists of 33 total credits; 25 from core BANA courses (24 credits for formal coursework, one credit for BANA 8083 capstone), and eight from electives, at least four of which must be BANA-prefixed courses at the 6000 level or above. All electives must be approved by the academic director.

Full-time study

One year program.

Sample fall semester schedule for full-time students. The semester may also include three possible credits of electives.
Course number Course title Credits
BANA 6037 Data Visualization 2
BANA 7030 Simulation Modeling and Methods 3
BANA 7025 Data Wrangling 2
BANA 7031 Probability Modeling 2
BANA 7051 Applied Statistical Methods 2
BANA 7052 Applied Linear Regression 2
IS 6030 Data Management 2
Sample spring semester schedule for full-time students. The semester may also include five to eight credits of electives, heeding course prerequisites. Students may finish and graduate.
Course number Course title Credits
BANA 7020 Optimization 3
BANA 7042 Statistical Modeling 2
BANA 7046 Data Mining I 2
BANA 7047 Data Mining II 2
BANA 8083 Capstone* 1
Sample summer semester schedule for full-time students.
Course number Course title Credits
BANA 8083 Capstone* 1

*BANA 8083 should be taken in the semester in which the student will graduate.

Part-time study

Part-time students typically, though not always, complete the program in two years.

Year one

Sample fall semester schedule for the first year of a two-year part-time study program.
Course number Course title Credits
BANA 6037 Data Visualization 2
BANA 7025 Data Wrangling 2
BANA 7051 Applied Statistical Methods 2
BANA 7052 Applied Linear Regression 2
Sample spring semester schedule for the first year of a two-year part-time study program. Also includes two credits of electives.
Course number Course title Credits
BANA 7042 Statistical Modeling 2
BANA 7046 Data Mining I 2
BANA 7047 Data Mining II 2
Sample summer semester schedule for the first year of a two-year part-time study program. The schedule may also include electives, depending on course offerings.
Course number Course title Credits
BANA 8083 Capstone* 1

Year two

Sample fall semester schedule for the second year of a two-year part-time study program.
Course number Course title Credits
BANA 7030 Simulation Modeling and Methods 3
BANA 7031 Probability Modeling 2
IS 6030 Data Management 2
Sample spring semester schedule for the second year of a two-year part-time study program. The schedule may also include four to six credits of electives, heeding prerequisites. The student may complete the program and graduate.
Course number Course title Credits
BANA 7020 Optimization 3
BANA 8083 Capstone* 1

*BANA 8083 should be taken in the semester in which the student will graduate.

Contact Us

Headshot of Michael Platt

Michael Platt

MS BANA Academic Director, Assistant Professor-Educator, Department of Operations, Business Analytics, and Information Systems

3459 Carl H. Lindner Hall