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.
The Master of Science in Business Analytics consists of 33 credit hours (25 core credits and 8 elective credits) beyond the program prerequisites. The prequisites include both mathematics prequisites and Basic Business Knowledge (BBK) requirements.
A sample Master of Science in Business Analytics class schedule is available for both full-time and part-time students.
|Mathematics Prerequisites||MATH 1061||Calculus I||4|
|MATH 1062||Calculus II||4|
|MATH 2063||Multivariable Calculus||4|
|MATH 2076||Linear Algebra||3|
|NA||Fundamental Computing Knowledge||NA|
|All mathematics requirements must be completed before entering the program.
|Basic Business Knowledge (BBK) Requirements||ACCT 7000||Foundations in Accounting
|ECON 7000||Foundations in Economics
|FIN 7000||Foundations in Finance||1|
|IS 7011||Information and Technology Management||2|
|MKTG 7000||Marketing Foundations
|OM 7011||Management of Operations
|Each student must complete at least 4 of the 7 Basic Business Knowledge courses for a total of 8 credit hours before graduating from the program.|
|BANA 6043||Statistical Computing||2|
|BANA 7030||Simulation Modeling & Methods||3|
|BANA 7031||Probability Modeling||4|
|BANA 7051||Applied Statistical Methods||2|
|BANA 7052||Applied Linear Regression||2|
|BANA 7042||Statistical Modeling||2|
|BANA 7046||Data Mining 1||2|
|BANA 7047||Data Mining 2||2|
|BANA 8083||MS Capstone||1|
|IS 6030||Data Management
|BA 7077||Career Management||0|
|Students may select individual electives or group their choices to earn a Lindner Graduate Certificate such as Data Science.|
|BANA Elective Options (Subject to Availability)|
|BANA 6037||Data Visualization||2|
|BANA 6044||Applications Development Using VBA||2|
|BANA 7025||Data Wrangling
|BANA 7048||Multivariate Statistical Methods||2|
|BANA 7050||Forecasting and Time Series Methods||2|
|BANA 7095||Graduate Case Studies in Business Analytics||2|
|BANA 8090||Special Topics in Business Analytics||1-2|
|Non-BANA Elective Options (Subject to Availability)
|ECON 8021||Micro. Theory Topics||2|
|FIN 7045||Portfolio Management
|IS 7012||Web Development with .Net
|IS 7034||Data Warehousing and Business Intelligence
|IS 7038||Managing Business Intelligence Projects||2|
|IS 8034||Big Data Integration||2|
|MKT 7012||Marketing Research for Managers||4|
|OM 7061||Managing Project Operations
|OM 7083||Supply Chain Strategy and Analysis||2|
|At least 4 of the elective credits must be from graduate-level BANA courses. Big Data Integration is considered a BANA elective.|
||MATH 1061||Calculus I||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.||NA|
|MATH 1062||Calculus II|
|MATH 2063||Multivariable Calculus|
|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.||NA|
|NA||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 assume that a student is already comfortable with spreadsheets, word processing, e-mail, web browsers, etc.||NA|
|Core Courses||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.||Recent Syllabus|
|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.||Recent Syllabus
|BANA 7030||Simulation Modeling & 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.||Recent Syllabus|
|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 theoremSTOCHASTIC 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.||Recent Syllabus|
|BANA 7041||Statistical Methods||Basic estimation, hypothesis testing, and data analysis. Point and interval estimation. One factor ANOVA. Fitting and drawing inferences from simple and multiple linear regression models. Variable selection procedures. Residual diagnostics and model correction procedure for linear regression.||Recent Syllabus
|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 1||This is a course in the statistical data mining with emphasis on hands-on data analysis experience using various statistical methods and major statistical software (SAS and R) to analyze large complex real world data. Topics include: Data Processing. Variable Selection for linear regression and generalized linear regression. Out-of-sample Cross Validation. Generalized Additive models. Nonparametric smoothing methods. Classification and Regression Tree. Neural Network. Monte Carlo Simulation.||Recent Syllabus|
|BANA 7047||Data Mining 2||This is a course in the statistical data mining II with emphasis on hands-on data analysis experience using various statistical methods and major statistical software (SAS and R) to analyze large complex real world data. Topics include: Missing Data Imputation, Bootstrapping, Boosting and Multiple Additive Regression Trees, Bayesian Trees, Support Vector Machine, Discriminant Analysis, Cluster Analysis, Factor Analysis,Principle Component Analysis.
|BANA 8083||MS 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.||Recent Syllabus
|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.||NA|
|BANA Elective Options||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.||Recent Syllabus
|BANA 6044||Applications Development Using VBA||The use of visual basic for applications for the development of applications of management science models for planning and decision support in a spreadsheet environment. Pre-req: See your college advisor for details.||Recent Syllabus|
|BANA 7045||Design of Experiments||Analysis of data from designed experiments including: Randomized complete block, Balance incomplete block, Partially balance complete block, 2k factorial arrangements in completely randomized designs (CRDs) and blocked designs, Latin, Graeco-Latin, and Youden square, Nested and crossed-nested, Split plot, split-split plot and other variations, Strip plot, Repeated measures, Cross-over; fixed, random, and mixed models; graphical and analytic checking of model assumptions; coping with violations of assumptions and unbalanced data; generation of and analysis of fractional factorial arrangements with CRDs and blocked designs
|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.||Recent Syllabus|
|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||Recent Syllabus
|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.||Recent Syllabus|
|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||Recent Syllabus
|BANA 8090||Data Wrangling with R||This course provides an intensive, hands-on introduction to the R programming language. You will learn the fundamental skills required to acquire, munge, transform, manipulate, and visualize data in a computing environment that fosters reproducibility.|
|BANA 8090||Introduction to Python||This course is designed to be an introduction to Python with case studies and homework focused on applications in data analytics. The material will emphasize the core concepts in Python, specically data types, data structures, functions, and classes and how they can be implemented and used to address a particular problem. Popular modules used in data analysis will also be covered at a high level to include both NumPy and Matplotlib.|
|NON-BANA Elective Options||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.||NA|
|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||NA|
|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||Recent Syllabus|
|IS 7038||Managing Business Intelligence Projects||This course is designed for the in-depth learning of data-mining knowledge and techniques in the context of business intelligence. The topics include association rules, classification, clustering and text mining. Students will apply and integrate the business intelligence knowledge learned in this course in leading software packages.||Recent Syllabus|
|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.||NA
|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.||NA|
|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.||Recent Syllabus
The University of Cincinnati 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.