Curriculum: Master of Science in Business Analytics

Next Term Application Deadline

April 20, 2023

Total Credits

33-41 credit hours

Next Term Starts

May 08, 2023

Request More Information from an Enrollment Advisor

Step 1 of 2

Program of Interest*
Program requires the completion of an associate degree program. Have you earned an associate degree?*
- Yes (includes in-progress degree)
- No
Desired Start Date*

Name*
First Name Last Name
Email*
Country*

Country
*
State
ZIP
Hidden
*
Region Postal Code
Phone*
Phone*
Military Affiliated
Freely given consent from contact*
By submitting this form I accept the privacy policy and understand that University of Cincinnati may contact me via telephone, email, and/or text messages about educational programs using an automated technology.*
Email
This field is for validation purposes and should be left unchanged.

Learn More from Our Academic Director Dr. Denise White

Were you able to attend our information session? If not, you can view it here! Take some time learn more about our Master's program from Program Director Dr. Denise White and ESA, Spencer Bradley!

Application Fees Waived for Summer 2023!

Apply today and we'll waive the $65 application fee.

Curriculum at a Glance

In the Masters in Business Analytics program, students work on real-world projects and learn the skills necessary to succeed in data visualization, statistical modeling, data mining and machine learning, optimization, and simulation in order to proficiently analyze large datasets and generate actionable insights. Classes include Data Wrangling, Data Visualization, Statistical Computing, Statistical Modeling, Data Mining, Big Data Integration/Warehousing, and Machine Learning.

UC Online’s Master’s in Business Analytics seeks full-time and part-time students with quantitative or technical backgrounds (mathematics, engineering, statistics, science, economics etc.) who are interested in pursuing careers in the fields of business analytics and data science.

Speak with an Enrollment Services Advisor today!

CURRICULUM AT A GLANCE

MS-BANA Core Courses MS-BANA Capstone MS-BANA Electives (BANA)MS-BANA Electives (non-BANA)Business Foundation Courses

Students must complete 12 of the following core courses ( 25 credit hours)

Course	Title / Description	Credit
BANA7025	Data Wrangling Course: BANA7025 Credit: 2 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.	2
BANA7020	Optimization Course: BANA7020 Credit: 3 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.	3
BANA7030	Simulation Modeling and Methods Course: BANA7030 Credit: 3 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.	3
BANA7031	Probability Models Course: BANA7031 Credit: 2 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, chi-square, 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.	2
BANA7042	Statistical Modeling Course: BANA7042 Credit: 2 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. Over dispersion. Generalized additive models. Limited dependent variable regression models (Tobit), Panel Data models.	2
BANA7046	Data Mining I Course: BANA7046 Credit: 2 This is a course in statistical data mining with emphasis on hands-on case study experiences using various data mining/machine learning methods and major software packages to analyze complex real world data. Topics include data preprocessing, k-nearest neighbors, generalized linear regression, subset and LASSO variable selection, model evaluation, cross validation, classification and regression trees.	2
BANA7047	Data Mining II Course: BANA7047 Credit: 2 This is a course in statistical data mining with emphasis on hands-on case study experiences using various data mining/machine learning methods and major software packages 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.	2
BANA7051	Applied Statistical Methods Course: BANA7051 Credit: 2 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.	2
BANA7052	Applied Linear Regression Course: BANA7052 Credit: 2 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.	2
IS6030	Data Management Course: IS6030 Credit: 2 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.	2
BANA6037	Data Visualization Course: BANA6037 Credit: 2 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.	2

BANA 8083 A minimum grade of C is required for this course.

Course	Title / Description	Credit
BANA8083	MS Capstone Course: BANA8083 Credit: 1 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.	1

Students are required to take 8 elective hours, four of which must be from BANA courses. Substitutions must be approved by the program director.

Course	Title / Description	Credit
BANA6043	Statistical Computing Course: BANA6043 Credit: 2 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.	2
BANA6044	Applications Development using VBA Course: BANA6044 Credit: 2 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.	2
BANA7048	Multivariate Statistical Methods Course: BANA7048 Credit: 2 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, Power of multivariate tests, multivariate linear models, canonical correlation analysis, Principal components analysis, Factor analysis, Classification and discrimination analysis.	2
BANA7050	Forecasting and Time Series Methods Course: BANA7050 Credit: 2 This is a course in the analysis of time series data with emphasis on appropriate choice of models for estimation, testing, and forecasting. Topics or methodologies covered include Univariate Box-Jenkins for fitting and forecasting time series; ARIMA models, stationarity and nonstationarity; diagnosing time series models; transformations; forecasting: point and interval forecasts; seasonal time series models; modeling volatility with ARCH, GARCH; modeling time series with trends; and other methods.	2
BANA7095	Graduate Case Studies in Business Analytics Course: BANA7095 Credit: 3 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.	3
BANA8090	Special Topics in Business Analytics Course: BANA8090 Credit: 1-4 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.	1-4

Four of the eight required elective credit hours can come from non-BANA graduate courses. Selected courses are listed below. Substitutions can be made but must be approved by the program director.

Course	Title / Description	Credit
CS6052	Intelligent Data Analysis Course: CS6052 Credit: 3 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.	3
ECON8021	Game Theory Course: ECON8021 Credit: 2 Students will know and comprehend the fundamental concepts in non-cooperative game theory. They will apply non-cooperative game theory to analyze imperfect competition, moral hazard, adverse selection, market failures, and externalities and public goods. The students will be evaluated through tests, where they will solve relevant problems by employing game theoretic tools.	2
FIN7045	Portfolio Management Course: FIN7045 Credit: 2 This course presents the mainstream and alternate view of portfolio management using research papers, articles, and materials from academics and the markets. Many of the concepts covered are covered in the body of knowledge leading to the CFA designation.	2
IS7012	Web Development with .Net Course: IS7012 Credit: 2 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 front-end (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.	2
IS7034	Data Warehousing and Business Intelligence Course: IS7034 Credit: 2 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 business 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.	2
IS8034	Big Data Integration Course: IS8034 Credit: 2 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.	2
IS8070	Special Topics in IS Course: IS8070 Credit: 1 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.	1
MKTG7012	Marketing Research for Managers Course: MKTG7012 Credit: 4 Explores the role of marketing research in marketing management. Students do hands-on assignments to develop their understanding of methods for designing and implementing marketing research projects, including collecting, analyzing, and summarizing data pertinent to solving marketing problems. Developing experience in key aspects of marketing research is stressed.	4
OM7061	Managing Project Operations Course: OM7061 Credit: 2 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.	2
OM7083	Supply Chain Strategy and Analysis Course: OM7083 Credit: 2 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.	2
IS8036	Survey of Machine Learning and Artificial Intelligence Course: IS8036 Credit: 2 This course is a survey of Machine Learning (ML) and Artificial Intelligence (AI) from the Data Scientist’s perspective. It explores ML and AI topics, current and emerging technologies, and applications for students to gain understanding of the successful implementation of ML and AI to address key business and industry problems.	2

Students without a business undergrad degree may be required to take up to four of the following courses

Course	Title / Description	Credit
ACCT7000	Foundations in Accounting Course: ACCT7000 Credit: 2 This course educates students in the fundamentals of finance and accounting. The methods covered are used extensively throughout the MBA program. Topics include: the accounting process that results in the preparation of financial statements for external users, techniques for analyzing a basic set of financial statements, using accounting information to support management decisions, and using time value of money techniques to evaluate capital asset decisions. (MS Accounting students cannot earn credit by taking this course.) This course cannot be used as an elective course for Lindner College of Business Master's programs.	2
ECON7000	Foundations in Economics Course: ECON7000 Credit: 2 This course provides an introduction to the fundamentals of economics at the graduate level for students without previous economics coursework. Students will be exposed to the essentials of both microeconomics and macroeconomics. Microeconomics topics to be discussed include the supply and demand mechanism,how markets are affected by regulation and taxation, costs of production, and how market structure affects outcomes. Macroeconomic topics to be discussed include the fundamental measures of the aggregate economy, the sources of economic growth, explaining short-run fluctuations in economic activity, and how government policies can affect these fluctuations. A particular focus will be to understand how fundamental economic principles at both the micro and macro level can affect companies, investments, industries, and national economies. This course may not be used as an elective course for Lindner College of Business Master's programs.	2
FIN7000	Foundations in Finance Course: FIN7000 Credit: 1 Upon completion of this course, students should be able to: 1. Apply concepts and perform Time Value of Money calculations 2. Understand differences in interest rates (due to differences in risk, horizon, and compounding) 3. Use present value calculations to solve bond pricing and risk applications 4. Use present value calculations to solve stock valuation applications This course cannot be used as an elective course for Lindner College of Business master's programs.	1
MKTG7000	Marketing Foundations Course: MKTG7000 Credit: 1 The purpose of this course is to provide students with a foundation in Marketing. Concepts such as segmentation, targeting, positioning, customer and market analysis, and basic marketing planning will be introduced. This course cannot be used as an elective for Linder College of Business master's students.	1
OM7011	Management of Operations Course: OM7011 Credit: 2 Introduces basic operations principles through case studies and explores major operations problems. Areas of concentration are decisions and activities involving product and process design, the use and control of resources, scheduling and quality management, supply chain management, and project management.	2
MGMT7000	Organizations Course: MGMT7000 Credit: 2 The purpose of this course is to provide students with a foundation in the study of Organizations (Management) in preparation for the MBA or MS program. The goal is to provide students with an introduction to the study of organizations (strategy, structure, design, and context) to help students navigate through the advanced graduate course work and to become a more effective manager. This entails understanding how organizations work as well as developing requisite personal skills in problem analysis and writing. This course cannot be used for an elective course for Lindner College of Business master's programs.	2

FAQs

What is the career outlook for the Master’s in Business Analytics
Your master’s in business analytics prepares you to lead business decisions in a number of roles with titles such as:
- Data Scientist
- Optimization Analyst
- Consultant
- Decision Scientist
- Project Manager
- Market Research Analyst
According to salary.com the salary range for a graduate with their Master’s in Business Analytics is $123,000 to $130,000.
What type of hands on experience will I get from the classes for my MS in BA?

Students work on real-world projects and learn skills in data visualization, statistical modeling, data mining, optimization, and simulation in order to proficiently analyze large datasets and to generate actionable insights.
What does a recent entering class profile look like for a MS in BA?

MS Business Analytics class of 2019:
Total students: 108
Total applications received: 505
Number of semesters: Two to three
Average undergraduate GPA: 3.53
Average GMAT score: 675
Average GRE score: 319
Average first year salary: $92,423 (2018-2019)
Employed within three months of graduation: 96% (2018-2019)
Average TOEFL score: 106
PhDs: 3
Female students: 35%
Average age: 26
Average years of work experience: 4
International students: 84%, including students from China, India, Pakistan, and Morocco.