Master of Science in Business Analytics
Program Overview
Master of Science in Business Analytics
The Master of Science (MS) in Business Analytics degree program at the University of Cincinnati’s Carl H. Lindner College of Business is consistently ranked among the top data science programs in the country. Graduates of the program are trained in the most in-demand data analytics and business intelligence skills that place them with successful and high-profile employers in various industries.
Program Outline
The program is designed to develop advanced skills and expertise in many areas of the field, including descriptive, predictive, and prescriptive analytics. Students have the option to complete their coursework on a full-time basis, in about nine months or one year, or on a part-time basis, with flexible course times in the late afternoon, evening, and on Saturdays. Financial aid is available in the form of scholarships and assistantships and is awarded on a competitive basis.
FAQs
- 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.
Top-Ranked and STEM-Designated
In 2022, for the fourth consecutive time, Predictive Analytics Today ranked UC Lindner as their No. 1 MS in Data Science school in the country. The business analytics master’s program is recognized as the No. 3 program in the country.
Additionally, as a Science, Technology Engineering, and Mathematics (STEM)-designated master’s program, the MS in Business Analytics is an ideal option for international applicants. Enriching the diverse student population and the greater community of the University of Cincinnati, international students who apply to the MS in Business Analytics can extend their training in the U.S. by 24 months after graduation.
Why Earn a Master of Science in Business Analytics at Lindner?
With national and global recognition, rigorous coursework, and unrivaled networking opportunities, UC Lindner’s MS in Business Analytics program remains flexible and customizable to personal and professional goals.
Career Outcomes
Earning a master’s degree in business analytics is an increasingly popular way to put oneself ahead of the competition in the job market. As businesses realize the potential of data, they demand skilled scientists and analysts who can transform their vast amounts of structured and unstructured data into insights that impact growth.
Not only does Lindner’s program enhance professional skills that ensure graduates can help businesses make successful decisions, but it also encourages those without prior experience to gain demonstrable skills through internships and professional development opportunities.
Backed by Lindner’s career-driven student resources, services, and community partnerships, graduates of the MS in Business Analytics earn an average starting salary of $99,874 and have found employment with top companies, including:
- Disney
- Procter & Gamble
- Amazon
- Apple Inc.
Flexible Full-Time and Part-Time Options
Students in the MS in Business Analytics program can pursue the degree full-time, gaining applied experience to boost job prospects in just two semesters. Through evening courses and with the option to attend part-time, anyone can begin to further their career without drastically altering their personal or professional life.
Experiential Learning and Networking Opportunities
The Lindner College of Business continues the University of Cincinnati’s dedication to tangible applications of knowledge.
The University of Cincinnati Center for Business Analytics offers unique opportunities for students to partner with industry-leading organizations through course projects and assistantships. Companies including Procter & Gamble, GE Aviation, and Nestlé turn to the center to solve their real-world, data-driven business problems with the help of students from the MS in Business Analytics program.
Corporate partners and major employers such as Tata Consultancy Services, Great American Insurance, PWC, GE Digital, Teradata, DMI, and Fifth Third Bank routinely recruit students in the MS in Business Analytics program.
In addition to recruiting visits, top companies regularly lead seminars and attend networking events in pursuit of forging relationships with technical talent and future hires. Students can find internships and post-graduation job placement through UC’s connections with leaders in the industry.
Add a Certificate to Your Degree
With just a few informed course selections, MS in Business Analytics students can attach a data science or AI in business certificate to their degree.
Lindner’s data science graduate certificate program offers students the ability to build on their Master’s in Business Analytics with additional training in programming languages, big data integration, and more. The AI in business certificate prepares students to leverage AI platforms to solve business problems, preparing students for job placements in a rapidly growing talent field.
Business Analytics Program Requirements and Outcomes
In the MS in Business Analytics program, students gain the skills to analyze data sets of any size, communicate insights, and implement practical solutions. In addition to coursework taught by experienced faculty, students complete hands-on projects with corporate partners from the community. Through applications inside and outside of the classroom, students learn:
- Principles and practices of data visualization
- Data wrangling and optimization
- High-level simulation and modeling techniques
- Data mining and machine learning methods
The degree requirements for the MS in Business Analytics range from 33 to 41 credits, depending on prior education and experience. Requirements include:
- Up to 4 Business Foundations courses
- 25 credit hours in core business analytics courses
- 8 credit hours in electives, 4 from business analytics courses
- A capstone project or internship experience
Sample Class Schedules
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.
Master’s in Business Analytics Courses
Courses in the Master’s in Business Analytics program include:
- Statistical Computing
- Data Mining
- Data Management
- Simulation and Optimization
- Probability Models
- Data Visualization
The business analytics capstone course consists of either writing an essay based on a research question proposed or performing a case analysis or independent project with new and innovative findings.
Curriculum
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.
- BANA 7025: Data Wrangling This course provides an intensive, hands-on introduction to data management and data manipulation. Students 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.
- 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, 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.
- 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.
- 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.
- 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.
- 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.
- 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.
BANA Electives
- BANA 6043: Statistical Computing This is a course on the use of computer tools for data management and analysis.
- BANA 7048: Multivariate Statistical Methods This is a course in the analysis of multivariate data with emphasis on appropriate choice of estimation and testing methods.
- 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.
- 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.
- BANA 7095: Graduate Case Studies in Business Analytics Real organizational problems or challenges will be presented to students by client companies.
- 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.
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.
- 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#.
- IS 7034: Data Warehousing and Business Intelligence This course is designed for the comprehensive learning of data warehousing technology for business intelligence.
- IS 7065: Generative AI for Business This course examines the technology underlying modern generative artificial intelligence / machine learning models from a business perspective.
- 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.
- 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.
- 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.
- MKT 7012: Marketing Research for Managers Explores the role of marketing research in marketing management.
- OM 7061: Managing Project Operations This course covers detailed issues related to managing product development and projects in organizations.
- OM 7083: Supply Chain Strategy and Analysis Presents an overview of issues relating to the design and operation of an organization's supply chain.
Capstone
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.
- 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.
- 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.
Examples of past Business Analytics capstone projects are available for review.