Graduate Certificate in Business Analytics
Develop your skills in making informed, data-based business decisions
In today’s fast-paced, competitive and dynamic business environment, organizations make decisions that have potential impact not only on their operations, but on the extended enterprise, including suppliers and end consumers. With the explosion of data collection tools and methods, business leaders are faced with a fundamental problem – how do you analyze and use the wealth of data to make smart business decisions that positively impact your organization?
Our certificate program will help you bridge the gap between data collection and data analysis. In just two semesters, you will gain and hone critical skills in statistics, data modeling, data analysis and industry analytics.
Part-time graduate certificate program
- Evening courses designed for working professionals
- Complete at your own pace taking one or more courses per term
Graduate Certificate in Business Analytics
The Graduate Certificate in Business Analytics provides:
- An immersive curriculum balancing technical skills and business application
- Embedded action-based learning projects
- A program developed in partnership with industry experts and employers
- Option to apply graduate certificate credits to a degree program, either the MS in Business Analytics or St. Thomas Flex MBA
Curriculum and Course Descriptions
12 credits total
Required Courses (9 credits)
Statistical Methods for Decision Making (OPMT 600)
This course examines statistical and analytical methods including sampling concepts, regression analysis, hypothesis testing, forecasting, quality control, simulation and database management.
Foundations of Data Analysis (R-Environment) (SEIS 631)
This course provides a broad introduction to the subject of data analysis, focusing on relevant methods for performing data collection, representation, transformation and data-driven decision making. You will also develop proficiency in the widely-used R language which will be used throughout the course to reinforce the topics covered.
The primary goal of this course is to develop a better understanding of data analysis for business research, emphasizing the interpretation of data rather than calculations. Building upon the groundwork provided by the core MBA statistics course (OPMT 600), topics will include techniques commonly used in business such as logistic regression, two-way analysis of variance and statistics for scale development. These skills are relevant for students involved in marketing research and survey development. Course deliverables will include a project, potentially based on a situation or analysis from students' workplaces or industries. Prerequisites: OPMT 600 or SEIS 631.
Spreadsheet Modeling and Data Visualization (OPMT 621)
This course is focused on developing the quantitative, analytical skills needed to gain insight into the resolution of practical business problems. Learn to analyze and solve management problems using spreadsheet-based methods. Specific methods of clarifying objectives, developing alternatives, addressing trade-offs and conducting a defensible quantitative analysis will be presented. Topics include spreadsheet modeling, linear programming, transportation modeling, decision analysis, project management and simulation. You will also be introduced to building decision support models using Visual Basic Applications (VBA).
Data Analytics and Visualization (SEIS 632)
The course provides an introduction to concepts and techniques used in the field of data analytics and visualization. Insights discovered from the data are then communicated using data visualization. Topics covered in the course include predictive analytics, pattern discovery and best practices for creating effective data visualizations. Through practical application of the above topics, you will also develop proficiency in using analytics tools.
Elective Courses (3 credits)
*course has prerequisite(s); may be waived for those with appropriate academic background. Contact program advisor for more information.
Students can take both analytics tool courses (OPMT 621 or SEIS 632), and one will apply as their elective. This is an ideal option for business analytics certificate-only students (those not concurrently completing a degree program), who may not have required prerequisites for other advanced elective options.
Marketing decisions are increasingly data driven. In this course, students will learn how to analyze marketing data to inform effective decision making. Students will learn how they can develop a deeper and more fully informed understanding of current and emerging customer needs using a broad range of marketing analytic techniques. Students will work hands-on with marketing data as they learn how to master the tools necessary to develop useful customer insights that can guide marketing decisions. Prerequisites: OPMT 600 and MKTG 600 or 625.
This course will discuss processes in health care analytics, including data acquisition, storage, retrieval, management and analysis of health care data in heterogeneous formats (i.e. numeric health records, medical text and medical images). Major topics include: (1) analyzing patient records and identifying frequent medical sequences for treatment and prevention; (2) evaluating medical text and generating aggregated summary based on hierarchical medical concepts; (3) retrieving information from different types of medical images; (4) building clinic decision support systems to detect possible medical mistakes; and (5) comparing brain connectivity graphs from patients with different neurological conditions. Amazon Cloud will be used to analyze multi-million records of numeric and text data. Prerequisites: SEIS 632 or SEIS 734 or SEIS 763, and requires knowledge of SQL.
To overcome data overloading problems, this course will discuss how to apply big data analytics to extract useful patterns from huge datasets and generate visual summary of data. This course will also demonstrate mining and analyzing big data on Amazon Cloud. Key topics of this course include: (1) mining association rule and market basket analysis, (2) classification and predictive analysis, (3) clustering and market segmentation, and (4) combining numeric analysis with text sentiment analysis. Real-world data will be used to illustrate the data mining concepts and their possible pitfalls. Prerequisite: SEIS 630