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Gain Knowledge & Expertise

Graduate Certificate in Business Analytics Curriculum

Develop Technical and Decision-Making Skills

Bridge the gap between data collection and data analysis, and learn to make more informed, strategic business decisions. In this four-course certificate program, you will build advanced quantitative skills and gain experience with various analytics applications and methods.

The curriculum is developed in partnership with industry experts, to ensure that what you learn at St. Thomas is up-to-date and highly applicable in the real world.

Complete the Graduate Certificate in Business Analytics entirely online or entirely on campus, or a mix of both. Complete the program at your own pace, taking one or two courses each semester, and finish in as few as 9 months.

Plan Out Your Program

Follow these sample plans to complete the Graduate Certificate in Business Analytics in 2 or 4 semesters.

Semester I (6 credits)

Statistical Methods for Decision-Making
Introduction to Business Analytics

Semester II (6 credits)

Data Narratives
Data Life Cycle for Analytics

Semester I (6 credits)

Statistical Methods for Decision-Making
Introduction to Business Analytics

Semester II (6 credits)

Data Narratives
Data Life Cycle for Analytics

Semester I (3 credits)

Statistical Methods for Decision-Making

Semester II (3 credits)

Introduction to Business Analytics

Semester III (3 credits)

Data Narratives

Semester IV (3 credits)

Data Life Cycle for Analytics

Semester I (3 credits)

Statistical Methods for Decision-Making

Semester II (3 credits)

Introduction to Business Analytics

Semester III (3 credits)

Data Narratives

Semester IV (3 credits)

Data Life Cycle for Analytics

Certificate Curriculum

12 Credits Total

Statistical Methods for Decision Making

(OPMT 600) - 3 credits

Gain a basic understanding of the role of statistics in the gathering of data, the creation of information and its use in decision-making. Learn methods for summarizing data, both numerically and graphically, and for drawing conclusions from sample data. Carry out statistical analyses using the computer and statistical software. The course focuses on how statistical methods can be placed on the design of statistical studies, collection of data, and the interpretation of results (rather than the details of computation). *online option offered

Data Preparation and Analysis

(SEIS 631) - 3 credits

This course provides a broad introduction to the subject of data analysis by introducing common techniques that are essential for analyzing and deriving meaningful information from datasets. In particular, the course will focus on relevant methods for performing data collection, representation, transformation, and data-driven decision making. The course will introduce students to Statistical Science including Probability Distribution, Sampling Distribution, Statistical Inference, and Significance Testing. Students will also develop proficiency in the widely used Python language which will be used throughout the course to reinforce the topics covered. Packages like NumPy and Pandas will be discussed at length for Data Cleaning, Data Wrangling: Joins, Combine, Data Reshape, Data Aggregation, Group Operation, and Time Series analysis.

Prerequisite: SEIS 601 or SEIS 603 (may be taken concurrently).


(BUAN 600) - 3 credits

This course teaches students how to perform data analysis using spreadsheet based methods to effectively and efficiently solve management problems. Students will learn how to effectively build, present and communicate advanced Excel spreadsheet models, forecasting models, optimization models and simulation models to drive managerial decision making. Students will also learn how to build interactive, data driven dashboards using Power BI to discover new insights and monitor key performance indicators.

Prerequisites: NONE


(BUAN 610) - 3 credits

This course will focus on developing the ability to understand the business needs for data insights, crafting those into an analytics problem statement, and developing a coherent and persuasive narrative of any data findings. Students will learn to create well-crafted data narratives and dashboards for business leaders while being able to translate insights into managerial decisions. Students will also be able to prepare raw data sets for their data narratives, executive summaries and technical memos. The Data Narratives course focuses on providing these fundamental data narrative and storytelling abilities while leveraging various tools to assist in the process.

Prerequisites: NONE


(BUAN 620) - 3 credits

This course covers the life cycle of data for analytics from the structure of relational and non-relational data stores, through the extraction, transformation, and loading (ETL) process into the analysis and presentation of data using data dashboards. Students will learn and practice acquiring, extracting, cleaning, and loading data from databases and other data stores. Students will learn to interpret and create data models, write and interpret the results of Structured Query Language (SQL), practice and apply industry ETL tools to solve business problems, and effectively communicate about data through the use of a dashboarding tool.

Prerequisites: NONE


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