Program Overview

Certificate of Graduate Study in Topological Data Analysis

Overview

The Certificate of Graduate Study in Topological Data Analysis is a fully online program that equips students with a mathematical background and new topological data analysis techniques to extract valuable information from large volumes of multi-dimensional data.

Program of Study

The program starts with a background in linear algebra, graph theory, and homological algebra. After this preparation, students will learn about persistent homology, the first major tool in TDA. This includes the basics of discrete Morse theory necessary for implementations of persistent homology in the "big data" setting. The last major topic in the program is the Mapper algorithm.

Required Courses (9 credits)

• Linear Algebra for Applications
• Topological Data Analysis I
• Topological Data Analysis II

Students can complete this certificate program as a standalone credential or gain the equivalence of 9 elective credits toward the master's program in data science.

Career Outcomes

This program prepares students for the growing needs of academia and industry to manage big data sets using the most contemporary mathematical tools. The topological data analysis program provides an excellent foundation for further graduate studies in areas including computer science, biology, psychology, geography, atmospheric science, chemistry, and sociology.

Potential Job Titles

• Data Analyst
• Data Engineer
• Data Research Analyst

Admissions Requirements

Deadlines

• Fall: Rolling
• Spring: Rolling
• Summer: Rolling

Required Application Materials

• Transcripts from all schools attended
• Statement of goals

The statement is generally one to two pages discussing what the applicant has to offer the program and what they wish to get out of the program. It should include a brief description of the applicant's field of interest, related background, desired area of study, and research emphasis or career goals.

Special Notes

Please note that this program is not eligible for federal financial aid.

Student Learning Objectives

• Understand the fundamental principles of Topological Data Analysis (TDA) at the level common among PhD graduates in the field
• Ability to critically choose the framework and appropriate software implementation of the TDA methods used in a specific project
• Gain essential experience in a variety of settings where TDA has become a go-to tool in modern data science workflows
• Have expertise in TDA methods available for use in artificial intelligence projects that use machine learning and statistical analysis