Tickle College of Engineering
The University of Tennessee, Knoxville, offers an online Data-Driven Decision-Making Graduate Certificate for working professionals and current students seeking to expand their analytics skills, including tools to extract information from data and how to use that information to make decision.
Cost Per Credit Hour*
*Cost per credit hour is an estimate based on maintenance and university fees. Some programs may have additional course fees. Please contact your department for additional information on any related fees, and visit Tuition and Fees in Detail at One Stop.
Gain the Skills Needed to Utilize Data for Decision-Making
Data analytics involves the development and application of statistical and quantitative analysis methods and the construction of explanatory and predictive models to drive the decision-making process. Students completing the certificate will have a basic foundation of critical tools to extract useful information from big data, as well as for modeling, simulation, optimization and decision analysis in order to support efficient data-driven decision making. The program is designed for working professionals as well as full-time students who want to broaden their analytics capabilities.
Asynchronous & Synchronous
The two core classes of this certificate focus on both the mathematical fundamentals of the state-of-the-art data analytics techniques as well as how to best utilize them. We discuss such questions as the following:
• How do these techniques work?
• Are they guaranteed to always work?
• How much can you trust their outcome?
Understanding how these techniques work, as well as their limitations, is necessary to maximize their ability to improve decision-making. For a variety of application areas, we model how to best do this. After the culmination of the two required courses, students will be able to take elective classes in their chosen department, allowing them to apply data analytics techniques in their home domain.
The graduate certificate in data-driven decision-making requires the following core courses:
Applied Data Science
An introduction to applied data science including machine learning and data mining tools. Topics include supervised and unsupervised algorithms, techniques for improving model performance, evaluation techniques and software packages for implementation. Emphasis will be put on real-world applications in various domains including healthcare, transportation systems, etc.
Optimization for Big Data
An introduction to modern optimization theories and algorithms for big data applications, including the structure of large-scale optimization problems, algorithms for smooth and non-smooth problems, and computational efficacy of algorithms