Offered Online Only
Overview
The post-bachelor’s Certificate for Advanced Study in Data Analysis provides both a practical and theoretical foundation for professionals who need to understand randomness and variability, its causes and consequences. Students will learn how to design efficient and effective experiments and observational studies in order to answer questions they find interesting. The program works students through important probability models and the underlying mathematics to understand random phenomena. Software is used to analyze data and make effective presentations of results to different audiences.
The program is designed for students who have a bachelor’s degree in any field. In addition, a background in mathematics including calculus I, II, III, and linear algebra/matrix methods is required.
Career Paths
Enrolled students are provided with the opportunity to expand their analytical and presentation skills in this dynamic and growing field, resulting in increased career advancement opportunities. Actuaries, electrical engineers, computer scientists and quality and system engineers all need strong foundations in probability and data analysis and would benefit professionally from this program.
Degree Requirements
The CAS in Data Analysis consists of 12 credit hours:
- MAT 505 Introduction to Probability
- MST 570 Design & Analysis of Experiments
- STA 510 Regression and Analysis of Variance
- MST 680 Reliability and Quality Assurance OR MAT 550 Time Series
Students are required to consult with a faculty member to develop an academic plan.
Special Program Notes:
- All graduate courses are 3 credit hours.
- All students must have a 3.0 GPA or higher to graduate.
Faculty
Dr. Andrea Dziubek, Assistant Professor
PhD, Berlin University of Technology , Germany
Modeling and simulation of problems in biomedical engineering, continuum mechanics, shell theory, structure preserving numerical methods and finite element methods.
Firas Khasawneh, Assistant Professor, Mechanical Engineering
BS, Jordan University of Science & Technology
MS, University of Missouri
PhD, Duke University
Dr. Edmond Rusjan, Associate Professor
PhD, Virginia Tech
Geometry and symmetry inspired mathematical models: Boltzmann equation, Lie groups and Lie algebras and Calabi-Yau spaces to solve problems in physics and engineering.
Dr. William Thistleton, Associate Professor
PhD, SUNY Stony Brook
Analysis, computational mathematics, data analysis, probability and statistics.
Dr. Zora Thomova, Professor
PhD, University of Montreal
Financial mathematics, fundamentals of derivative markets, continuous symmetries of differential and difference equations.