(COURSE DELIVERED IN THE MORNING)
Massive collections of data are created by businesses, governments, and individuals as a by-product of their activity. Therefore, decision-makers and systems depend on intelligent technology to analyze data systematically to improve decision-making.
In this course, our focus is on the ability to understand and translate business challenges into data mining problems and on examining how data analysis technologies can be used to improve decision-making. Therefore, we will emphasize heavily on students obtaining hands-on experience in implementing a range of commonly used data mining techniques by using “R”, the widely used programming language, on business analytic problems.
We will study the fundamental principles and techniques of data mining, and we will examine real-world examples and cases to place data-mining techniques in context, to develop data-analytic thinking, and to illustrate that proper application is as much an art as it is a science.
Nedret Billor is professor of Statistics in the Department of Mathematics and Statistics at Auburn University, Alabama. She received her Ph.D. in Statistics (1992) from University of Sheffield, UK. Her primary interests include robust multivariate data analysis, robust functional data analysis and outlier detection.
Professor Billor is an Elected Member of ISI since 2012 and serves as the country’s representative of the ISI Committee on Women in Statistics (CWIS). Dr. Billor is the associate editor of Communications in Statistics in Theory and Methods and Simulation and Computation She has advised numerous Ph.D./M.Sc. students and served on numerous interdisciplinary graduate committees.
She was awarded numerous research and instruction grants grant activities.