Thursday, May 30, 2019
1 p.m., 112
Motassem Al-TaraziPh.D. Graduate , Iowa State University
One of the most common problem that a data scientist faces in data cleaning/exploration analysis is the handling of missing (incomplete) data. In general, there is no one good way to deal with missing data. In statistics, an outlier is data point that is distant from other data points. Removing or correcting outliers is one of the difficult decisions during data analysis. In this talk, we will demonstrate different methods for dealing with missing data and how to detect outliers and when to remove or correct them.
Motassem Al-Tarazi received his B.S. and M.S. in computer information systems and computer science, respectively, from Jordan University of Science and Technology, Jordan, and his Ph.D. in computer science from Iowa State University. After obtaining his M.S., he worked as an instructor at Jerash University, Jordan for more than two years teaching wide range of computer science courses. During his Ph.D. study, he worked as a graduate teaching assistant for more than five years.