ASA Connect

rent D. Buskirk, Ph.D.

Old Dominion University

This is a two-day, virtual event from 1:00 pm - 5:00 pm on August 14 and 15

Registration Link 

Description:

The amount of data generated as a by-product in society is growing fast including data from satellites, sensors, transactions, social media, smartphones, and even thermostats, just to name a few.  Such data are often referred to as "big data" and can be leveraged to create value in different areas such as health, crime prevention, commerce and fraud detection, among others.  This course offers a broad overview of big data to allow participants to understand the need for alternate methods to analyze and visualize such data and introduces machine learning framework.  The course will discuss the difference between inference and prediction within the statistical machine learning paradigm as well as the difference between supervised and unsupervised machine learning methods and close with an intuitive, accessible yet rigorous, discussion of four of the most common machine learning methods that every analyst should understand in the era of big data including k-means clustering, k nearest neighbors, tree-based methods and random forest models using examples in R.  Time permitting, we will highlight the Rattle package in R that provides an intuitive and accessible graphic user interface for reproducible specification of a broad assortment of machine learning models within the R environment.

About the instructor:

Trent D. Buskirk, Ph.D., is a Professor and Data Science Fellow in the New School of Data Science at Old Dominion University.  Prior to this appointment, Trent was the Novak Family Distinguished Professor of Data Science and Chair of the Applied Statistics and Operations Research Department at Bowling Green State University.  Dr. Buskirk is a Fellow of the American Statistical Association and his research interests include big data quality, recruitment methods through social media, the use of big data and machine learning methods for health, social and survey science design and analysis, mobile and smartphone survey designs and in methods for calibrating and weighting nonprobability samples and fairness in AI models and interpretable ML methods.