LECTURE | Prof. Coggeshall: Do Business Analysis as a Data Scientist: From Market and Consumer Aspects
Topic: Do Business Analysis as a Data Scientist: From Market and Consumer Aspects
Date: March 7, 2019 - Tuesday
Venue: Room 102, Building 45
Speaker: Dr. Stephen Coggeshall
Dr. Stephen Coggeshall is the retired Chief analytics and science officer at ID Analytics, an identity fraud protection company owned by LifeLock and Symantec. He was the founding CTO of ID Analytics where he built the analytics team and helped design the technical solution approach. Prior to ID Analytics Coggeshall worked for 11 years as a researcher in nuclear fusion at the Los Alamos National Laboratory. In addition to ID Analytics, Dr. Coggeshall also cofounded the analytics consulting companies CASA (acquired by HNC Software/FICO) and Los Alamos Computational Group (acquired by Morgan Stanley). His expertise is in forming and managing teams of data scientists to attack complex business problems using advanced algorithms on big data. He has deep expertise in consumer behavior modeling, optimization, forecasting and financial engineering, and spent the past 15 years focusing on identity fraud dynamics.
Dr. Coggeshall holds undergraduate degrees in math, music and physics. He has a master’s in music and a master’s and Ph.D. in nuclear engineering from the University of Illinois. He currently is a Professor at USC and UCSD teaching classes on Fraud Analytics and Business Analytics.
This presentation will cover a variety of topics that describe working as a data scientist in industry. First is an overview of what is data science with a brief description of some example applications in industry. Next is a discussion of the necessary skills and desired characteristics for a successful data scientist. The presentation then covers how one can enter this field, either as new student graduate or an experienced technologist from another discipline. Lastly is a discussion of the U.S. interview process and a description of a typical day-to-day environment for a data scientist working in industry.