University of Colorado Denver - Tuesday October 22, 2013 @ 6:15pm MST
NOTE: For folks unable to attend in person register and we will email you a livestream link 2 hours prior to event.
Large auditorium (170 person capacity) with 20' screen.
Location: CU Denver - North Classroom #1539 - 1200 Larimer Street
Denver, CO[masked] - Map: http://bit.ly/Tyznzg
6:15 - 6:30 Schmooze - Old Chicago Pizza will be served.
6:30 - 8:00 R & Hadoop by Joseph Rickert
8:00 - 9:00 Panel Discussion: "Resolved: Traditional Statistics is Dead"
9:00 - 10:00 Network at Old Chicago at 14th and Market.
R, Statistics and Hadoop - Abstract
There is a considerable amount of hype surrounding and supporting the notion of "Big Data". But, putting the hype aside, large data sets do pose practical and theoretical challenges. In this talk Joseph will describe how the experience of "doing statistics" with R does change as one moves from small data to Hadoop scale data sets, and show examples of using R and Hadoop with both RHadoop and Revolution R Enterprise 7.0.
Joseph Rickert is a Data Scientist and Community Manager at Revolution Analytics with a passion for analyzing data and teaching people about R. He is a regular contributor to the Revolutions blog and an organizer of the Bay Area R Users Group. Joseph has worked for a number of Silicon Valley start-ups and has experience building statistical models in industries as diverse as local area networks and healthcare. Joseph holds graduate degrees in both the Humanities and Statistics. He taught statistics briefly at SJSU.
"Resolved: Traditional Statistics is Dead" - Abstract
"If your experiment needs statistics, you ought to have done a better experiment." Ernest Rutherford (Nobel Prize Winner)
A panel discussion about traditional statistics, large data sets and new data science methods.
As data science evolves into a separate and distinct scientific and business discipline, there is talk about the death of traditional statistics. It is true that today's large data sets are unlike the ones we analyzed in graduate statistics classes. It is also true that big data sets have different properties than small data sets. It is very true that one can lie with statistics and present an illusion of reality. It is certainly true that many professional statisticians lack business acumen and communication skills.
Yet prudent use of statistics can be very useful for finding meaning in messy, large data sets. Misuse or intentional abuse of statistics can mislead and present a false view of reality. At best, statistics helps us simplify complexity to make better decisions faster. At worst, statistics can define and measure the wrong things, create dangerous illusions of reality and cause us to make bad decisions.
Nancy Abramson, Theodore Van Rooy, Mark Labovitz, Michael Malak and others.