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#43 Election Forecasting and Polling

#43 Election Forecasting and Polling

FromDataFramed


#43 Election Forecasting and Polling

FromDataFramed

ratings:
Length:
65 minutes
Released:
Oct 8, 2018
Format:
Podcast episode

Description

Hugo speaks with Andrew Gelman about statistics, data science, polling, and election forecasting. Andy is a professor of statistics and political science and director of the Applied Statistics Center at Columbia University and this week we’ll be talking the ins and outs of general polling and election forecasting, the biggest challenges in gauging public opinion, the ever-present challenge of getting representative samples in order to model the world and the types of corrections statisticians can and do perform. "Chatting with Andy was an absolute delight and I cannot wait to share it with you!"-Hugo  Links from the show FROM THE INTERVIEWAndrew's Blog Andrew on Twitter We Need to Move Beyond Election-Focused Polling (Gelman and Rothschild, Slate)We Gave Four Good Pollsters the Same Raw Data. They Had Four Different Results (Cohn, The New York Times).19 things we learned from the 2016 election (Gelman and Azari, Science, 2017)The best books on How Americans Vote (Gelman, Five Books)The best books on Statistics (Gelman, Five Books)Andrew's Research FROM THE SEGMENTSStatistical Lesson of the Week (with Emily Robinson at ~13:30)The five Cs (Loukides, Mason, and Patil, O'Reilly)Data Science Best Practices (with Ben Skrainka~40:40)Oberkampf & Roy’s Verification and Validation in Scientific Computing provides a thorough yet very readable treatment A comprehensive framework for verification, validation, and uncertainty quantification in scientific computing (Roy and Oberkampf, Science Direct) Original music and sounds by The Sticks.
Released:
Oct 8, 2018
Format:
Podcast episode

Titles in the series (100)

Data science is one of the fastest growing industries and has been called the ‘Sexiest job of the 21st Century’. But what exactly is data science? In this podcast, brought to you by DataCamp, Hugo Bowne-Anderson approaches the question by exploring what problems data science can solve rather than defining what data science is. From automated medical diagnosis and self-driving cars to recommendation systems and climate change, come on a journey with experts from industry and academia to explore the industry that will change the course of the 21st century.