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<!DOCTYPE html>
<html>
<head>
<title>What data should you use?</title>
<meta charset="utf-8">
<meta name="description" content="What data should you use?">
<meta name="author" content="Jeffrey Leek">
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<body style="opacity: 0">
<slides class="layout-widescreen">
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<slide class="title-slide segue nobackground">
<aside class="gdbar">
<img src="../../assets/img/bloomberg_shield.png">
</aside>
<hgroup class="auto-fadein">
<h1>What data should you use?</h1>
<h2></h2>
<p>Jeffrey Leek<br/>Johns Hopkins Bloomberg School of Public Health</p>
</hgroup>
</slide>
<!-- SLIDES -->
<slide class="" id="slide-1" style="background:;">
<hgroup>
<h2>A succcessful predictor</h2>
</hgroup>
<article>
<p><img class=center src=../../assets/img/08_PredictionAndMachineLearning/fivethirtyeight.png height=400></p>
<p><a href="fivethirtyeight.com">fivethirtyeight.com</a></p>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-2" style="background:;">
<hgroup>
<h2>Polling data</h2>
</hgroup>
<article>
<p><img class=center src=../../assets/img/08_PredictionAndMachineLearning/gallup.png height=400></p>
<p><a href="http://www.gallup.com/">http://www.gallup.com/</a></p>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-3" style="background:;">
<hgroup>
<h2>Weighting the data</h2>
</hgroup>
<article>
<p><img class=center src=../../assets/img/08_PredictionAndMachineLearning/538.png height=400></p>
<p><a href="http://www.fivethirtyeight.com/2010/06/pollster-ratings-v40-methodology.html">http://www.fivethirtyeight.com/2010/06/pollster-ratings-v40-methodology.html</a></p>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-4" style="background:;">
<hgroup>
<h2>Key idea</h2>
</hgroup>
<article>
<p><center>To predict X use data related to X</center></p>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-5" style="background:;">
<hgroup>
<h2>Key idea</h2>
</hgroup>
<article>
<p><center>To predict player performance use data about player performance</center></p>
<p><img class=center src=../../assets/img/08_PredictionAndMachineLearning/moneyball.jpg height=400></p>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-6" style="background:;">
<hgroup>
<h2>Key idea</h2>
</hgroup>
<article>
<p><center>To predict movie preferences use data about movie preferences</center></p>
<p><img class=center src=../../assets/img/08_PredictionAndMachineLearning/netflix.png height=400></p>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-7" style="background:;">
<hgroup>
<h2>Key idea</h2>
</hgroup>
<article>
<p><center>To predict hospitalizations use data about hospitalizations</center></p>
<p><img class=center src=../../assets/img/08_PredictionAndMachineLearning/heritage.png height=400></p>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-8" style="background:;">
<hgroup>
<h2>Not a hard rule</h2>
</hgroup>
<article>
<p><center>To predict flu outbreaks use Google searches</center></p>
<p><img class=center src=../../assets/img/08_PredictionAndMachineLearning/flutrends.png height=400></p>
<p><a href="http://www.google.org/flutrends/">http://www.google.org/flutrends/</a></p>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-9" style="background:;">
<hgroup>
<h2>Looser connection = harder prediction</h2>
</hgroup>
<article>
<p><img class=center src=../../assets/img/08_PredictionAndMachineLearning/oncotype.png height=300></p>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-10" style="background:;">
<hgroup>
<h2>Data properties matter</h2>
</hgroup>
<article>
<p><img class=center src=../../assets/img/08_PredictionAndMachineLearning/fluproblems.jpg height=400></p>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="" id="slide-11" style="background:;">
<hgroup>
<h2>Unrelated data is the most common mistake</h2>
</hgroup>
<article>
<p><img class=center src=../../assets/img/08_PredictionAndMachineLearning/choc.png height=400></p>
<p><a href="http://www.nejm.org/doi/full/10.1056/NEJMon1211064">http://www.nejm.org/doi/full/10.1056/NEJMon1211064</a></p>
</article>
<!-- Presenter Notes -->
</slide>
<slide class="backdrop"></slide>
</slides>
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