Data Science Weekly – Issue 154: Predicting The Election; 10 Ways DS Projects can fail;

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Issue #154

Nov 3 2016

Editor Picks

  • Is Bayesian A/B Testing Immune to Peeking? Not Exactly
    Since I joined Stack Exchange as a Data Scientist in June, one of my first projects has been reconsidering the A/B testing system used to evaluate new features and changes to the site. Our current approach relies on computing a p-value to measure our confidence in a new feature. Unfortunately, this leads to a common pitfall in performing A/B testing, which is the habit of looking at a test while it’s running, then stopping the test as soon as the p-value reaches a particular threshold…
  • Ten Ways Your Data Project is Going to Fail
    Data science continues to generate excitement and yet real-world results can often disappoint business stakeholders. How can we mitigate risk and ensure results match expectations? Working as a technical data scientist at the interface between R&D and commercial operations has given me an insight into the traps that lie in our path. I present a personal view on the most common failure modes of data science projects…
  • Predicting the Presidential Election
    With the presidential election less than a week out, I thought it would be fun to make my own predictions about the race. There are plenty of blog and websites that forecast the election, but there aren’t that many that tell you how exactly their “secret models” work OR show you how to do it yourself. Well good news is that’s exactly what I’m going to do ;)…

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