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Myths of Data Science: Things you Should and Should Not Believe

Our most important data science tools are our theories and methods. In this talk, we will go back to fundamentals and look closely at some usually unexamined assumptions about statistics and machine learning.We will look at "myths" that arise in three common data scientist tasks: predictive modeling, analyzing the reliability or validity of results, and running controlled experiments (A/B testing). We will "debunk" these myths and offer some potential fixes to issues that can arise, all in a (hopefully) entertaining way. 

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