From the course: Data Science Foundations: Fundamentals
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Bayes' theorem
From the course: Data Science Foundations: Fundamentals
Bayes' theorem
- [Instructor] Performing is hard. You put in a lifetime of training, weeks of preparation for an event, and even when you've done the very best, you can never be completely certain that everything is going to work out exactly the way you wanted it to. There's a certain element of luck or probability associated with it. It's the same thing in data science. No matter how big your dataset, no matter how sophisticated your analysis and the resources available to your organization, there's still an inescapable element of probability. It's just how it works. And one of the best things you can do is to explicitly incorporate that uncertainty into your data science work to give you more meaningful and more reliable insights. This is Bayes' theorem, and this is one of the keys to incorporating that uncertainty. What Bayes' theorem does is it gives you the posterior, or after the data, probability of a hypothesis as a function of the likelihood of the data given the hypothesis, the prior…
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