It is hard to do data science without some sort of understanding of probability and it’s mathematics. As with our treatment of statistics in article (Statistics for Data Science), we’ll wave our hands a lot and elide many of the technicalities.

It is hard to do data science without some sort of understanding of probability and it’s mathematics. As with our treatment of statistics in article (Statistics for Data Science), we’ll wave our hands a lot and elide many of the technicalities.

For our purposes you should think of probability as a way of quantifying the uncertainty associated with events chosen from a some universe of events. Rather than getting technical about what these terms mean, think of rolling a die. The universe consists of all possible outcomes. And any subset of these outcomes is an event; for example, “the die rolls a one” or “the die rolls an even number.”

Notationally, we write P E to mean “the probability of the event E.”

- We’ll use probability theory to build models.
- We’ll use probability theory to evaluate models.
- We’ll use probability theory all over the place.

One could, were one so inclined, get really deep into the philosophy of what probability theory means. (This is best done over beers.) We won’t be doing that.

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