Essential Probability in Python: Permutations and Combinations
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Essential Probability in Python: Permutations and Combinations
Nov 6 ·6min read
Probability theory isn’t hard, well at least on a level needed to get started in data science. It might have been a while since your last exposure to the topic, and if you feel rusty, this article might just be a way to get back on track.
If you were to just start in data science, a quick Google search would uncover 4 main mathematical topics the whole area is based on:
- Linear Algebra
- Calculus
- Statistics
- Probability
Recently I’ve coveredLinear Algebra andCalculus, so feel free to read those articles too if you’re rusty on the topic. Today, however, I want to cover two crucial concepts from the probability theory: combinations and permutations .
Let’s begin with a basic definition of the probability itself:
Probability is a measure quantifying the likelihood that events will occur. Probability quantifies as a number between 0 and 1, where, roughly speaking, 0 indicates impossibility and 1 indicates certainty. The higher the probability of an event, the more likely it is that the event will occur.[1]
The whole field of probability is important because uncertainty and randomness occur in pretty much every aspect of your life , hence having a good knowledge of probability will help you to make more informed decisions, and also to make sense of uncertainties.
Before diving into permutations and combinations there is one important term that needs to be discussed — factorial .
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