NSDC Data Science Flashcards – Probability #2 – Probability Density Functions

This NSDC Data Science Flashcards series will teach you about probability, including random variables, probability density functions, cumulative distribution functions, and expected values. This installment of the NSDC Data Science Flashcards series was created and recorded by Stephanie Guo. You can find these videos on the NEBDHub Youtube channel.

Hello everyone, my name is Stephanie and I am the Program Manager for the National Student Data Corps. Welcome to the NSDC Data Science Flashcard Video Series. This series will break down data science topics in simple terms that you can leverage throughout your data science journey. Today, we’ll be talking about probability.

Taking our understanding of random variables a step further, let’s dive into Probability Density Functions, commonly known as PDFs.

For discrete random variables, we have a Probability Mass Function (PMF). It assigns a probability to each possible value of the random variable.

Continuous random variables use the Probability Density Function. Unlike the PMF, the PDF doesn’t give probabilities directly. Instead, it tells us the probability that a random variable falls within a particular range.

Please follow along with the rest of the NSDC Data Science Flashcard series to learn more about math and probability.