# Variance and Standard Deviation

In this module, you will learn how to quantify the spread of the dataset by calculating the variance and standard deviation.

Start- 2Now that you have learned the importance of describing the spread of a dataset, let’s figure out how to mathematically compute this number. How would you attempt to capture the spread of the data …
- 3We now have five different values that describe how far away each point is from the mean. That seems to be a good start in describing the spread of the data. But the whole point of calculating vari…
- 4We’re almost there! We have one small problem with our equation. Consider this very small dataset: [-5, 5] The mean of this dataset is 0, so when we find the difference between each point and the…
- 5Well done! You’ve calculated the variance of a data set. The full equation for the variance is as follows: \sigma^2 = \frac{\sum_{i=1}^{N}{(X_i -\mu)^2}}{N} Let’s dissect this equation a bit. * …

- 1When beginning to work with a dataset, one of the first pieces of information you might want to investigate is the spread — is the data close together or far apart? One of the tools in our st…
- 2Variance is a tricky statistic to use because its units are different from both the mean and the data itself. For example, the mean of our NBA dataset is 77.98 inches. Because of this, we can say s…
- 3There is a NumPy function dedicated to finding the standard deviation of a dataset — we can cut out the step of first finding the variance. The NumPy function std() takes a dataset as a param…
- 4Now that we’re able to compute the standard deviation of a dataset, what can we do with it? Now that our units match, our measure of spread is easier to interpret. By finding the number of standa…

## What you'll create

Portfolio projects that showcase your new skills

## How you'll master it

Stress-test your knowledge with quizzes that help commit syntax to memory