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Expand Up @@ -52,11 +52,20 @@ Other requirements may including ___normalizing___ data sets,
which generally means scaling the data to values between 0 and 1
(this enables certain types of numerical analysis).

There are several techniques used in data normalization and standardization, and three commonly applied methods include:
Min-Max Normalization: This technique rescales the data to a fixed range, usually between 0 and 1.
This method is useful when features have different scales but no significant outliers.

Z-score Standardization (Standard Scaling): This method transforms data to have a mean of 0 and a standard deviation of 1.
Robust Scaling: This method uses the median and interquartile range (IQR) to scale data, making it more robust to outliers

The end result may sometimes be referred to as ___tidy data___,
however it is important to remember that data cleaning is not
always a one-time task. The further use of any given dataset
may well highlight details that need further cleaning.



## Exploration

Lets start with our first dataset.
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