Measuring the relationship between variables will be helpful for data analysts to understand the dynamics between variables—for example, an HR manager needs to understand the strength of the relationship between employee performance score and satisfaction score. Statistics offers two measures of covariance and correlation to understand the relationship between variables. Covariance measures the relationship between a pair of variables. It shows the degree of change in the variables—that is, how the change in one variable affects the other variable. Its value ranges from -infinity to + infinity. The problem with covariance is that it does not provide effective conclusions because it is not normalized. Let's find the relationship between the communication and quantitative skill score using covariance, as follows:
# Covariance between columns of dataframe
data.cov()
This results in the following...
