Now that we have a better understanding of how to work with SQL sourced data using Python and pandas, let's explore some fundamental statistics along with practical usage for data analysis. So far, we have focused on descriptive statistics versus predictive statistics. However, I recommend not proceeding with any data science predictive analytics without a firm understanding of descriptive analytics first.
Fundamental statistics
Descriptive analytics is based on what has already happened in the past by analyzing the digital footprint of data to gain insights, analyze trends, and identify patterns. Using SQL to read data from one or more tables supports this effort, which should include basic statistics and arithmetic. Having the data structured and conformed, which includes defined data types per column, makes this type of analysis easier once you understand some key concepts and commands.There are many statistical functions...
