Climate Case Study Through Python

Perform risk analysis of climate change on humans by measuring air quality metrics for different locations in the United States.

Learn the following Python Concepts:

• How to get N rows of data in Python
• How to find column types in Python
• How to find missing data in Python through Numpy
• How to find missing data in Python through Pandas
• How to use Aggregate Functions in Python
  • Count, Count Distinct, Sum, Maximum, Minimum etc.
• How to Group data and aggregate in Python
• How to combine Filtering, Grouping and Sorting in Python
• How to use AND and OR multiple filtering conditions in Python
• How to create variables based on Aggregate Statistics in Python
• How to use variables to filter data in Python
• How to use Mathematical Functions on Aggregate Values in Python
• How to convert an aggregate column to percentage in Python

Learn the following Concepts about Climate Data Science:

• Understand the various climate air quality metrics
• How to analyze the at risk population based on high Ozone levels
• How to analyze the at risk population based on high PM25 levels
• How to rank states based on maximum population exposed to bad air quality
• How to find the state-wise low income population
• How to analyze the low-income population's exposure to bad air quality
• How to find the best & worst blocks within each state based on air quality indicators
• How to calculate total population at risk to air toxic cancer
• How to calculate low income population at risk to air toxic cancer