Food Insecurity CurriculumModule 4 of 6
Data Quality and Socioeconomic Patterns
What this module is about
Two skills real analysts need constantly: catching bad data before it corrupts a result, and reading a relationship across three related socioeconomic columns at once.
What you'll achieve
You'll use Data_Quality_Flag and Data_Quality_Issue to find and correct records with intentional data-quality problems seeded into this dataset, then explore how Education_Level, Employment_Status, and Annual_Household_Income move together.
Exercises
- Find and correct the intentional data-quality issues in the dataset (start with
Data_Quality_Flag = 'Yes'and readData_Quality_Issuefor what's wrong with each flagged row). - Explore the relationship between
Education_Level,Employment_Status, andAnnual_Household_Income.
Why it matters
Every real dataset has errors; the difference between a junior and senior analyst is often just whether they went looking. This dataset seeds issues on purpose so you practice finding them before they reach a chart someone else relies on.
Suggested tools
Filtering/sorting on the flag columns; grouped averages or a pivot table for exercise 8.