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Scenario - Data Analysis

  • Download the H1B data for years from 2016 to 2019 from the link below

    https://www.dol.gov/agencies/eta/foreign-labor/performance:

    • Download the data files for the years 2016 to 2019 in a format like CSV.

  • Ingest the records into a Postgres database

    • Ensure you have PostgreSQL installed and running.
    • Create a new PostgreSQL database and a table to store the H1B data.
    • Write Python code to read the downloaded CSV files and insert the data into the PostgreSQL database.

  • ​Create a Django App-Based API:

    • Create a Django project and app for your API.
    • Define models in Django to represent the data in your PostgreSQL database.
    • Create API endpoints for the following functionalities:
      • Number of results: Count the total number of records in the database.
      • Mean salary: Calculate the mean (average) salary of H1B applicants.
      • Median salary: Calculate the median salary of H1B applicants.
      • 25% percentile salary: Calculate the 25th percentile salary.
      • 75% percentile salary: Calculate the 75th percentile salary.

  • Data Analysis :

    • Write Python code within your Django app to perform the required data analyses without using pandas or numpy.
    • You'll need to calculate the statistical measures using SQL queries or custom Python functions.

  • Performance Measurement:

    • Implement performance measurement within your Django app.
    • Use tools like Django Debug Toolbar or custom profiling to measure API response times and query performance.
    • Optimize your code for better performance, if needed.