Data scraped to be used to analyze the impact of a teacher subsidy program on student performance.
Python scripts using web scraping techniques, including packages such as Selenium
and bs4
, to automate data extraction, transformation, and cleaning of Saudi exam data for all private schools in the country. Each jupyter notebook corresponds to a unique combination of the following features:
- Type of Exam (Capabilities or Achievement)
- School Type (Boy or Girl)
- Exam Specialization (Scientific or Theoretical)
- School Type (Private, in this case)
The 'Cleaning_Appending' notebook brings all datasets together and adds relevant columns. The resulting dataset is named 'exam_scores_complete.csv'
and can be found in the data folder along with the individually scraped datasets.
Data from the National Center for Assessment: https://www.qiyas.sa/en/Pages/default.aspx