This project aims at collecting and analyzing interview questions from Glassdoor. The project is currently in its first phase, focused on data-oriented roles.
- Data Science (Main focus)
- Data Analysis
- Data Engineering
All data science interviews from FAANG are scraped and saved into CSV files. (Old)
Now we have 9000+ data-oriented from FAANG and non-FAANG companies so our data is more diverse.
NEW: A new deep learning model is under development to tag the scrapped questions. Currently, there are 2000+ Tagged questions on the dataset. The new deep learning model is being developed in this repo InterviewQuestionTagging
- Python 3.10.1
- Selenium
- Beautiful Soup
- Plotly (for Visualization)
- Jupyter notebook
- Go to the company you want to scrape its interview
- Filter the job interview
- Get the paged link (e.g. go to page 2, then change the IP2 in the link to IP1)
- Plug the link into the
config.py - Fill the rest of the data into the
config.py - Run the scraper script
$ Python scraper.py - Wait for it until it finishes scrapping
- Run the
data_cleaner.ipynb - Get your final CSV file it will be named
items_$company_name.csv - Enjoy analyzing your data (this repo will contain data analysis in the future)
The project is still in scarping phase, so far, 9000+ data-oriented interviews. There will be data analysis scripts and insights for the collected data.
If you have any questions or need anything relevant to the project, feel free to email me.