This repository contains a data scraping project focused on extracting job listings related to data-related jobs from JobStreet. The data was collected on 30 September 2024 using the keyword "Data".
The aim of this project is to gather job listings data with the keyword "Data" from JobStreet, and process it for further analysis or insights into the current job market.
The dataset includes job titles, companies, job type, sallary, job description and other relevant fields related to data-related job opportunities. This can be used to analyze trends in the job market, the demand for specific data-related skills, and locations where these opportunities are concentrated.
- Job Title
- Company
- Job type
- Job category
- Job sub category
- Job id
- job desc
Scrape_Job_Street.ipynb: Jupyter Notebook for scraping the job listings and saving them into a structured dataset.data_crawling.csv: The dataset collected from the scraping process, including various data-related job listings.
- Clone this repository:
git clone https://github.com/nnatureall/Jobstreet_Scrape_Data_Related.git
- Install the required dependencies (see below).
- Open the Jupyter notebook
Scrape_Job_Street.ipynband run the cells to execute the scraping process or modify it for future use.
- Python 3.x
- Jupyter Notebook
- Required Python libraries:
requestsbeautifulsoup4pandas
Install the dependencies using:
pip install -r requirements.txt- Expand the scraping to include more job boards or add more filters for advanced keyword searches.
- Perform in-depth analysis of the data, such as salary predictions, popular skills, and location-based job demand.
This project is licensed under the MIT License - see the LICENSE file for details.