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Description

Employ the technique of web scraping to gain comprehensive insights into the diverse Data Analyst job listings available on JobStreet, enabling you to explore a wide range of promising career prospects.

Skills Applied: Python

Libraries Used: csv, requests, BeautifulSoup

Implementation

• To begin, I examine the source code of the webpage I intend to scrape for relevant information.
• Next, I aim to fetch webpage content from a URL using the requests library. It then employs BeautifulSoup to parse the HTML and create a structured representation of the webpage. Specifically, it extracts all elements with the specified class names and saves them in the job_listings variable for later use.
• Subsequently, I would print the extracted information from each job listing as per my requirements.
• Lastly, I will store all the collected data in a CSV file.

1. Importing Libraries

• I start the project by importing the necessary libraries (csv, requests, BeautifulSoup)

2. Extract Raw HTML

• Next, I fetch the HTML content from a Jobstreet webpage for Data Analyst jobs in Singapore.
• It then extracts the job listings by finding specific elements with the provided class names.
• The length of the job_listings variable is obtained to determine the number of job postings on the page.

3. Scrape Information

• Subsequently, I used a For Loop to iterate through each job listing in the job_listings variable. For each listing, it extracts the following using specific HTML elements and class names:
  • Title
  • Company Name
  • Salary
  • Description
  • Date Posted
  • How long ago it was posted

4. Print Scraped Information

• I print out all the scraped information to make sure everything is scraped correctly.

5. Save data to CSV

• Finally, I store all the extracted data into a CSV file for convenient reference and potential future analysis.

Important Note:

This scraping is only for one page. For multiple pages, please refer to this code.

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