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Python for Data Science

Web Scraping Using Python

2 Modules
1 Hour


Data scraping refers to the process of extracting data from websites or other sources using automated tools or scripts. The scraped data can then be analyzed and used for various purposes, including data wrangling. Data scraping can play a critical role in the data wrangling process by providing a means to collect large amounts of data quickly and efficiently. One example of when data scraping is useful is in sentiment analysis, where web scraping tools can be used to collect large amounts of social media data to analyze public opinion and sentiment about a particular topic, brand, or product. This information can help companies understand their customers’ preferences and attitudes, identify areas for improvement, and make data-driven decisions. In this case, data scraping can save significant time and effort compared to manual data collection, while providing more comprehensive and accurate data for analysis.

In this course, you will:

  • Extract data from websites using scraping tools such as BeautifulSoup
  • Parse raw data extracted from online sources and extract useful information
  • Interact with REST APIs using a Python script

Course Contents

1. Rest API’s

  • What are REST APIs?
  • Why are they important?
  • Making HTTP requests
  • Parsing HTTP response
  • Authenticating HTTP requests
  • Rate limiting requests
  • Knowledge check

2. Web Scraping

  • What is web scraping?
  • Why is it important?
  • Fetching and parsing HTML pages
  • Searching for HTML tags
  • Knowledge check