Automate Data Extraction and Web Scraping using Python

In today’s competitive world everybody is looking for ways to innovate and make use of new technologies. Web scraping (also called web data extraction or data scraping) provides a solution for those who want to get access to structured web data in an automated fashion. Web scraping is useful if the public website you want to get data from doesn’t have an API, or it does but provides only limited access to the data.

Web scraping is the process of collecting structured web data in an automated fashion. It’s also called web data extraction. Some of the main use cases of web scraping include price monitoring, price intelligence, news monitoring, lead generation, and market research among many others.

In general, web data extraction is used by people and businesses who want to make use of the vast amount of publicly available web data to make smarter decisions.

If you’ve ever copied and pasted information from a website, you’ve performed the same function as any web scraper, only on a microscopic, manual scale. Unlike the mundane, mind-numbing process of manually extracting data, web scraping uses intelligent automation to retrieve hundreds, millions, or even billions of data points from the internet’s seemingly endless frontier.  In this course we are going to extract data using Python and a Python module called Beautiful Soup.