Why Web Crawling is Critical in Every Data Science Coding Project?

Web crawling plays a crucial role in the data science ecosystem to discover and collect data

To your surprise every single person today is a data agent. How? Well, everything a person does nowadays generate data, therefore every individual is a data agent. According to reports, there are 4.66 billion active internet users globally that have created 2.5 quintillion data bytes daily. The Data Science ecosystem uses internet data to create different solutions that can solve business problems. Web crawling plays a crucial role in the data science ecosystem to discover and collect data that can be used in a data science coding project. Many organizations are depended on a web crawler to collect data about their customers, products, and more. A data science coding project is created by formulating the business problem to solve and then followed by the second stage of collecting the right data to solve that problem. At this point, you can use web crawlers to collect the internet data that you need for your data science coding project.

What is web crawling?

Web crawling is the process of indexing data on on-site pages by utilizing a program or automated script. These automated scripts or projects are known by various names, including web crawler, spider, spider bot, and frequently abbreviated to the crawler.

Web crawlers copy pages for processing by a search engine, which lists the downloaded pages so clients can look through them more productively. The objective of a crawler is to learn what webpages are about. This empowers clients to recover any data on at least one page when it’s required.

Why is web crawling important?

Thanks to the digital revolution, the total amount of data on the web has increased. In 2013, IBM stated that 90% of the world’s data had been created in the previous 2 years alone, and we continue to double the rate of data production every 2 years. Yet, almost 90% of data is unstructured, and web crawling is crucial to index all these unstructured data for search engines to provide relevant results.

According to Google data, interest in the web crawler topic has decreased since 2004. Yet, at the same time period, interest in web scraping has outpaced the interest in web crawling. Various interpretations can be made, some are:

• Increasing interest in analytics and data-driven decision-making are the main drivers for companies to invest in scraping.
• Crawling done by search engines is no longer a topic of increasing interest since they have done this since the early 2000s
• The search engine industry is a mature industry dominated by Google and Baidu, so few companies need to build crawlers.

Use Cases of Web Crawling in Data Science coding Projects

Web crawling is an integral part of your data science coding project. The following are some of the use cases of using web crawling in different data science coding projects.

1. Gather Social Media Data for Sentiment Analysis

Many organizations use web crawling to gather posts and remarks on different social media platforms like Facebook, Twitter, and Instagram. Organizations utilize the gathered information to survey how their brand is performing and find how their items or services are reviewed by their clients, it very well may be a positive survey, negative review, or unbiased.

2. Gather Financial Data at Stock Prices Forecasting

The stock market is brimming with vulnerability, hence stock price forecasting is vital in business. Web crawling is utilized to gather stock cost information from different platforms for various periods (for instance 54 weeks, two years, and so on).

The stock price data gathered can be dissected to discover trends and other behaviors. You can likewise utilize the information to make predictive models to predict future stock prices. This will assist stockbrokers with settling on choices for their business.

3. Gather Real Estate information for Price Estimation

Assessing and ascertaining the cost of land is tedious. Some real-estate companies use data science to make a prescient model to foresee the costs of properties by utilizing historical data.

These historical data are gathered from different sources on the web and extricated valuable data by utilizing web crawlers. Organizations likewise utilize this information to help their marketing strategy and make the right decisions.

For instance, an American online real estate company called Zillow has used data science to determine prices based on a range of publicly available data on the web.

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