Web scraping typically extracts large amounts of data from websites for a variety of uses such as price monitoring, enriching machine learning models, financial data aggregation, monitoring consumer sentiment, news tracking, etc. Browsers show data from a website. However, manually copy data from multiple sources for retrieval in a central place can be very tedious and time-consuming. Web scraping tools essentially automate this manual process.

Table of Content:

• What is Web Scraping?
• Basic of Data Science.
• Use Cases of Web Scraping in Data Science

What is Web Scraping?

“Web scraping,” also called crawling or spidering, is the automated gathering of data from an online source usually from a website. While scraping is a great way to get massive amounts of data in relatively short timeframes, it does add stress to the server where the source hosted.

Primarily why many websites disallow or ban scraping all together. However, as long as it does not disrupt the primary function of the online source, it is relatively acceptable.

Despite its legal challenges, web scraping remains popular even in 2019. The prominence and need for analytics have risen multifold. This, in turn, means various learning models and analytics engines need more raw data. Web scraping remains a popular way to collect information. With the rise of programming languages such a Python, web scraping has made significant leaps.

Basic of Data Science.

Data science is increasing the world with its capabilities to identify trends, predict the future, and derive deep insights like never before from large data sets. It is understood that data is the fuel for any data science-related project. Since the web is becoming the most important repository of data that has ever been, it makes sense to consider web scraping for fueling data science use cases.  In fact, aggregating web data has many applications in the data science arena. Here are some of the use cases.

Interesting Read: Top 15 Data Analytics Tools should use in 2020

Use Cases of Web Scraping in Data Science.

Use Case 1: Real-time analytics

Many data science projects require real-time or near real-time data for analytics. This can be helped by crawling websites using a low latency crawl. Low latency crawls work by extracting data at a very high frequency that matches with update speed of the target site. This gives near real-time data for analytics.

Use Case 2: Predictive modeling

Predictive modeling is all about analyzing data and using probability to predict outcomes for future scenarios. Every model includes a number of predictors, which are variables that can influence future results. The data required for making important predictors can be acquired from different websites by using web scraping. An analytical model is formulated once the processing is done.

Use Case 3: Natural language processing:

Natural language processing is used to provide machines with the ability to understand and process natural languages used by humans like English as opposed to a computer language like Java or Python. As it’s difficult to determine a definite meaning for words or even sentences in natural languages, natural language processing is a vast and complicated field. Since the data available on the web is of various nature, it happens to be highly useful in NLP. Web data can be extracted to form large text corpora which can be used in Natural language processing. Forums, blogs, and websites with customer reviews are great sources for Natural language processing.

Use Case 4: Training machine learning models

Machine learning is all about providing machines to learn on their own by providing them training data. Training data could differ according to unique cases. However, data from the web is ideal for training machine learning models for a wide range of use cases. With training data sets, machine learning models can be taught to do correlational tasks like classification, clustering, attribution, etc. Since the performance of a machine learning model will depend on the quality of training data, it is important to crawl only high-quality sources.

Provided with the training data, machine learning programs learn to do correlational tasks like classification, clustering, attribution, etc. Here, the efficiency and power of the machine learning program will hugely depend on the quality of the training data.

Interesting Read: Top 4 Website Data Scraping Services Use Cases

How Hir Infotech Data Scraping can help?

Hir Infotech is one of the miners in web crawling and data as a service model. The fully-managed nature of our solution helps data scientists focus on their core projects rather than try and master web scraping, which is a niche and technically challenging process. Since the solution is customizable from end to end, it can easily handle difficult and dynamic websites that aren’t crawl-friendly. We offer data in different structured formats like CSV, XML, and JSON. If you are looking to get web data for a data science requirement, you can get in touch with us.