The following comprehensive listings were produced by analyzing our large member database, extracting websites that our members mentioned or liked, and for each web site, identifying
When it is first mentioned by one of our members
The number of times it was mentioned
Keywords found when visiting the front page with a web crawler, using a pre-selected list of seed keywords
The design of the member database (non-mandatory sign-up questions and choices offered to new members on sign-up) was done by our home data scientist (me) long ago, precisely with the purpose in mind of performing analyses like this one, down the road. Other analyses produced in the past include: 6,000 companies hiring data scientists, best cities for data scientists, demographics of data scientists, and 400 job titles for data scientists: see related links at the bottom of this article.
Web Scraping tools are specifically developed for extracting information from websites. They are also known as web harvesting tools or web data extraction tools. These tools are useful for anyone trying to collect some form of datafrom the Internet. Web Scraping is the new data entry technique that don’t require repetitive typing or copy-pasting.
These software look for new data manually or automatically, fetching the new or updated data and storing them for your easy access. For example, one may collect info about products and their prices from Amazon using a scraping tool. In this post, we’re listing the use cases of web scraping tools and the top 10 web scraping tools to collect information, with zero coding.
The ability to use data to achieve enterprise goals requires advanced skills that many organizations don’t yet have. But they are looking to add them – and fast. The question is, what type of big data expert is needed? Does an organization need a data scientist or does it need a business analyst? Maybe it even needs both. These two titles are often used interchangeably, and confusion abounds.
Business analysts typically have educational backgrounds in business and humanities. They find and extract valuable information from a variety of sources to evaluate past, present, and future business performance – and then determine which analytical models and approaches will help explain solutions to the end users who need them.
With educational backgrounds in computer science, mathematics, and technology, data scientists are digital builders. They use statistical programming to actually construct the framework for gathering and using the data by creating and implementing algorithms to do it. Such algorithms help businesses with decision making, data management, and the creation of data visualizations to help explain the data that they gather.