Should Data Scientists Learn Web Scraping?
More and more information is becoming available on the web with each passing second, but most of this data is only accessible using a web browser. Imagine the potential applications of all this data if it were structured and in a ready to analyze form. Businesses could get their hands on compelling insights and many new avenues of data-driven growth strategies could be unlocked with it.
That’s exactly what web scraping is – a tool for turning the unstructured data on the web into machine readable, structured data which is ready for analysis. There are many different approaches to getting data from the web such as writing a custom crawler from scratch, web crawler tools and ‘Data as a Service’ model companies. While there are dedicated services catering to the web data requirement of businesses, web scraping as a skill is gaining popularity too. Data scientist is a role that’s most likely to get some value addition with web scraping in the skill set.
However, there is a clear distinction between an enterprise-grade web scraping service and learning to scrape a simple HTML page from the web. We’ll get into this later, let’s now see if data scientists should actually pursue web scraping as a skill.
The evolution of data scientist
Data scientist is one of the most in-demand jobs in the technology industry right now. This demand is expected to increase further as more companies realize the value of big data as a business intelligence tool. Big data is helping businesses get insights about customer preferences, predict future industry trends and is enabling them to track their competitors’ activity in real time. As the person responsible for turning data into insights, the role of a data scientist is no more obscure and has gained mainstream popularity over the last few years.
The term ‘data scientist’ was coined by Jeff Hammerbacher and DJ Patil in 2008 in Silicon Valley. Ever since its inception, the skill sets and responsibilities associated with this role has been evolving, owing to the fresh challenges that keep coming up. Data is now growing both in volume and variety, contributing to the addition of complexities.
This scenario demands more from the role of data scientist such as the ability to deploy unconventional techniques to extract, mine and analyze data sets with a creative appetite.
Although there were no specialized courses for data scientists when the term first made it to the job postings, there are dedicated training programs and courses for data scientists now.
The data scientist’s skill-set
As with the challenges in big data, the skill sets of data scientists have been evolving too. Here are the key skills anyone pursuing data science as a career path should possess.
- Machine learning
- Multivariable Calculus and Linear Algebra
- Data munging
- Data Visualization and communication
Web being the biggest and ever-growing source of big data, there’s no doubt about web scraping being a great addition to your skill set as a data scientist. Having this unique skill would also help you stand out when on the lookout for a job.
Web scraping as a skill
When it comes to basic web scraping, you don’t really need to learn programming and reinvent the wheel, thanks to the nifty DIY tools out there. There are web scraping tools that are available as hosted solutions, desktop clients and browser extensions.
As a data scientist, you will be working around data a lot and the know-how of web scraping will prove to be invaluable in many occasions. Let’s say you’re looking to export a Wikipedia table to a CSV file for quick reference, learning how to scrape a webpage using Google docs might help you.
If you like to do things from scratch, learning programming can help you perform web scraping without the help of tools. You can check out our recent post on the best programming languages for web scraping to get started. An in-house crawler setup that you would build on your own can be useful in scraping small volumes of data from relatively simple websites. It will however not be adequate for recurring crawls that involve large-scale extraction. Since that would need a robust infrastructure, continuous maintenance and monitoring, it’s always better to outsource the web scraping project to a dedicated service provider.
Data scientists can think of web scraping as a welcome addition to their skill set if they want to be dynamic and take on more cross functional roles to help grow the business using data-driven decisions. The technical know-how of web scraping is not meant to replace the analytical skills that a data scientist should possess, but rather complement them. Those candidates who can draw on a wide range of skills surrounding big data will be an asset to the team and would land better opportunities. Web scraping is one of those relatively simple skills that will put you light years ahead of the competition.