How Web Scraping Complements Data Scientists' Skills
Information about the skills of data analysis specialists is increasing every second, but most of this data is only accessible through a web browser. Imagine the potential applications of all this data if it were structured and presented in a ready-to-analyze format. That is exactly what web scraping is — a tool for converting unstructured data into machine-readable structured data, ready for analysis.
There are many different approaches to obtaining data from the Internet, such as writing a custom parser from scratch, using parser tools, and companies operating under the "data as a service" model. While specialized services exist to meet enterprise web data needs, web scraping as a skill is also gaining popularity. Data analysts can learn scraping to enhance the value of their skills.
Should Data Scientists Learn Web Scraping?
However, there is a clear distinction between enterprise-level scraping services and learning to collect a simple HTML page from the web. We will discuss this later, but for now, let's look at whether data scientists should learn web scraping as a skill.
The Evolution of Data Scientists
Data Scientist is one of the most sought-after professions in the tech industry. This demand is expected to grow as more companies realize the value of big data as a business intelligence tool. Big data helps companies gain insight into customer preferences, predict future industry trends, and monitor competitor activity in real time. The role of the data scientist, responsible for turning data into insights, has become widely known and gained popularity over the past few years.
The term 'data scientist' was coined by Jeff Hammerbacher and DJ Patil in 2008 in Silicon Valley. Since its inception, the skill sets and responsibilities associated with this role have continuously evolved in response to emerging challenges. Currently, data is growing both in volume and variety, leading to additional challenges in analyzing it.
Such a scenario requires data scientists to do more, such as the ability to apply unconventional methods to extract, mine, and analyze data sets.
Although when this term first appeared in job postings, there were no specialized courses for data scientists, now there are specific educational programs and courses for data scientists.
Data Scientist Skill Set
Just like big data challenges, the skills of data scientists are also evolving. Here are the core skills that everyone choosing data science as a career path should possess.
- Programming
- Statistics
- Machine Learning
- Multivariable Calculus and Linear Algebra
- Data Wrangling
- Data Visualization and Communication
The internet is the largest and constantly growing source of big data, so there is no doubt that web scraping will be an excellent addition to your skill set as a data scientist. Mastering this unique skill will also help you stand out when job hunting.
Data Scientists Should Definitely Learn Web Scraping
When it comes to basic web scraping, you don't need to learn programming and reinvent the wheel, thanks to convenient DIY tools. There are web scraping tools available as hosted solutions, desktop clients, and browser extensions.
As a data scientist, you will work with data extensively, and scraping know-how will prove invaluable in many cases. For instance, if you need to export a Wikipedia table to a CSV file for quick reference, learning how to scrape a web page using Google Docs might help.
If you prefer to do everything from scratch, learning programming can help you perform scraping without ready-made tools. You can check out our recent article on the best programming languages for web scraping to get started.
A custom scraper that you build yourself can be useful for collecting small amounts of data from relatively simple websites. However, it will not be sufficient for regular large-scale data collection. Since that would require reliable infrastructure, ongoing maintenance, and monitoring, it is always better to outsource web scraping projects to a specialized service provider.
In Conclusion
Data scientists can learn web scraping as a valuable addition to their skill set if they want to be dynamic and take on more cross-functional roles to help business growth with data-driven solutions.
Technical know-how of scraping is not intended to replace the analytical skills a data scientist should have, but rather to complement them. Candidates who can leverage a broad range of big data-related skills will be valuable to the team and enjoy more opportunities. Web scraping is one of those relatively simple skills that will keep you ahead of the competition for years to come.