In a world dominated by information systems, data is king. In fact, with so many sources of versatile and applicable knowledge, our progress is only constrained by ineffective tools to collect, store, and analyze information. That being said, tech-savvy internet users have long understood that the future lies in big data acquisition.
While large data sets don’t do much for our primitive brains, information is the key to driving modern businesses, information systems, and tech solutions to new heights. By picking the most relevant and valuable sources of information, even a beginner internet user can start automating collection processes and implementing analysis tools to benefit from the collected information.
For example, let’s assume that you are searching for cheap plane tickets to save money and go on vacation. With the help of automated data scrapers, you can build a data set of flights and their prices. Then, there are many opportunities to branch out and discover new insights: you can run multiple extraction bots to target different airlines and find the best possible deal. Even better, with enough time, you can start implementing additional tools to change your location and track all price changes to uncover different trends that will help you best options a lot more often.
Isn’t that incredible? Just look at how much value we extracted from a simple individual project that is achievable with very little training. Modern businesses take data scraping procedures to a whole new level, targeting retailers, social media sites, and other platforms with automated tools. While anyone could visit these pages and manually collect public data, data scrapers are more efficient. Well-scripted extraction tools can visit multiple targets and construct a massive data set in a matter of seconds.
Well-structured data extraction procedures are like art for data science geeks. Fortunately, the most beautiful part is that everyone can acquire this skill. In this article, we will cover the process of web scraping. Here you will find the best ways to start extracting and analyzing data from the web. With enough expertise, you can transform a well-catered hobby into a good-paying job.
We will also cover the use of Internet privacy tools for data extraction. For example, if you use cURL to collect data, hiding your connection with a specialized cURL proxy will help keep your main address safe from IP bans. Modern internet users understand the benefits of risks of web scraping, and proxy suppliers meet their demands with a cURL proxy or other residential proxies for data-sensitive activities. For more info on these products and their benefits, click here.
Data scraping is an automated process that extracts information from the site’s HTML files. The task itself is usually split into two segments – web scraping and parsing.
Anyone can learn the first step: writing a script that downloads the HTML document file from the web server. The web is full of tutorials that let you set up a coding environment and run your first web scraping script.
Python is the world’s most popular coding language. Paired with powerful libraries that enrich the effectiveness of the script, you can find amazing step-by-step tutorials to begin your data scraping journey.
After scraping, the parsing process takes the HTML and eliminates the unnecessary clutter that structures the web page. Remember, what we are looking for is raw data. For reference, you can download BeautifulSoup4 (BS4) – an external Python library that deconstructs the HTML code and structures the valuable information in the tags into data sets.
While anyone can visit the page and look at presented data, computers are not as perceptive and work much better with numbers. At some point, you get too much information on your hands, especially if collected data is appended in short time intervals.
For example, the most common web scraping use case for businesses is market research. Let’s look at a concrete subtask – competitor price monitoring. If your company sells similar or identical products, data scrapers can help you keep track of their prices and sensitive changes. With instant feedback from analysis tools, companies change their pricing to offer more attractive deals. Extracting knowledge from big data sources helps you make the right decisions much faster.
While Python remains the most versatile option for scraping with plenty of educational material, you can start extracting data in an even easier way – a simple cURL command. You can combine commands, make conditional statements, and other adjustments in a shell script. Once the need for extra features and more control arises, then it is a good time to transition to Python or take a step further and work with pre-built scrapers.
Data scraping is an incredibly valuable skill that anyone can learn. With the tools and coding tips described above, you can build a solid foundation of data science and analytics through valuable practice. Find a suitable target to apply the newly acquired knowledge, and start collecting useful data in no time!