Why Every Data Scientist Should Know Web Scraping

Data scientists are skilled in making meaning out of data; so much so, that today data science is often synonymous with data analytics. But before data scientists can run algorithms and analytics technologies, they must collect data. Data mining is an appropriate collection process for structured data, but web scraping is more useful for unstructured data. Data scientists should be skilled in web scraping as an essential data collection method.

What is Web Scraping?

Web scraping is an automated process for crawling and scraping websites to compile large amounts of data. A web crawler is essentially an algorithm that searches the web for the requested data, after which the web scraper pulls the data from the websites. Data scientists can program a web crawler to collect specific data types pages across the web or just from a particular site. After the crawler has collected the information, the scraper extracts that data for further analysis. Web scraping can also be done manually for simple collection processes, and some data scientists collect and categorize web-based data by hand, simply copying and pasting information from a website to save in a document or data storage system. Whether automated or manual, web scraping compiles data from one place to store it in a database or spreadsheet for further investigation. 

While individual data scientists or teams can use web scraping to collect data, it is more common to find web scrapers programmed into a website. For example, many websites and search engines compile information on a particular topic from across the web. The web scrapers then compile the data from multiple websites and present it to the user in one place. Building a web scraping program and presenting the scraped data through a website or database saves users time looking for information on countless pages and includes data such as product prices, job-board posts, and photos or location data. Data scientists should learn web scraping to improve their data collection and curation skills.

Why Data Scientists Should Learn Automated Web Scraping

Although it is possible to perform web scraping manually, it’s not always practical. Using machine learning algorithms to automate the web-scraping process is faster and easier and saves time and resources. Data scientists can compile much more information using a web crawler than a manual process. In addition, automated web scraping allows data scientists to seamlessly extract data from websites and upload it into a categorized system for a more organized database.

Web scraping is a required skill for collecting data from online sources—which include a mix of text, images, and links to other pages—in formats that correspond to different programming languages. Web-based data is less structured than numerical data or plain text and requires a method of translating one type of data format into another. Automated web scraping compiles and transposes unstructured HTML into structured rows-and-columns data making it easier for data scientists to understand and analyze the different data types collected. 

Web-Scraping with Python Data Science Libraries 

Many data scientists create web crawlers and scrapers using Python and data science libraries. Among the different types of web crawlers are those programmed to collect data from specific URLs, on a general topic, or to update previously collected web data. Web crawlers can be developed with many programming languages, but Python's open-source resources and active user community have ensured that there are multiple Python libraries and tools available to do the job. For example, BeautifulSoup is one of many Python data science libraries for HTML and XML data extraction. 

Scrapy is another popular Python library that includes classes and features such as spiders and crawlers, to collect data. Data scientists use Scrapy to program web crawlers and scrapers with specific search criteria. Spider crawlers and arguments define the data type to be collected and where to collect it from. After the crawl request is generated, the web scraper extracts data. Data scientists then export the extracted data into various Excel-compatible text files or SQL databases. Uploading data into a compatible relational database management system simplifies data cleaning and analysis. Data scientists often curate this data and make it available as an open resource, i.e., publicly available datasets.

Web Scraping and Responsible Machine Learning

Many popular websites use web scrapers to compile information, but their use has come under criticism. Using Python for web scraping has raised legal and ethical concerns in the data science industry. Web scraping could be considered a cybercrime, depending on a website’s terms of service and the methods used to crawl the site. The scraper may be regarded as stealing copyrighted information or attacking the site. Web scraping is associated with creating malicious bots that hack sites and wreak havoc on the internet, even outside the data science industry. Web crawlers also raise concerns about data privacy, safety, and cybersecurity. Data science students and professionals interested in web scraping should uphold the tenets of responsible machine learning when constructing a crawler. 

Want to Learn Web Scraping?

If you want to learn more about web scraping, you essentially have two choices: use an existing web scraping tool or build your own. If building your own is more appealing, learning the Python programming language is one of the best ways to go about it. Noble Desktop's Python classes and bootcamps offer students and professionals the training needed to crawl the web for digital content and user data. In the Python for Automation bootcamp, students learn to automate data mining and web scraping processes. Noble Desktop’s live online data science courses and in-person data science classes include additional training in programming, automation, and machine learning for beginners and advanced data scientists.