Why?
One of the most critical and challenging stages in any data science project is data acquisition. Without high-quality, relevant data, even the most advanced models cannot perform well. Web scraping is a powerful method for collecting structured and unstructured data from websites, especially when public APIs are unavailable. Mastering this skill enables data scientists to unlock real-world insights from diverse online sources, making it an essential part of the data gathering toolbox.
What?
This course introduces students to the core concepts and tools used in web scraping. It covers basic scraping logic, tools and libraries, Python implementations, and methods to manage, clean, and store scraped data. Students will also learn how to scrape dynamic content and multiple pages, while considering ethical and legal constraints.
Curriculum:
Introduction to Web Scraping
Understanding what web scraping is, its applications in data science, basic concepts like HTML and HTTP, and the ethics and legality of scraping websites.
Applications and Tools
Overview of tools and libraries commonly used for web scraping such as BeautifulSoup, Scrapy, and Selenium, and selecting the right tool based on the task.
Web Scraping in Python
Hands-on scraping using Python libraries, parsing HTML, navigating website structures (DOM), and extracting useful information from tags and attributes.
Storing Scraped Data
Techniques for cleaning, formatting, and saving scraped data into CSV, JSON, or databases for later analysis or integration into data pipelines.
Scraping from Multiple Web Pages
Navigating and scraping data across multiple web pages using URL patterns, pagination handling, and maintaining scraping efficiency and scalability.
Notes
This field has its limitations due to copyrights and bot detectors for example, so you must be little careful what and how much you scrape.