Here’s a detailed curriculum and design for an Advanced Web Scraping course. The focus is on teaching advanced techniques, best practices, and ethical considerations.
Curriculum Outline: Advanced Web Scraping
1. Introduction to Advanced Web Scraping
- Overview of advanced concepts in web scraping
- Difference between basic and advanced web scraping
- Ethical and legal considerations
- Tools and libraries for advanced scraping (e.g., Scrapy, Selenium, BeautifulSoup, Requests, Puppeteer)
2. Dynamic Websites and JavaScript Rendering
- Scraping websites with dynamic content
- Introduction to headless browsers (e.g., Selenium, Puppeteer)
- Handling single-page applications (SPAs) and infinite scrolling
- Extracting data from Shadow DOM and iframes
3. Handling Anti-Scraping Mechanisms
- Understanding anti-bot measures (e.g., CAPTCHA, rate-limiting, IP blocking)
- Using proxies and rotating IPs (e.g., ScraperAPI, Bright Data)
- Implementing user-agent rotation and session persistence
- Strategies to bypass CAPTCHAs (e.g., third-party solvers, machine learning techniques)
4. Advanced Parsing Techniques
- Parsing complex HTML and nested structures
- Extracting data using XPath and CSS selectors
- Handling large datasets efficiently
- Managing encoding issues (e.g., Unicode, non-UTF8 pages)
5. Working with APIs
- Reverse-engineering APIs using browser developer tools
- Authentication techniques (e.g., OAuth, tokens, cookies)
- Using GraphQL and REST APIs for scraping
- Handling rate limits and API errors gracefully
6. Data Storage and Post-Processing
- Storing scraped data in databases (SQL, NoSQL)
- Exporting data to formats like JSON, CSV, Excel
- Cleaning and transforming data with Pandas
- Integration with ETL pipelines
7. Scraping at Scale
- Distributed scraping using tools like Scrapy and Celery
- Managing concurrency and parallelism
- Caching strategies to reduce redundant requests
- Monitoring and maintaining scraping jobs
8. Advanced Use Cases
- Scraping e-commerce sites for product data
- Gathering social media data (Twitter, Instagram, LinkedIn)
- News aggregation and sentiment analysis
- Building custom web crawlers for large-scale data collection
9. Introduction to Machine Learning in Web Scraping
- Using NLP for data extraction (e.g., named entity recognition)
- Automating data classification and categorization
- Introduction to AI-driven scraping tools
10. Deploying and Monitoring Scraping Bots
- Deploying scraping bots to cloud platforms (AWS, GCP, Azure)
- Automating scraping with CI/CD pipelines
- Monitoring and logging scraping activities
- Debugging common errors and failures
Design: Course Delivery
1. Duration
- Total time: 6–8 weeks
- Weekly sessions: 2–3 hours each
- Hands-on projects at the end of each module
2. Teaching Methods
- Lecture: Theoretical concepts and walkthroughs
- Live Coding: Practical demonstrations during class
- Assignments: Mini-projects to practice concepts
- Case Studies: Real-world scraping challenges
- Capstone Project: Build a fully functional scraper for a complex website
3. Tools & Resources
- Software: Python, Scrapy, Selenium, Puppeteer
- Platforms: Jupyter Notebook, Google Colab, GitHub
- Libraries: BeautifulSoup, Requests, Pandas, SQLAlchemy
- Cloud Services: AWS Lambda, Google Cloud Functions, Docker
4. Assessment
- Weekly quizzes
- Graded assignments
- Final capstone project evaluation
- Peer reviews
Basics
Web Scraping is the process of automatically extracting data and particular information from websites using software or script.
Web scraping is an automatic process of extracting information from web.
To understand the difference between these two terms, let us look into the comparison table given hereunder −
| Web Crawling | Web Scraping |
|---|---|
| Refers to downloading and storing the contents of a large number of websites. | Refers to extracting individual data elements from the website by using a site-specific structure. |
| Mostly done on large scale. | Can be implemented at any scale. |
| Yields generic information. | Yields specific information. |
| Used by major search engines like Google, Bing, Yahoo. Googlebot is an example of a web crawler. | The information extracted using web scraping can be used to replicate in some other website or can be used to perform data analysis. For example the data elements can be names, address, price etc. |
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