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Section outline

    • Introduction to Data Scraping (1 Hour)
      • Overview, applications, and ethical considerations.
    • Web Scraping Basics (3 Hours)
      • Understanding HTML, DOM, and web page structures.
      • Tools and libraries for web scraping.
    • Beautiful Soup for Web Scraping (3 Hours)
      • Navigating and parsing HTML.
      • Hands-on exercises extracting data using tags and attributes.
    • Scrapy for Large-Scale Scraping (3 Hours)
      • Setting up Scrapy projects, handling pagination, and complex extractions.
    • Selenium for Dynamic Content (2 Hours)
      • Automating browsers for JavaScript-rendered content.
      • Interaction with dynamic elements like forms and buttons.
    • Data Cleaning and Preparation (2 Hours)
      • Techniques for handling missing data and duplicates using Pandas.
    • Introduction to Data Mining (1 Hour)
      • Overview of concepts, tasks, and applications.
    • Clustering and Classification (3 Hours)
      • Techniques like K-means and decision trees.
      • Implementation using Scikit-learn.
    • Association Rules and Pattern Mining (2 Hours)
      • Market basket analysis and Apriori algorithm.
    • Project and Case Study (2 Hours)
      • End-to-end data scraping and mining workflow.
      • Presenting findings and insights from practical projects.