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Course Skill Level:


Course Duration:

3 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    Big Data & Data Science

  • Course Code:


Who should attend & recommended skills:

Those with basic IT and Python skills

Who should attend & recommended skills

  • How to build graph data structures and create your own dynamic and interactive visualizations using a variety of tools.
  • Simple, but effective, techniques to model your data, handle big data, and depict temporal and spatial data
  • Fascinating examples and case studies to show you the real-world value of graph visualizations.
  • How to understand graph data structures, and create meaningful visualizations
  • Techniques for creating effective visualizations
  • Examples using the Gephi and Key Lines visualization packages
  • Real-world case studies
  • You’ll have a conceptual foundation as well as the practical skills to explore your own data with confidence

About this course

Web scraping is an essential technique used in many organizations to gather valuable data from web pages. This course will enable you to delve into web scraping techniques and methodologies. The course will introduce you to the fundamental concepts of web scraping techniques and how they can be applied to multiple sets of web pages. You’ll use powerful libraries from the Python ecosystem such as Scrapy, lxml, pyquery, and bs4 to carry out web scraping operations. You will then get up to speed with simple to intermediate scraping operations such as identifying information from web pages and using patterns or attributes to retrieve information. This course adopts a practical approach to web scraping concepts and tools, guiding you through a series of use cases and showing you how to use the best tools and techniques to efficiently scrape web pages. You’ll even cover the use of other popular web scraping tools, such as Selenium, Regex, and web-based APIs. By the end of this course, you will have learned how to efficiently scrape the web using different techniques with Python and other popular tools.

Skills acquired & topics covered

  • Different scraping techniques using a range of Python libraries such as Scrapy and Beautiful Soup
  • Building scrapers and crawlers to extract relevant information from the web
  • Automating web scraping operations to bridge the accuracy gap and manage complex business needs
  • Analyzing data and information from web pages
  • How to use browser-based developer tools from the scraping perspective
  • Using XPath and CSS selectors to identify and explore markup elements
  • Handle and manage cookies
  • Advanced concepts in handling HTML forms and processing logins
  • Optimizing web securities, data storage, and API use to scrape data
  • Using Regex with Python to extract data
  • Dealing with complex web entities by using Selenium to find and extract data

Course breakdown / modules

  • Introduction to web scraping
  • Understanding web development and technologies
  • Data finding techniques for the web
  • Python and the Web – Using urllib and Requests
  • Technical requirements
  • Accessing the web with Python
  • URL handling and operations with urllib and requests
  • Implementing HTTP methods

  • Technical requirements
  • Introduction to XPath and CSS selector
  • Using web browser developer tools for accessing web content
  • Scraping using lxml, a Python library

  • Technical requirements
  • Introduction to pyquery
  • Exploring pyquery
  • Web scraping using pyquery

  • Technical requirements
  • Web scraping using Beautiful Soup
  • Web scraping using Scrapy
  • Deploying a web crawler

  • Technical requirements
  • Introduction to secure web
  • HTML <form> processing
  • Handling user authentication
  • Working with cookies and sessions

  • Technical requirements
  • Introduction to web APIs
  • Accessing web API and data formats
  • Web scraping using APIs

  • Technical requirements
  • Introduction to Selenium
  • Using Selenium for web scraping

  • Technical requirements
  • Overview of regular expressions
  • Using regular expressions to extract data

  • Technical requirements
  • Managing scraped data
  • Analysis and visualization using pandas and matplotlib
  • Machine learning
  • Data mining