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Web Data Mining with Python

The world runs on data, but do you know how to use it? Upskill with our data mining in Python course.

(DM-PYTHON.AW1) / ISBN : 978-1-64459-687-6
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About This Course

Enroll in our data mining with Python course to uncover hidden patterns, extract valuable insights, and master the techniques that turn web data into actionable intelligence.

In this course, dive into web scraping, opinion mining, and social network analysis, then apply them to real-world problems. Learn how to collect data from dynamic websites, analyze sentiments, and predict user behavior—all using powerful Python libraries.

From basics like HTML inspection to advanced topics like hyperlink analysis and Web usage mining, you’ll get hands-on practice with real datasets. 

Intrigued? Get on the bandwagon with us!

Skills You’ll Get

  • Web Scraping: Extract data from websites, including text, images, and dynamic content, using Python libraries.
  • Opinion & Sentiment Analysis: Analyze emotions and trends from reviews, social media, and online discussions.
  • Web Structure Mining: Discover hidden relationships in web data through link analysis and graph mining.
  • Social Network Analysis: Map and study connections in networks like Facebook or Twitter to identify influencers.
  • Web Usage Mining: Predict user behavior by analyzing browsing patterns and transaction data.
  • Python for Data Mining: Master libraries like BeautifulSoup, Scrapy, and Pandas for efficient data extraction and processing.
Download Course Outline

1

Preface

2

Web Mining--An Introduction

  • Introduction
  • Introduction to Web mining
  • World Wide Web
  • Evolution of the World Wide Web
  • Internet and Web 2.0
  • An overview of data mining, modeling, and analysis
  • Difference between data mining and Web mining
  • Web mining and Python
  • How Python is helpful in Web mining?
  • Conclusion
  • Points to Remember
  • Questions
  • Key terms
3

Web Mining Taxonomy

  • Introduction
  • Introduction to Web mining
  • Web content mining
  • Web structure mining
  • Web usage mining
  • Key concepts
  • Conclusion
  • Points to remember
  • Questions
4

Prominent Applications with Web Mining

  • Introduction
  • Personalized customer applications-- E-commerce
  • Web search
  • Personalized portal and Web
  • Web service performance optimization
  • Process mining
  • Concepts of association rules
  • Association rule mining
  • Components of Apriori algorithm
  • Concepts of Sequential Pattern
  • Association Rule Mining and Python Libraries
  • Conclusion
  • Points to remember
  • Questions
  • Key terms
5

Python Fundamentals

  • Introduction
  • Introduction to Python
  • Basics of Python
  • Functions
  • Lists
  • Basics of HTML: inspecting a Web page
  • Basics of Python Libraries
  • Installation of Python
  • Introduction to commonly used IDE's and PDE
  • Installation of Anaconda
  • Conclusion
  • Points to remember
6

Web Scraping

  • Introduction
  • Introduction to Web scraping
  • Web scraping
  • Data extraction and preprocessing
  • Handling text, image, and videos
  • Scraping dynamic websites
  • Dealing with CAPTCHA
  • Case study: Implementing Web scraping to develop a scraper for finding the latest news
  • Conclusion
  • Points to remember
  • Questions
  • Key terms
7

Web Opinion Mining

  • Introduction
  • Concepts of opinion mining
  • Document level
  • Collection of review
  • Working with data
  • Pre-processing of data
  • Part of Speech tagging
  • Feature extraction
  • Case study for Sentiment Analysis
  • Conclusion
  • Points to remember
  • Questions
  • Key terms
8

Web Structure Mining

  • Introduction
  • Introduction to Web structure mining
  • Concepts of Web structure mining
  • Web structure mining
  • Web graph mining
  • Web information extraction
  • Deep Web mining
  • Web Search and Hyperlinks
  • Hyperlink analysis on the Web
  • Hyperlink Induced Topic Search (HITS)
  • Partitioning algorithm
  • Implementation in Python
  • Conclusion
  • Points to remember
  • Questions
  • Key terms
9

Social Network Analysis in Python

  • Introduction
  • Introduction to Social Network Analysis
  • Creating a network
  • Analyzing network
  • Distance measures in network connectivity
  • Network influencers
  • Case study on Facebook dataset
  • Conclusion
  • Points to remember
  • Questions
  • Key terms
10

Web Usage Mining

  • Introduction
  • Process of Web usage mining
  • Sources of data
  • Types of data
  • Key elements of Web usage data pre-processing
  • Data modeling
  • Discovery and analysis of pattern
  • Predictions on transaction pattern
  • Conclusion
  • Points to remember
  • Questions
  • Key terms

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  • Web scraping is the process of extracting raw data from websites, such as text, images, or tables, using tools like Python (BeautifulSoup, Scrapy).
  • Data mining goes further. It analyzes and uncovers patterns in large datasets (including scraped data) to generate insights, predict trends, or make decisions.

Think of it like this:

  • Web scraping = Collecting data from the web.
  • Data mining = Processing & analyzing that data to find valuable information.

Our Python Web Data Mining course helps businesses and researchers:

  • Discover hidden trends (e.g., customer buying habits, stock market shifts).
  • Improve decision-making by predicting future behavior (e.g., recommendation systems).
  • Detect fraud & risks (e.g., banking, cybersecurity).
  • Optimize marketing (e.g., personalized ads, sentiment analysis).
  • Enhance efficiency by automating insights from large datasets.

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