DM-PYTHON.AW1

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.

10 Interactive Lessons and 113 topics mapped to the official exam objectives

Intermediate Self-paced · 1 year access

10Interactive Lessons

113Topics

01 / Skills you'll get

What you will be able to do

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!

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.

Course Highlights

10 Structured Lessons Comprehensive coverage of core course objectives
1 Year Full Access Self-paced learning accessible anytime on all devices

02 / Lessons & labs

See exactly what you will learn and practice

Download outline (PDF)

Lessons

10 Interactive Lessons · 113 topics

01 Preface

02 Web Mining--An Introduction 13 topics

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

03 Web Mining Taxonomy 9 topics

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

04 Prominent Applications with Web Mining 15 topics

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

05 Python Fundamentals 12 topics

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

06 Web Scraping 12 topics

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

07 Web Opinion Mining 13 topics

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

08 Web Structure Mining 16 topics

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

09 Social Network Analysis in Python 11 topics

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 12 topics

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

03 / FAQs

Questions before you start

What is the difference between web scraping and data mining?

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.

Why is data mining important?

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.

Turn Data into Information

Extract, analyze, and profit from the web with this powerful data mining with Python course.

1 year of full access
Certificate of completion