Programming Python

Python Web Scraping Fundamentals

Collect, parse, clean, and store web data with practical Python workflows

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Quick Course Facts
19
Self-paced, Online, Lessons
19
Videos and/or Narrated Presentations
6.8
Approximate Hours of Course Media
About the Python Web Scraping Fundamentals Course

Python Web Scraping Fundamentals is a practical Programming course that teaches you how to gather useful information from websites using Python. You will learn how to collect, parse, clean, and store web data with practical Python workflows while building confidence with real scraping tools, patterns, and responsible data practices.

Build Practical Python Web Scraping Skills From Page Request To Stored Data

  • Learn the core web concepts behind URLs, HTTP, HTML, status codes, headers, and page structure.
  • Use Requests, Beautiful Soup, browser developer tools, and CSS selectors to find and extract the data you need.
  • Clean text, dates, numbers, links, duplicates, and inconsistent markup so scraped data becomes usable.
  • Save results to CSV, JSON, and SQLite while adding error handling, retries, logging, and responsible scraping habits.

This course introduces Python Web Scraping Fundamentals through a complete workflow for collecting, preparing, and storing web data.

You will begin with the foundations of how web scraping fits into Python data work, including the role of web pages, URLs, HTTP requests, and HTML. From there, you will set up a Python scraping environment and learn how to fetch pages with Requests, read response content, and understand the signals that status codes and headers provide.

As the course progresses, you will inspect pages with browser developer tools and parse HTML with Beautiful Soup. You will practice selecting elements by tags, classes, IDs, attributes, and CSS selectors, then apply those skills to extract links, images, tables, and text fields from real page structures.

The course also focuses on turning raw scraped content into reliable data. You will clean text, dates, numbers, and missing values, handle relative URLs and duplicates, work around inconsistent markup, and build reusable scraper functions for pagination and multi-page listings.

By the end of the course, you will know how to collect, parse, clean, and store web data with practical Python workflows, including saving results to CSV, JSON, and SQLite. You will also understand error handling, retries, timeouts, logging, robots.txt, rate limits, site policies, and how to recognize JavaScript-rendered pages or API alternatives, leaving you ready to build a complete product listing scraper and approach Programming data tasks with greater independence.

Course Lessons

Full lesson breakdown

Lessons are organized by topic area and each includes descriptive copy for search visibility and student clarity.

Foundations

3 lessons

Lesson 1: How Web Scraping Fits Into Python Data Work

18 min Preview
This lesson positions web scraping as one practical way Python data workers collect information when a clean dataset or API is not already available. Learners will see how scraping fits into a broader…

Lesson 2: Web Pages, URLs, HTTP, and HTML Basics

22 min
This lesson builds the mental model needed before writing scraping code: how browsers request pages, how URLs point to resources, what HTTP responses contain, and how HTML structures the content Pytho…

Lesson 3: Setting Up a Python Scraping Environment

17 min
In this lesson, learners set up a reliable Python environment for web scraping work. The focus is on practical tooling: choosing a Python version, creating an isolated virtual environment, installing …

Getting Data from the Web

2 lessons

Lesson 4: Fetching Pages with Requests

21 min
In this lesson, students learn how to fetch web pages with Python's requests library and inspect the responses before parsing. The focus is on practical request workflows: installing and importing req…

Lesson 5: Reading Status Codes, Headers, and Response Content

20 min
In this lesson, students learn how to inspect the raw HTTP response returned by a Python request before trying to parse page data. The focus is on reading status codes, interpreting response headers, …

Finding the Right Data

4 lessons

Lesson 6: Inspecting Pages with Browser Developer Tools

19 min
In this lesson, students learn how to use browser Developer Tools to locate the data behind a web page before writing scraper code. The focus is on inspecting rendered HTML, identifying stable selecto…

Lesson 7: Parsing HTML with Beautiful Soup

23 min
In this lesson, students learn how to locate the exact HTML elements that contain useful data using Beautiful Soup. The focus is not on downloading pages or storing results, but on reading a parsed do…

Lesson 8: Selecting Elements by Tags, Classes, IDs, and Attributes

22 min
In this lesson, students learn how to move from a messy HTML document to precise element selection using Beautiful Soup. The focus is on practical selectors: tags, classes, IDs, attributes, and CSS se…

Lesson 9: Using CSS Selectors for Cleaner Extraction

20 min
In this lesson, students learn how to use CSS selectors to extract web page data more cleanly than with broad tag searches. The focus is on practical selector patterns for titles, prices, links, table…

Core Scraping Patterns

1 lesson

Lesson 10: Extracting Links, Images, Tables, and Text Fields

24 min
In this lesson, students practice the core extraction patterns used in everyday scraping: links, images, tables, and labeled text fields. The focus is on selecting the right elements, reading the righ…

Preparing Useful Data

2 lessons

Lesson 11: Cleaning Text, Dates, Numbers, and Missing Values

23 min
Scraped web data rarely arrives ready for analysis. This lesson shows how to turn messy page text into reliable fields by normalizing whitespace, removing unwanted symbols, parsing dates and numbers, …

Lesson 12: Handling Relative URLs, Duplicates, and Inconsistent Markup

21 min
This lesson teaches the practical cleanup work that turns scraped links and elements into useful data. Students learn how to convert relative URLs into absolute URLs, normalize URLs before comparison,…

Scaling Basic Scrapers

2 lessons

Lesson 13: Scraping Pagination and Multi-Page Listings

24 min
This lesson teaches practical patterns for scraping paginated listings, such as product catalogs, search results, job boards, article archives, and directory pages. Students learn how to identify pagi…

Lesson 14: Structuring Scrapers with Functions and Reusable Helpers

22 min
In this lesson, Professor Chloe Vincent shows how to move from one-off scraping scripts to small, reusable scraping workflows. You will learn how to separate fetching, parsing, cleaning, and storing l…

Storing Scraped Data

1 lesson

Lesson 15: Saving Results to CSV, JSON, and SQLite

24 min
Scraped data is only useful when it can be reused. In this lesson, students learn how to save cleaned scraping results into three practical storage formats: CSV for spreadsheets, JSON for structured r…

Reliability and Maintenance

2 lessons

Lesson 16: Error Handling, Retries, Timeouts, and Logging

23 min
This lesson teaches the reliability layer every scraper needs once it leaves a notebook: bounded timeouts, careful exception handling, retry rules, backoff, and useful logging. Students learn how to p…

Lesson 17: Responsible Scraping: Rate Limits, robots.txt, and Site Policies

21 min
Responsible scraping is a reliability practice, not just an ethics topic. In this lesson, students learn how to check robots.txt , read site policies, identify rate limits, and design scrapers that re…

Beyond Static HTML

1 lesson

Lesson 18: Recognizing JavaScript-Rendered Pages and API Alternatives

22 min
This lesson teaches learners how to recognize when a page is not fully represented by the static HTML returned by requests , and how to decide whether the better path is browser automation, an API end…

Applied Project

1 lesson

Lesson 19: Capstone: Build a Complete Product Listing Scraper

25 min
In this capstone lesson, students assemble the core skills from the course into a complete product listing scraper. The project focuses on a realistic workflow: inspecting a catalog page, requesting H…
About Your Instructor
Professor Chloe Vincent

Professor Chloe Vincent

Professor Chloe Vincent guides this AI-built Virversity course with a clear, practical teaching style.