Beautiful Soup & Requests for Data Science 2025

Name: Beautiful Soup & Requests for Data Science 2025
Rating: 4.6 (32 reviews)

Master Web Scraping with Beautiful Soup & Requests and Python: From Beginner to Advanced + Quizzes

Created byDr. Alexander Schlee

Last updated 4/2025

English

What you'll learn

How to use Beautiful Soup and Requests to extract data from websites
Extract, transform, and save data into CSV, JSON, and Excel
Apply scraping in a Data Science context (e.g. for analysis or automation)
Handle pagination, dynamic content, and clean extracted data
Build 3 complete hands-on projects

Course content

5 sections • 24 lectures • 4h 45m total length

Course Introduction3:20

Install Dependencies | Fetch and Parse HTML | Soup Object10:23
Find Elements with find()-function - Part 113:09
Master BeautifulSoup's find and find_all to retrieve the first matching element from HTML. Capture data by tag, class, and id, practice text extraction, and compare methods with practical scraping examples.
Find Elements with find()-function - Part 26:15
Use Beautiful Soup find with the a keyword and attrs to access elements and extract the first category from a tag.
Find Elements with find_all()-function8:20
Find Elements with CSS-Selectors | Select_One & Select8:59
Master data extraction with css selectors in BeautifulSoup by using select_one and select to target first elements or all matches, with class and id syntax to retrieve text.
Navigating DOM| Find Sibling Elements | Extract Attributes using get() & attrs()18:46
Question 1

Project Intro | Imports | Soup Object8:41
Extracting Data from the First Listing10:33
Learn to scrape the first book listing with Beautiful Soup, extracting name, rating, author, category, and price, then prepare data for Excel and JSON storage.
Extracting Data of all Listings12:39
Extract data for all 30 book listings using list comprehension and for loops, storing results in a list for export to pandas data frames, Excel, and JSON.
Save Data in Pandas Dataframe | Excel | JSON7:54
Questions

Overview | Import Libraries | GET Request8:07
learn to fetch a real estate site with requests, parse HTML with BeautifulSoup, and extract 15 listings per page across five pages, using a get request and building the soup.
Target necessary Data - First Listing - Part 113:20
Target necessary Data - First Listing - Part 29:11
Extract bedrooms, bathrooms, size in square meters, and garage data from listing using Beautiful Soup; then expand to 15 listings on the first page and five pages via list comprehension.
Appending all Results with List Comprehension - First Page13:41
Use a list comprehension to loop through results, extract listing names, streets, prices, beds, baths, sizes, and garages, and export to a pandas DataFrame and Excel/JSON for five pages scraping.
Store Data of First Page in Pandas Dataframe, Excel & JSON7:36
Pagination & Writing a Logic to extract Data from Multiple Pages22:16
Data Cleaning | Data Manipulation | Save Results ins Excel & JSON13:49
Scrape five pages into a pandas data frame, inspect with head and tail, perform a zip–state split for cleaning, and save results to Excel and JSON.

Project Overview | GET Request | Soup Object9:28
Learn to scrape restaurant listings by loading results page and following detail pages via relative URLs with requests and BeautifulSoup, storing data in a pandas dataframe for export to Excel.
Combining Base URL & Relative URLs to get Full URL11:30
Extracting Data of the First Page22:30
Learn to extract nine data points: restaurant name, category, phone, website, email, address, distance from the city, payment methods, and price range—from the first page using Beautiful Soup and requests.
Web Scraping Data by using the Try-Except Block20:20
Pagination - Expand the existing Logic to extract data from all pages15:14
Expand the existing pagination logic to scrape all 40 listings across five pages using Beautiful Soup and Requests, generating URLs and storing results in Panda's data frame, Excel, and JSON.
ExtractData from all Detail Pages and save in Pandas Dataframe | Excel | JSON9:43

Requirements

Basic knowledge of Python (variables, loops)
A computer with Python installed
Internet connection and curiosity to work with real-world data

Description

Unlock the power of web scraping for your data science journey!
In this hands-on course, you’ll learn how to collect, clean, and organize data from websites using Python’s most popular libraries — Beautiful Soup and Requests.

Web scraping is a vital skill in today’s data-driven world. Whether you're trying to collect real estate listings, product data, financial information, or research content — this course will show you how to automate the process from start to finish.

You’ll begin with the basics: how the web works, how HTML and the DOM are structured, and how to target specific content on a page. Step by step, you'll move on to real-world scraping techniques, navigating through nested elements, handling pagination, and exporting your data into formats ready for analysis.

To reinforce your skills, we’ve included 3 complete projects where you’ll build practical scrapers for real estate listings, book catalogs, and restaurant directories. You’ll also learn how to save your data into CSV, JSON, and Excel — perfect for further analysis with pandas or Excel.

By the end of this course, you'll be confident in your ability to:

Read and parse HTML using Beautiful Soup
Send and manage requests to websites
Work with real websites and extract meaningful data
Clean, format, and export data for analysis
Use scraping as a data source in your own projects

Whether you’re preparing for a data science job, building a personal project, or just curious about how websites work behind the scenes — this course is for you.

Who this course is for:

Beginners who want to get started with web scraping using Python
Data analysts or aspiring data scientists looking to collect real-world data
Developers or automation enthusiasts wanting to extract data from websites
Students or researchers working on data projects involving websites
Anyone who wants to master Beautiful Soup and Requests in practice

Beautiful Soup & Requests for Data Science 2025

What you'll learn

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Course content

Introduction1 lecture • 3min

Beautiful Soup Basic Concepts6 lectures • 1hr 6min

Beginner Level Project - Web Scraping Books4 lectures • 40min

Intermediate Level Project - Web Scraping Real Estate Listings7 lectures • 1hr 28min

Advanced Level Project - Web Scraping Restaurant Entries6 lectures • 1hr 29min

Requirements

Description

Who this course is for: