Web Scraping Projects For Beginners

Extract data with Python , JavaScript, API and using No Code

What you'll learn
Scrape data automatically with No Code
Scrape YouTube data using JavaScript and YouTube Data API
Scrape Amazon Data with Python
Scrape data with Scrapy
Scrape data with BeautifuSoup
Scrape data into GoogleSheets from YouTube
Requirements
Basic knowledge of Python advised
Basic knowledge of JavaScript advised.
Description
Web scraping is the process of collecting structured web data in an automated fashion. It’s also called web data extraction. Some of the main use cases of web scraping include price monitoring, price intelligence, news monitoring, lead generation, and market research among many others.In general, web data extraction is used by people and businesses who want to make use of the vast amount of publicly available web data to make smarter decisions.If you’ve ever copied and pasted information from a website, you’ve performed the same function as any web scraper, only on a microscopic, manual scale. Unlike the mundane, mind-numbing process of manually extracting data, web scraping uses intelligent automation to retrieve hundreds, millions, or even billions of data points from the internet’s seemingly endless frontier.Web data extraction – also widely known as data scraping – has a huge range of applications. A data scraping tool can help you automate the process of extracting information from other websites, quickly and accurately. It can also make sure the data you’ve extracted is neatly organized, making it easier to analyze and use for other projects.In the world of e-commerce, web data scraping is widely used for competitor price monitoring. It’s the only practical way for brands to check the pricing of their competitors’ products and services, allowing them to fine-tune their own price strategies and stay ahead of the game. It’s also used as a tool for manufacturers to ensure retailers are compliant with pricing guidelines for their products. Market research organizations and analysts depend on web data extraction to gauge consumer sentiment by keeping track of online product reviews, news articles, and feedback.There’s a vast array of applications for data extraction in the financial world. Data scraping tools are used to extract insight from news stories, using this information to guide investment strategies. Similarly, researchers and analysts depend on data extraction to assess the financial health of companies. Insurance and financial services companies can mine a rich seam of alternative data scraped from the web to design new products and policies for their customers.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 What is Web Scraping and Web Crawling

Lecture 3 What is Robot.txt

Lecture 4 Legality of Web Scraping

Lecture 5 Checks before web scraping

Section 2: Python Setup

Lecture 6 Installing Python on Windows

Lecture 7 Installing Python on Macs

Lecture 8 Create a virtual environment on Windows

Lecture 9 Activate a virtual environment on Windows

Lecture 10 Create a virtual environment on Macs

Lecture 11 Activate a virtual environment on Macs

Lecture 12 Update pip

Lecture 13 Install Beautiful soup

Lecture 14 Install Scrapy

Lecture 15 Installing Visual Studio Code

Section 3: Web Scraping with Python and Beautiful Soup

Lecture 16 What we will scrape

Lecture 17 Building the web scraping script : part 1

Lecture 18 Building the web scraping script : part 2

Lecture 19 Prototyping the script: Part 1

Lecture 20 Prototyping the script: Part 2

Lecture 21 Prototyping the script: Part 3

Lecture 22 Prototyping the script: Part 4

Lecture 23 Prototyping the script: Part 5

Lecture 24 Testing the script

Section 4: Web Scraping with Python and Scrapy

Lecture 25 Create a Scrapy project

Lecture 26 Components ofa scrapy project

Lecture 27 Scrapy architecture

Lecture 28 Creating a Spider: Part 1

Lecture 29 Creating a Spider: Part 2

Lecture 30 Scraping data with Scrapy shell: Part 1

Lecture 31 Scraping data with Scrapy shell: Part 2

Lecture 32 Testing the Scrapy Spider

Section 5: Scraping Amazon Data with Python

Lecture 33 Create and activate a virtual environment

Lecture 34 Install Python Packages

Lecture 35 Create a Python File

Lecture 36 Create variables

Lecture 37 send emails from Python

Lecture 38 Create functions: Part 1

Lecture 39 Create functions: Part 2

Lecture 40 Create functions: Part 3

Lecture 41 Testing the script

Section 6: Web Scraping YouTube Data with API & JavaScript

Lecture 42 What is an API

Lecture 43 YouTube Data API

Lecture 44 Using GoogleSheets

Lecture 45 Building JavaScript Scraper : Part 1

Lecture 46 Building JavaScript Scraper : Part 2

Lecture 47 Building JavaScript Scraper : Part 3

Lecture 48 Testing the first scraper

Lecture 49 Building the second scraper

Lecture 50 Testing the second scraper

Section 7: Microsoft 365 and Power Automate Setup

Lecture 51 Microsoft 365 Business Standard Setup

Lecture 52 Getting started with Microsoft 365

Lecture 53 What is Power Automate

Section 8: Automating Web Scraping with No Code

Lecture 54 Installing Power Automate Desktop

Lecture 55 Explore Power Automate Desktop

Lecture 56 Automate web scraping

Beginners to web scraping