Building Automated Data Extraction Pipelines With Python

Data Extraction and Scraping Techniques Using Python

What you'll learn

How to automate data extraction pipelines using Python

How to scrape data from e-commerce websites using Python

How to use Scrapy to build scalable and efficient web scrapers

How to use Requests to make HTTP requests to web servers

Scrape data with BeautifuSoup

Scrape data with Scrapy

Scrape e-commerce Data with Python

How to use Beautiful Soup to parse HTML

How to install and set up Python libraries for data extraction

How to use Python libraries for data extraction

Common use cases for automated data extraction

The importance of automated data extraction

Python 3.x installed on the computer

Requirements

A computer with internet access and the ability to run Python

Basic knowledge of Python programming language

Basic knowledge of HTML, CSS, and JavaScript

Text editor or integrated development environment (IDE) for Python coding

Comfortable using the command-line interface (CLI)

Description

In the age of Big Data, the ability to effectively extract, process, and analyze data from various sources has become increasingly important. This  course will guide you through the process of building automated data extraction pipelines using Python, a powerful and versatile programming language. You will learn how to harness Python's vast ecosystem of libraries and tools to efficiently extract valuable information from websites, APIs, and other data sources, transforming raw data into actionable insights.This  course is designed for data enthusiasts, analysts, engineers, and anyone interested in learning how to build data extraction pipelines using Python. By the end of this course, you will have developed a solid understanding of the fundamental concepts, tools, and best practices involved in building automated data extraction pipelines. You will also gain hands-on experience by working on a real-world project, applying the skills and knowledge acquired throughout the course. We will be using two popular Python Libraries called BeautifulSoup and Scrapy  f to build our  data pipelines.Beautiful Soup is a popular Python library for web scraping that helps extract data from HTML and XML documents. It creates parse trees from the page source, allowing you to navigate and search the document's structure easily. Beautiful Soup plays a crucial role in data extraction by simplifying the process of web scraping, offering robust parsing and efficient navigation capabilities, and providing compatibility with other popular Python libraries. Its ease of use, adaptability, and active community make it an indispensable tool for extracting valuable data from websites.Scrapy is an open-source web crawling framework for Python, specifically designed for data extraction from websites. It provides a powerful, flexible, and high-performance solution to create and manage web spiders (also known as crawlers or bots) for various data extraction tasks.Scrapy plays an essential role in data extraction by offering a comprehensive, high-performance, and flexible web scraping framework. Its robust crawling capabilities, built-in data extraction tools, customizability, and extensibility make it a powerful choice for data extraction tasks ranging from simple one-time extractions to complex, large-scale web scraping projects. Scrapy's active community and extensive documentation further contribute to its importance in the field of data extraction.

Overview

Section 1: Introduction to Automated Data Extraction

Lecture 1 Introduction

Lecture 2 Understanding the importance of automated data extraction

Lecture 3 Identifying use cases for automated data extraction

Lecture 4 Web Scraping Overview

Lecture 5 Introduction to Python libraries for data extraction

Section 2: Setting up Your Data Extraction Environment

Lecture 6 Installing Python on Windows

Lecture 7 Installing Python on Mac OS

Lecture 8 Updating Pip

Lecture 9 Create and activate a virtual environment

Lecture 10 Install Scrapy

Lecture 11 Install Beautiful Soup

Lecture 12 Note on Text Editors

Lecture 13 Installing Visual Studio Code Text Editor

Lecture 14 Best practices for data extraction pipelines

Section 3: Building Basic Data Extraction Pipeline using BeautifulSoup

Lecture 15 What we will extract

Lecture 16 Writing Python script for basic data extraction - Part 1

Lecture 17 Writing Python script for basic data extraction -Part 2

Lecture 18 Prototyping the script - Part 1

Lecture 19 Prototyping the script - Part 2

Lecture 20 Prototyping the script - Part 3

Lecture 21 Prototyping the script - Part 4

Lecture 22 Prototyping the script - Part 5

Lecture 23 Extracting data with the script

Section 4: Building Basic Data Extraction Pipeline using Scrapy

Lecture 24 Creating a Scrapy project

Lecture 25 Components of a scrapy project

Lecture 26 Scrapy architecture

Lecture 27 Creating a spider : Part 1

Lecture 28 Creating a spider : Part 2

Lecture 29 Extracting data with scrapy shell : Part 1

Lecture 30 Extracting data with scrapy shell : Part 2

Lecture 31 Running the spider to extract data

Section 5: Building Basic Data Extraction Pipeline for e-commerce

Lecture 32 Create and activate a virtual environment

Lecture 33 Install Python Packages

Lecture 34 Creating a Python file

Lecture 35 Creating Variables

Lecture 36 Enabling Gmail Security

Lecture 37 Creating Functions: Part 1

Lecture 38 Creating Functions: Part 2

Lecture 39 Creating Functions: Part 3

Lecture 40 Extracting data with the Python Script

Data analysts and data scientists who want to expand their skills and automate the data collection process.,Business analysts who need to extract data from websites to inform business decisions.,Researchers who need to extract data from a variety of sources for their research projects.,Web developers who want to build web scrapers for their projects.,Digital marketers who want to extract data from social media platforms and other online sources.,Students who want to learn practical skills in data extraction and scraping.,Professionals who want to switch careers to a data-related field.,Anyone who wants to learn how to automate the process of collecting data from the web.