Building Automated Data Extraction Pipelines with Python

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Tech Academy

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1. Introduction to Automated Data Extraction

1. Introduction.mp4

00:20

2. Understanding the importance of automated data extraction.html

3. Identifying use cases for automated data extraction.html

4. Web Scraping Overview.html

5. Introduction to Python libraries for data extraction.html

2. Setting up Your Data Extraction Environment

1. Installing Python on Windows.mp4

03:38

2. Installing Python on Mac OS.mp4

05:28

3. Updating Pip.mp4

01:43

4. Create and activate a virtual environment.mp4

04:19

5. Install Scrapy.mp4

06:11

6. Install Beautiful Soup.mp4

05:49

7. Note on Text Editors.html

8. Installing Visual Studio Code Text Editor.mp4

06:00

9. Best practices for data extraction pipelines.html

3. Building Basic Data Extraction Pipeline using BeautifulSoup

1. What we will extract.mp4

04:04

2. Writing Python script for basic data extraction - Part 1.mp4

06:35

3. Writing Python script for basic data extraction -Part 2.mp4

06:35

4. Prototyping the script - Part 1.mp4

06:23

5. Prototyping the script - Part 2.mp4

04:06

6. Prototyping the script - Part 3.mp4

07:02

7. Prototyping the script - Part 4.mp4

06:36

8. Prototyping the script - Part 5.mp4

11:37

9. Extracting data with the script.mp4

06:26

4. Building Basic Data Extraction Pipeline using Scrapy

1. Creating a Scrapy project.mp4

03:44

2. Components of a scrapy project.mp4

08:27

3. Scrapy architecture.mp4

06:13

4. Creating a spider Part 1.mp4

05:43

5. Creating a spider Part 2.mp4

10:07

6. Extracting data with scrapy shell Part 1.mp4

05:07

7. Extracting data with scrapy shell Part 2.mp4

11:59

8. Running the spider to extract data.mp4

06:47

5. Building Basic Data Extraction Pipeline for e-commerce

1. Create and activate a virtual environment.html

2. Install Python Packages.mp4

01:54

3. Creating a Python file.mp4

03:30

4. Creating Variables.mp4

07:09

5. Enabling Gmail Security.mp4

01:34

6. Creating Functions Part 1.mp4

11:20

7. Creating Functions Part 2.mp4

13:52

8. Creating Functions Part 3.mp4

10:27

9. Extracting data with the Python Script.mp4

04:22

Description

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

Who is this for?

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.

What You Need to Know?

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)

More details

Description
In the age of Big Data, the ability to effectively extract, process, and analyze data from various sources has become increasingly important. ThisÂ 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.

ThisÂ 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 ScrapyÂ f to build ourÂ 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.

Who this course is for:
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.

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