Consuming data with API | Web Scraping: Python, JavaScript
Consuming data with API | Web Scraping: Python, JavaScript, available at $44.99, with 52 lectures, and has 8 subscribers.
You will learn about Scrape data from a website and save extracted data to a text file Scrape YouTube data into Google Sheets Scrape and email amazon data Build an application to consume data with API Build scrapers with Python Build scrapers with JavaScript and API This course is ideal for individuals who are Beginner Data Analyst or Beginner Data Scientist It is particularly useful for Beginner Data Analyst or Beginner Data Scientist.
Enroll now: Consuming data with API | Web Scraping: Python, JavaScript
Summary
Title: Consuming data with API | Web Scraping: Python, JavaScript
Price: $44.99
Number of Lectures: 52
Number of Published Lectures: 52
Number of Curriculum Items: 52
Number of Published Curriculum Objects: 52
Original Price: $34.99
Quality Status: approved
Status: Live
What You Will Learn
- Scrape data from a website and save extracted data to a text file
- Scrape YouTube data into Google Sheets
- Scrape and email amazon data
- Build an application to consume data with API
- Build scrapers with Python
- Build scrapers with JavaScript and API
Who Should Attend
- Beginner Data Analyst
- Beginner Data Scientist
Target Audiences
- Beginner Data Analyst
- Beginner Data Scientist
We build an application with Python and Django and use it to transform the data that an API sends, so our application is acting as a client and consuming the data.
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.
Course Curriculum
Chapter 1: Python Setup
Lecture 1: Introduction
Lecture 2: Installing Python on Windows
Lecture 3: Installing Python on Macs
Lecture 4: Creating a virtual environment on Windows
Lecture 5: Activating a virtual environment on Windows
Lecture 6: Updating pip on windows
Lecture 7: Creating a virtual environment on Macs
Lecture 8: Activating a virtual environment on Macs
Lecture 9: Updating pip on Macs
Chapter 2: Consume News with API ,Python and Django
Lecture 1: What is an API
Lecture 2: Installing Django on Windows
Lecture 3: Installing Django on Macs
Lecture 4: Creating a Django Project
Lecture 5: Creating a Django App
Lecture 6: Starting a Django Server
Lecture 7: Create URLS Route
Lecture 8: Install Request Module
Lecture 9: Generate API Key
Lecture 10: Create a view function
Lecture 11: Create HTML Template
Lecture 12: Consume data
Lecture 13: Download Project Code
Chapter 3: Web Scraping: Extract YouTube Data with API and JavaScript
Lecture 1: What is Web Scraping
Lecture 2: What is Robots.txt
Lecture 3: Legality of web scraping
Lecture 4: Checks before scraping data
Lecture 5: YouTube Data API
Lecture 6: Saving extracted data to Google Sheets
Lecture 7: Building the first JavaScript Scraper: part 1
Lecture 8: Building the first JavaScript Scraper: part 2
Lecture 9: Testing the first scraper
Lecture 10: Building the second JavaScript scraper
Lecture 11: Testing the second scraper
Lecture 12: Project Code
Chapter 4: Web Scraping: Extract Amazon Data with Python
Lecture 1: Create and activate a virtual environment
Lecture 2: Installing Python Packages
Lecture 3: Create a Python File
Lecture 4: Create variables
Lecture 5: Send emails from Python
Lecture 6: Create functions: Part 1
Lecture 7: Create functions: Part 2
Lecture 8: Create functions: Part 3
Lecture 9: Testing the scraper
Chapter 5: Web Scraping: Extract Data from website with Python
Lecture 1: What we will scrape
Lecture 2: Building the scraper: part 1
Lecture 3: Building the scraper: part 2
Lecture 4: Prototyping the scraper: part 1
Lecture 5: Prototyping the scraper: part 2
Lecture 6: Prototyping the scraper: part 3
Lecture 7: Prototyping the scraper: part 4
Lecture 8: Prototyping the scraper: part 5
Lecture 9: Scraping data
Instructors
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Digital Learning Academy
Digital learning 24 /7
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