Published 8/2024
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 50m | Size: 423 MB
Save yourself from repetitive data download processes by using advanced Python programming.
What you’ll learn
Download data from any API
Scrape tables from any webpage on the Internet
Preprocess tables to visualize data correctly
Merge multiple tables into one
Refactor code to write fewer lines
Requirements
All materials are explained step by step from a blank notebook. It is adapted for people of all levels, although beginners will spend more time learning.
Description
In the field of Data Science, much of the focus is placed on building and fine-tuning Machine Learning models, often overlooking the crucial steps of obtaining and preprocessing data, especially when it comes from APIs.Data scientists typically receive data that is already clean and well-organized, often in Excel or CSV formats, thanks to the efforts of Data Engineers. This can lead to a disconnect, as the ability to acquire and prepare raw data is a vital skill for any data professional.This course is designed to bridge that gap by teaching you how to source data from any type of API using Python. You’ll learn how to handle situations where data isn’t readily available through APIs by employing Web Scraping techniques to gather the information you need directly from websites.Raw data often comes in messy formats that require significant cleaning and processing. Throughout this practical course, I will guide you through each step of this process using Python, leveraging powerful libraries such as Selenium, Requests, and Pandas.After exporting the cleaned data to CSV or Excel tables, we will delve into visualizing this data using Python’s graphical libraries. Beyond just obtaining and processing data, you’ll also master how to refactor your code into reusable functions and custom modules, ensuring that your work is efficient and adaptable to future projects.
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