Data Ingestion with Python

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Video: .MP4, AVC, 1280x720, 30 fps | Audio: English, AAC, 44.1 KHz, 2 Ch | Duration: 1h 25m | 211 MB
Instructor: Miki Tebeka

A sizable portion of a data scientist's day is often spent fetching and cleaning the data they need to train their algorithms. In this course, learn how to use Python tools and techniques to get the relevant, high-quality data you need. Instructor Miki Tebeka covers reading files, including how to work with CSV, XML, and JSON files. He also discusses calling APIs, web scraping (and why it should be a last resort), and validating and cleaning data. Plus, discover how to establish and monitor key performance indicators (KPIs) that help you monitor your data pipeline.

Topics include:

Why is data ingestion important?
Working in CSV, XML, and Parquet/Avro/ORC
Working with JSON
Making HTTP calls
Using the Scrapy framework to write a scraping system
What should be in the schema?
Working with relational, key-value, and document databases
Troubleshooting data
Monitoring KPIs
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