- 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
Skill Level Intermediate
- [Miki] Algorithms govern our life. They trade the stock market, control our police patrolling and soon will drive our car. All of these algorithms are trained on data. Sometimes a lot of data. What surprises many people doing data science is that finding high quality and relevant data takes most of their time. Hi there, I'm Miki Tebeka and for more than 10 years I've been helping researchers become more productive. In this course, I'll show tips and tricks from my experience of getting the right kind of data into the hands of scientist. We'll cover many sources of data from files to APIs to databases. We'll also talk about validating and cleaning data and how to integrate data quality in your process. At the end of this course you'll be able to fit your algorithm with the data it needs no matter where it's residing.
Processing Text with Python Essential Trainingwith Kumaran Ponnambalam33m 31s Intermediate
Python: Data Analysis (2015)with Michele Vallisneri2h 16m Intermediate
Data Science Foundations: Python Scientific Stackwith Miki Tebeka3h 34m Intermediate
1. Data Ingestion Overview
2. Reading Files
3. Calling APIs
4. Web Scraping
6. Working with Databases
7. Troubleshooting Data
8. Data KPIs and Process
Next steps1m 1s
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