Models and charts are only as good as the data behind them, and most of that data does not arrive as a tidy CSV. It lives in databases, behind APIs, or on web pages. This module covers the three main ways data scientists get hold of it.
You'll start with SQL: selecting, filtering, joining, and aggregating data in relational databases, a skill used in almost every data role. Then you'll move to APIs, requesting live data over HTTP and reading JSON responses, and finish with web scraping for data that has no API, using Python, Requests, and Beautiful Soup.
With data in hand, you'll be ready for the next module, Data Cleaning & Exploratory Analysis, where you'll tidy it up and start looking for patterns.
A quick look at what SQL is, how tables and queries work, and why it remains the standard language for relational data.
A complete walk through creating tables, writing queries, joins, aggregates, and designing a database from scratch.
What APIs are, how requests and responses work, authentication, and how to pull live data from real services.
Fetching a web page with Requests and extracting the data you need with Beautiful Soup when no API is available.