Introduction to Data Analytics
What is Data Analytics?
Data Analytics is the process of extracting insights from data. AI/ML and data analytics both depend on clean, organized data. This section covers data pipelines (collecting and transforming data) and analytics (finding patterns and making decisions).
Introduction to Data Analytics Video
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Data Pipelines and ETL
Data must be in a format usable by analytics tools and AI algorithms. ETL (Extract, Transform, Load) processes handle this:
- Extract: Get data from various sources
- Transform: Convert to a consistent, usable format
- Load: Put into a destination system like a data warehouse
Data pipelines automate ETL to make it efficient and repeatable. AWS provides integrated services for building custom pipelines.
Data Analytics Use Cases
Analysts transform raw historical data to uncover insights and trends. Common applications include:
- Loan companies explaining lending decisions
- Medical researchers analyzing clinical trial data
- Insurance companies creating transparent risk models for regulators
