The Foundation of All Models
1. Data Types & Structures
Understanding Numerical, Categorical, and Temporal data formats.
Numerical
- Discrete (Integers)
- Continuous (Floats)
Categorical
- Nominal (Gender)
- Ordinal (Rating)
Temporal
- Timestamps
- Intervals
Structural
- JSON / Objects
- Arrays / Lists
Pro-Tip for ML Readiness
Always transform Categorical data into Numerical format (One-Hot Encoding or Label Encoding) so your ML models can process them mathematically.
Why this matters
In the real world, data is messy, incomplete, and buried in multiple locations. Data Engineering is the "plumbing" that builds reliable roads for your AI to travel on.
The Architecture
Every pixel of an AI model comes from original training data. If your engineering is flawed, your model will eventually collapse due to "Technical Debt" or "Data Drift".