CHAPTER 01 / 06 · INPUT
Find the signal in the data.
Data foundations Applied AI Engineering
Before a model can answer a question, the underlying records need structure, checks and a written account of what changed.
Can another person reproduce the same starting point?
The same fictional records arrive in different shapes. Missing fields and odd values stay visible.
01 / THE PROCESS
From idea
to evidence.
Three connected moves explain the subject and the work that makes it reviewable.
A reproducible data notebook with clear assumptions.
- 01 / RAW INPUT
Shape the question
Name the outcome, choose useful fields and inspect the source before writing transformations.
WHAT YOU CAN SHOWA clear data dictionary - 02 / VALIDATE
Check the records
Use Python, SQL and simple statistics to find missing values, duplicates and surprising ranges.
WHAT YOU CAN SHOWA visible cleaning log - 03 / DOCUMENT
Make it reproducible
Record the choices so the same input can produce the same result for someone else.
WHAT YOU CAN SHOWA notebook with assumptions
THE PRACTICE
Make the work
inspectable.
The example is a way to explore the idea. Real project evidence should include what was tried, what was observed and what remains uncertain.
KEEP IN VIEW
What the visual
doesn’t prove.
A clean looking table can still contain biased or incomplete data. Keep unresolved issues visible.
ROLE CONNECTIONS
These directions use ideas from this subject within the Applied AI Engineering learning path.