01
ETL Fundamentals
Design extraction, transformation, and loading pipelines that are easy to maintain.
Idempotency, schema evolution, partitioning, and retry logic — the principles every reliable pipeline must follow before adding any AI layer.
Data Pipelines with Python and AI
Build reliable ETL pipelines and automate data cleaning with AI assistance.
What this skill covers
Overview
What this skill covers
OverviewA practical skill for data engineers and analysts: design extraction and transformation pipelines in Python, use AI to detect anomalies and fix dirty data, and ship automated reports to stakeholders.
Steps & content
3 items
Steps & content
3 items02
AI-Assisted Data Cleaning
Use LLMs to detect anomalies, normalize messy fields, and classify ambiguous rows.
Practical patterns for calling AI APIs inside a pandas or Polars pipeline, batching requests efficiently, and logging every AI decision for auditability.
03
Automating Reports
Generate and deliver stakeholder reports from pipeline outputs — on schedule, zero manual work.
Combine data summaries with an AI narrative layer, render to PDF or Markdown, and push to Slack or email automatically after each pipeline run.
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