A practical overview of the AI-Assisted book series for data analysts: one book for AI-assisted analytical work and one for AI-assisted interview practice.

A practical 10-point AI quality-control checklist for data analysts covering problem framing, grain, joins, totals, edge cases, assumptions and recommendations.

Use AI to broaden root-cause hypotheses, then test them against the data. This practical framework keeps root-cause analysis evidence-driven.

AI-assisted and AI-dependent analysts may use the same tools, but their workflows differ. Learn the practical signs of ownership, verification and dependency.

A practical 2026 AI-assisted data analyst workflow, showing where AI fits from problem framing and data inspection through validation and stakeholder communication.

A practical AI-assisted data analyst workflow from business question to decision, including problem framing, data inspection, AI acceleration, validation and communication.

Seven data analyst tasks are good candidates for AI assistance. This guide shows where AI saves time, what to verify, and where analysts should keep full ownership.

A complete AI-assisted analyst workflow from business question to decision: frame, prepare, build, verify, explain and systematize.

Use AI with Python for data analysis without losing control of transformations. Check data types, filters, missing values, joins and output shape.