AI in the Flow of Data Processing: 5 Days to Cleaner Data

AI and analytics are only as reliable as the data behind them, but most real-world datasets are messy, inconsistent, and risky to use as-is. In this hands-on, five-day data challenge, you tackle one concrete data task per day, working through a realistic dataset as data professionals do. Using the COAT framework—Consistent, Organized, Accurate, Trustworthy—discover the essentials of audit data quality, clean inconsistencies, restructure tables, and apply validation checks with support from AI tools. Each day of the challenge is designed to help you produce a tangible output, building toward a final AI-ready dataset you can trust. By the end of this course, you’ll be equipped with a repeatable, practical framework to prepare data for reliable analytics and AI outcomes.

Learning objectives
Audit a messy dataset and identify data quality and AI risk areas.
Apply the COAT framework to systematically improve data quality.
Clean and standardize inconsistent data using formulas and AI assistance.
Restructure raw data into an AI‑ready table suitable for analytics and automation.
Validate, document, and deliver a trusted dataset ready for AI use.