Bad data is everywhere - causing broken dashboards, misleading insights, and costly business mistakes. But data quality isn't just about cleaning up messes; it's about preventing them before they happen.

In this course, you’ll learn how to systematically prevent, diagnose, and resolve data quality issues. Whether you're constantly fixing reports, debugging pipelines, or advocating for data governance, this course gives you the tools to take control of your data and ensure its reliability.

What You’ll Learn:

✅ Identify & Prevent Bad Data – Analyze the root causes of data issues and design proactive solutions to stop them before they spread.

✅ Diagnose & Debug with SQL – Develop a structured approach to finding and fixing data anomalies using SQL and debugging best practices.

✅ Automate Quality Checks – Build automated alerts and implement best practices to maintain reliable, high-quality datasets.

✅ Secure Stakeholder Buy-In – Learn how to make a compelling case for prioritizing data quality and get leadership support for governance initiatives.

By the End of This Course, You’ll Be Able To:

🚀 Assess the health of your data and pinpoint weak spots in your data lifecycle.

🔍 Investigate & Fix data quality issues with confidence, reducing time spent firefighting.

⚙️ Implement Automation to catch and prevent errors before they impact decision-making.

🤝 Advocate for Change and drive a culture of data quality in your team and company.

This course is designed for data analysts, data scientists, and data engineers who want to level up their foundational data skills and ensure their insights are built on trustworthy, high-quality data. If you’re ready to stop firefighting bad data and start preventing it, this course is for you.