Master the critical skills for ensuring data reliability and building self-healing data systems. This course transforms your approach to data quality from reactive firefighting to proactive engineering driven reliability.
Improve Data Quality and Automate Errors
Gain insight into a topic and learn the fundamentals.
Advanced level
Recommended experience
3 hours to complete
Flexible schedule
Learn at your own pace
What you'll learn
Data quality measurement uses standardized quantitative methods to objectively assess reliability across all critical data dimensions.
Proactive monitoring of quality trends enables early intervention and systematic remediation before issues cascade to downstream systems.
Self-healing data systems with automated error recovery reduce operational overhead while maintaining data integrity at scale.
Quality assurance is most effective when built into the data pipeline architecture rather than applied as an afterthought.
Details to know
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Recently updated!
February 2026
Taught in English
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There are 3 modules in this course
Learners will master the quantitative measurement of critical data quality dimensions through systematic calculation methods that provide objective assessment of data reliability.
What's included
3 videos1 reading2 assignments
Learners will master the evaluation of quality key performance indicators over time and develop actionable remediation strategies that prevent quality degradation before it impacts business operations.
What's included
3 videos2 readings2 assignments
Learners will create resilient automated SQL routines that detect, quarantine, and reprocess data errors without manual intervention, building self-healing data systems at enterprise scale.
What's included
3 videos1 reading2 assignments1 ungraded lab
Instructor
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