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
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Recommended experience
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.
Skills you'll gain
- Data Cleansing
- SQL
- Trend Analysis
- Data Validation
- Root Cause Analysis
- Data Integrity
- Continuous Monitoring
- Data Quality
- Data Pipelines
- Anomaly Detection
- Automation
- Stored Procedure
- Extract, Transform, Load
- Data Management
- Quality Improvement
- Performance Analysis
- Quality Assurance
- Key Performance Indicators (KPIs)
- Quality Assessment
- Data Processing
Details to know

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February 2026
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There are 3 modules in this course
Instructor

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Why people choose Coursera for their career

Felipe M.

Jennifer J.

Larry W.

Chaitanya A.

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