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.

您将学到什么
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.
您将获得的技能
- Data Integrity
- Quality Improvement
- Data Pipelines
- Extract, Transform, Load
- Automation
- Anomaly Detection
- Trend Analysis
- Stored Procedure
- Quality Assessment
- Root Cause Analysis
- Continuous Monitoring
- Key Performance Indicators (KPIs)
- Data Cleansing
- Quality Assurance
- Data Validation
- Data Processing
- SQL
- Data Quality
- Data Management
- Performance Analysis
- 技能部分已折叠。显示 10 项技能,共 20 项。
要了解的详细信息
了解顶级公司的员工如何掌握热门技能

该课程共有3个模块
Learners will master the quantitative measurement of critical data quality dimensions through systematic calculation methods that provide objective assessment of data reliability.
涵盖的内容
3个视频1篇阅读材料2个作业
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.
涵盖的内容
3个视频2篇阅读材料2个作业
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.
涵盖的内容
3个视频1篇阅读材料2个作业1个非评分实验室
位教师

提供方
人们为什么选择 Coursera 来帮助自己实现职业发展

Felipe M.

Jennifer J.

Larry W.

Chaitanya A.
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