Data Quality: Analytics and Serving

linkedin.com/learning/data-quality-analytics-and-serving

3,604 learners | 1h 1m | Advanced | Released Jun 2025 | 4.9/5 stars

Hands-on course for identifying, analyzing, and resolving data quality issues using root cause analysis and chaos engineering. Learn SQL queries and dbt tests to safeguard data pipelines and ensure dataset reliability — all in a GitHub Codespaces sandbox environment.

What You'll Learn

  • Identify and analyze data quality issues systematically
  • Use SQL queries and dbt tests for data validation
  • Conduct downstream pipeline investigations
  • Uncover business logic that affects data quality
  • Apply SBAR communication strategies for stakeholder reporting
  • Implement fixes through testing and code modifications

Course Sections

  1. Project setup and infrastructure overview
  2. Issue scoping and documentation
  3. Data profiling and replication exercises
  4. Pipeline investigation techniques
  5. Test implementation and code fixes
  6. Stakeholder communication strategies

Integrated with GitHub Codespaces — no local setup required.

1 # Data Quality: Analytics and Serving
2
3 [linkedin.com/learning/data-quality-analytics-and-serving](https://www.linkedin.com/learning/data-quality-analytics-and-serving)
4
5 **3,604 learners** | 1h 1m | Advanced | Released Jun 2025 | 4.9/5 stars
6
7 ---
8
9 Hands-on course for identifying, analyzing, and resolving data quality issues using root cause analysis and chaos engineering. Learn SQL queries and dbt tests to safeguard data pipelines and ensure dataset reliability — all in a GitHub Codespaces sandbox environment.
10
11 ## What You'll Learn
12
13 - Identify and analyze data quality issues systematically
14 - Use SQL queries and dbt tests for data validation
15 - Conduct downstream pipeline investigations
16 - Uncover business logic that affects data quality
17 - Apply SBAR communication strategies for stakeholder reporting
18 - Implement fixes through testing and code modifications
19
20 ## Course Sections
21
22 1. Project setup and infrastructure overview
23 2. Issue scoping and documentation
24 3. Data profiling and replication exercises
25 4. Pipeline investigation techniques
26 5. Test implementation and code fixes
27 6. Stakeholder communication strategies
28
29 ---
30
31 *Integrated with GitHub Codespaces — no local setup required.*

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