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CodeSignal

Evaluating and Improving Data Quality

via CodeSignal

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Overview

You will learn how to identify common data quality issues and choose practical actions to improve trust in data. You will focus on missing values, duplicates, invalid entries, inconsistent formats, outdated records, irrelevant fields, and unreliable sources. You will also learn how cleaning, transformation, governance, documentation, and data culture help make data more dependable.

Syllabus

  • Unit 1: Identifying Data Quality Issues
    • Identify Data Quality Issues
    • Match Data Issues To Categories
    • Complete Data Quality Statements
  • Unit 2: Data Validity and Consistency
    • Checking Valid and Invalid Data
    • Complete The Consistency Statements
    • Match Data Consistency Issues
  • Unit 3: Handling Data Issues
    • Missing Data for Analysis
    • Finding Duplicate Records
    • Data Quality Decisions
  • Unit 4: Building Data Trust
    • Match Data Quality Methods
    • Applying the Five Pillars of Trust
    • Choosing Governance Controls
  • Unit 5: Final Data Readiness Review
    • Complete Data Readiness Statements.
    • Data Readiness Review
    • Match Scenarios And Priority Fixes

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