What will you in Data Quality Masterclass – The Complete Course
Define data quality and explain why high-quality data is critical for business outcomes
Apply key data quality dimensions (accuracy, completeness, consistency, timeliness) to assess datasets
Implement data profiling, parsing, and standardization techniques to clean raw data
Use identity resolution and record-linkage methods to merge and dedupe records effectively
Design and execute data cleansing and enhancement pipelines for continuous improvement
Establish a data quality management process, roles, and best practices for your organization
Program Overview
Module 1: Introduction to Data Quality Management
⏳ 30 minutes
What is Data Quality and why it matters
Overview of the data quality lifecycle
Module 2: Data Quality Dimensions & Rules
⏳ 45 minutes
Deep dive into accuracy, completeness, consistency, and timeliness
Defining and implementing data quality rules for validation
Module 3: Data Profiling Techniques
⏳ 45 minutes
Profiling datasets to surface anomalies and patterns
Tools and methods for automated data assessment
Module 4: Parsing & Standardization
⏳ 45 minutes
Parsing free-form data into structured formats
Standardizing values (dates, addresses, codes) for consistency
Module 5: Identity Resolution & Record Linkage
⏳ 45 minutes
Matching algorithms for deduplication and entity resolution
Building linkage workflows for large datasets
Module 6: Data Cleansing & Enhancement
⏳ 30 minutes
Applying transformation logic and enrichment services
Handling missing data, outliers, and normalization
Module 7: Data Quality Roles & Tools
⏳ 45 minutes
Defining organizational roles: data steward, data owner, and data engineer
Survey of leading data quality tools and platforms
Module 8: Data Quality Process & Best Practices
⏳ 45 minutes
Designing end-to-end data quality processes and governance
Industry best practices and continuous monitoring strategies
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Job Outlook
Data Quality Analyst and Data Steward roles rely on these techniques to ensure reliable reporting and analytics
Business Analysts and Data Engineers use data quality skills to maintain clean, trustworthy datasets across pipelines
In demand across industries—finance, healthcare, retail, and technology—for compliance, customer analytics, and AI readiness
Provides a foundation for advanced certifications (DAMA CDMP, IBM Data Quality) and leadership in data governance
Specification: Data Quality Masterclass – The Complete Course
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