Data Quality Masterclass – The Complete Course Syllabus

Overview: This comprehensive course provides a structured, hands-on approach to mastering data quality fundamentals and practical techniques. Designed for beginners, it spans approximately 6 hours of content, guiding you through key stages of data quality management—from defining dimensions to implementing governance frameworks. Each module combines theory with real-world applications, ensuring you can assess, clean, and govern data effectively in professional environments.

Module 1: Introduction to Data Quality Management

Estimated time: 0.5 hours

• What is Data Quality and why it matters
• Overview of the data quality lifecycle

Module 2: Data Quality Dimensions & Rules

Estimated time: 0.75 hours

• Deep dive into accuracy, completeness, consistency, and timeliness
• Defining and implementing data quality rules for validation

Module 3: Data Profiling Techniques

Estimated time: 0.75 hours

• Profiling datasets to surface anomalies and patterns
• Tools and methods for automated data assessment

Module 4: Parsing & Standardization

Estimated time: 0.75 hours

• Parsing free-form data into structured formats
• Standardizing values (dates, addresses, codes) for consistency

Module 5: Identity Resolution & Record Linkage

Estimated time: 0.75 hours

• Matching algorithms for deduplication and entity resolution
• Building linkage workflows for large datasets

Module 6: Data Cleansing & Enhancement

Estimated time: 0.5 hours

• Applying transformation logic and enrichment services
• Handling missing data, outliers, and normalization

Module 7: Data Quality Roles & Tools

Estimated time: 0.75 hours

• 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

Estimated time: 0.75 hours

• Designing end-to-end data quality processes and governance
• Industry best practices and continuous monitoring strategies

Prerequisites

• Familiarity with basic data concepts
• Basic comfort with Excel for data manipulation
• Fundamental understanding of SQL is helpful but not required

What You'll Be Able to Do After

• Define data quality and explain its impact on business outcomes
• Apply key data quality dimensions to assess real datasets
• Implement data profiling, cleansing, and standardization techniques
• Use identity resolution methods to deduplicate and link records
• Design data quality processes and define roles within governance frameworks