Learn Data Quality Management from A to Z. Data Quality principles, techniques, AI methods, governance, best practices.
InstructorGeorge SmartsEditorial course preview
These 2 complementary views highlight concrete, legible examples from the course presentation.
This curriculum overview highlights the technical depth of the course by expanding Module 4 to show specific data quality techniques such as profiling, parsing, and cleansing.
This visual framework categorizes the target audience into four distinct personas—Data Consumers, Analysts, Scientists, and Managers—to demonstrate the course's broad relevance across organizational levels.
Free
Free
The curriculum provides a structured look at essential dimensions including accuracy, timeliness, completeness, uniqueness, and consistency.
Instruction includes practical applications of ChatGPT and Microsoft Copilot for detecting missing data, standardizing fields, and generating scripts.
One signal from late 2025 indicates a lack of specific technical depth regarding which features within enterprise tools correspond to particular data quality needs; the displayed update label does not prove this has been corrected.
One signal from mid-2026 suggests that certain practical resources or labs may require additional subscriptions beyond the initial course purchase; the displayed update label does not prove this has been corrected.
The curriculum aligns well with the stated goals of data professionals and beginners through its focus on industry roles and certifications.