Data Quality Analyst: Validation, Debugging, and Monitoring

Build practical data quality skills that help you assess datasets, surface risks, and verify that data is accurate, complete, and usable. In this course, you’ll develop hands-on experience used in roles such as data analyst, operations analyst, reporting analyst, data quality analyst, and business analyst. You’ll practice profiling datasets, reviewing summary statistics, identifying common data quality issues, and applying validation techniques to catch problems before they affect reporting, analysis, or business decisions. This is a non-traditional, skill-based learning experience organized around real workplace tasks instead of a fixed lecture sequence. It’s designed to reflect responsibilities you may see in job descriptions, from running data quality checks and flagging capture errors to reconciling records across systems and supporting data integrity efforts. You can personalize your path based on what you already know, focus on the skills you need most, and skip content when it’s not necessary. The course curates high-quality lessons from expert instructors, selecting the strongest content for each skill so you can build practical, career-relevant data quality experience. By the end, you’ll be able to generate data profiling reports, use descriptive statistics to assess dataset characteristics, run predefined validation checks and scripts to identify quality issues, flag common capture errors, and perform basic data reconciliation to verify consistency across systems. This course is a strong fit if you already have basic experience working with datasets, spreadsheets, reporting, or data analysis tools.

Build practical data quality and monitoring skills that help you assess source systems, prepare data for ingestion, and diagnose quality issues across pipelines. In this course, you’ll develop hands-on experience used in roles such as data analyst, analytics engineer, data engineer, business intelligence analyst, and data quality analyst. You’ll work on evaluating source data for availability, structure, and quality, then apply parsing and transformation logic to harmonize data from different formats so it can move more reliably into downstream workflows. This is a non-traditional, skill-based learning experience organized around real workplace tasks instead of a fixed lecture sequence. It’s designed to reflect responsibilities you may see in job descriptions, from reviewing source readiness and preparing ingestion logic to investigating pipeline failures and recommending remediation steps. You can personalize your path based on what you already know, focus on the skills you need most, and skip content when it’s not necessary. The course curates high-quality lessons from expert instructors, selecting the strongest content for each skill so you can build practical, career-relevant data quality experience. By the end, you’ll be able to assess source systems for ingestion readiness, implement parsing and transformation logic for structured data ingestion, analyze data profiles and pipeline outputs to identify quality issues, and perform root cause analysis by tracing data lineage to diagnose and explain failures. This course is a strong fit if you already have basic experience with data workflows, SQL, ETL concepts, or working with structured datasets.