Data Quality for Data Analysts

Course overview

Participants will acquire skills to ensure high data quality as a foundation for meaningful analyses and informed decisions. Central aspects such as accuracy, completeness, consistency, and timeliness of data will be the focus. Practical examples will demonstrate how data can be systematically checked, cleaned, and validated using appropriate methods and tools. Contents Central aspects of data quality: accuracy, completeness, consistency, and timeliness Systematic checking, cleaning, and validation of data Analysis of databases using SQL to uncover sources of errors Creation of structured queries for quality control Visual presentation of data quality metrics with Power BI Design of clear dashboards to visualize the state and need for improvement of the data foundation Workflows for sustainable assurance of data quality in analytical processes Creation of a reliable data foundation through automated checking mechanisms, documented standards, and clear validation rules

Upcoming dates

• 2026-09-06T22:00:00.000Z · 2027-03-15T23:00:00.000Z · Berlin · Hybrid Learning
• 2026-09-06T22:00:00.000Z · 2027-03-15T23:00:00.000Z · München · Hybrid Learning
• 2026-09-06T22:00:00.000Z · 2027-03-15T23:00:00.000Z · Berlin · Hybrid Learning
• 2026-09-06T22:00:00.000Z · 2027-03-15T23:00:00.000Z · Frankfurt am Main · Hybrid Learning
• 2026-09-06T22:00:00.000Z · 2027-03-15T23:00:00.000Z · Stuttgart · Hybrid Learning