Moyens d'évaluation :
- Quiz pré-formation de vérification des connaissances (si applicable)
- Évaluations formatives pendant la formation, à travers les travaux pratiques réalisés sur les labs à l’issue de chaque module, QCM, mises en situation…
- Complétion par chaque participant d’un questionnaire et/ou questionnaire de positionnement en amont et à l’issue de la formation pour validation de l’acquisition des compétences
Who should attend
• Data Analysts responsible for data quality using QualityStage • Data Quality Architects • Data Cleansing Developers
Prerequisites
Participants should have: • Familiarity with the Windows operating system • Familiarity with a text editor Helpful, but not required, would be some understanding of elementary statistics principles such as weighted averages and probability.
Course Content
This course teaches how to build QualityStage parallel jobs that investigate, standardize, match, and consolidate data records. Students will gain experience by building an application that combines customer data from three source systems into a single master customer record.
Moyens Pédagogiques :