Data quality for analytics using SAS
Analytics offers many capabilities and options to measure and improve data quality, and SAS is perfectly suited to these tasks. Gerhard Svolba's Data Quality for Analytics Using SAS focuses on selecting the right data sources and ensuring data quantity, relevancy, and completeness. The book is...
| Autor principal: | |
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| Autores Corporativos: | , |
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Cary, N.C. :
SAS Institute
2012
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| Edición: | 1st edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009628492306719 |
Tabla de Contenidos:
- Introductory case studies
- Definition and scope of data quality for analytics
- Data availability
- Data quantity
- Data completeness
- Data correctness
- Predictive modeling
- Analytics for data quality
- Process considerations for data quality
- Profiling and imputation of missing values
- Profiling and replacement of missing data in a time series
- Data quality control across related tables
- Data quality with analytics
- Data quality profiling and improvement with SAS analytic tools
- Introduction to simulation studies
- Simulating the consequences of poor data quality for predictive modeling
- Influence of data quality and data availability on model quality in predictive modeling
- Influence of data completeness on model quality in predictive modeling
- Influence of data correctness on model quality in predictive modeling
- Simulating the consequences of poor data quality in time series forecasting
- Consequences of data quantity and data completeness in time series forecasting
- Consequences of random disturbances in time series data
- Consequences of systematic disturbances in time series data.
