Libro adottato a Politecnico di Milano, a.a. 2026/2027 · 4 canali
«Ihab – Xu Chu» è adottato per Data And Information Quality dal prof. Cinzia Cappiello (Computer Science and Engineering, Food Engineering, Geoinformatics Engineering e altri corsi – Politecnico di Milano).
Come lo indica il docente: Ihab F. Ilyas, Xu Chu, Data Cleaning, Association for Computing Machinery, New York, NY, United States, Anno edizione: 2019, ISBN: 978-1-4503-7152-0
Argomenti del programma: Poor data quality is related to data errors, inconsistencies or delays that often negatively affect the output of the processes (from business processes to pure computational process). Such issue is perceived as important in Data Analytics field since an adequate quality level of the input data increases the reliability and value of the obtained results.
Argomenti del programma: Poor data quality is related to data errors, inconsistencies or delays that often negatively affect the output of the processes (from business processes to pure computational process). Such issue is perceived as important in Data Analytics field since an adequate quality level of the input data increases the reliability and value of the obtained results.
Argomenti del programma: Poor data quality is related to data errors, inconsistencies or delays that often negatively affect the output of the processes (from business processes to pure computational process). Such issue is perceived as important in Data Analytics field since an adequate quality level of the input data increases the reliability and value of the obtained results.
Argomenti del programma: Poor data quality is related to data errors, inconsistencies or delays that often negatively affect the output of the processes (from business processes to pure computational process). Such issue is perceived as important in Data Analytics field since an adequate quality level of the input data increases the reliability and value of the obtained results.
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