Cleaning data for effective data science : doing the other 80% of the work with Python, R, and command-line tools /

A comprehensive guide for data scientists to master effective data cleaning tools and techniques Key Features Master data cleaning techniques in a language-agnostic manner Learn from intriguing hands-on examples from numerous domains, such as biology, weather data, demographics, physics, time series...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Mertz, David (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: [S.l.] : Packt Publishing Limited, 2021.
Temas:
Python (Programming language).
Computational biology > Methods.
Database management.
Data integrity.
Python (Computer program language)
R (Computer program language)
Computational Biology > methods
Data Analysis
Data Accuracy
Bases de données > Gestion.
Intégrité des données.
Python (Langage de programmation)
R (Langage de programmation)
Qualité des données.
Database design & theory.
Data capture & analysis.
Mathematical theory of computation.
Machine learning.
Information architecture.
Computers > Data Processing.
Computers > Machine Theory.
Computers > Data Modeling & Design.
Computational biology.
Fulltext.
Internet Resources.
Methods (Music)
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • Table of ContentsData Ingestion - Tabular FormatsData Ingestion
  • Hierarchical FormatsData Ingestion
  • Repurposing Data SourcesThe Vicissitudes of Error
  • Anomaly DetectionThe Vicissitudes of Error
  • Data QualityRectification and Creation
  • Value ImputationRectification and Creation
  • Feature EngineeringAncillary Matters
  • Closure/Glossary.

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