Practical data science for information professionals /

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The growing importance of data science, and the increasing role of information professionals in the management and use of data, are brought together in Practical Data Science for Information Professionals to provide a practical introduction specifically designed for information professionals. Data s...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Stuart, David (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: London : Facet, 2020.
Temas:
Data curation in libraries.
Data services librarians.
Database management in libraries.
Research > Data processing > Management.
Édition de contenu dans les bibliothèques.
Bibliothécaires de données.
Base de données > Gestion, dans les bibliothèques.
Data curation in libraries
Data services librarians
Database management in libraries
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Intro
  • Title page
  • Contents
  • Preface
  • 1 What is data science?
  • Data, information, knowledge, wisdom
  • Data everywhere
  • The data deserts
  • Data science
  • The potential of data science
  • From research data services to data science in libraries
  • Programming in libraries
  • Programming in this book
  • The structure of this book
  • 2 Little data, big data
  • Big data
  • Data formats
  • Standalone files
  • Application programming interfaces
  • Unstructured data
  • Data sources
  • Data licences
  • 3 The process of data science
  • Modelling the data science process
  • Frame the problem
  • Collect data
  • Transform and clean data
  • Analyse data
  • Visualise and communicate data
  • Frame a new problem
  • 4 Tools for data analysis
  • Finding tools
  • Software for data science
  • Programming for data science
  • 5 Clustering and social network analysis
  • Network graphs
  • Graph terminology
  • Network matrix
  • Visualisation
  • Network analysis
  • 6 Predictions and forecasts
  • Predictions and forecasts beyond data science
  • Predictions in a world of (limited) data
  • Predicting and forecasting for information professionals
  • Statistical methodologies
  • 7 Text analysis and mining
  • Text analysis and mining, and information professionals
  • Natural language processing
  • Keywords and n-grams
  • 8 The future of data science and information professionals
  • Eight challenges to data science
  • Ten steps to data science librarianship
  • The final word: play
  • References
  • Appendix
  • Programming concepts for data science
  • Variables, data types and other classes
  • Import libraries
  • Functions and methods
  • Loops and conditionals
  • Final words of advice
  • Further reading
  • Index

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