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    Practical Data Science for Information Professionals

    Practical Data Science for Information Professionals by Stuart, David;

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    Rövid leírás:

    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.

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    Hosszú leírás:



    Practical
    Data Science for Information Professionals
    provides an accessible introduction to
    a potentially complex field, providing readers with an overview of data science
    and a framework for its application. It provides detailed examples and analysis
    on real data sets to explore the basics of the subject in three principle
    areas: clustering and social network analysis; predictions and forecasts; and
    text analysis and mining.



    As well as highlighting a wealth of user-friendly data science
    tools, the book also includes some example code in two of the most popular
    programming languages (R and Python) to demonstrate the ease with
    which the information professional can move beyond the graphical user interface
    and achieve significant analysis with just a few lines of code.



     After reading, readers will
    understand:



    ?     
    the growing importance of data science



    ?     
    the role of the information professional in
    data science



    ?     
    some of the most important tools and methods
    that information professionals can use.



    Bringing together the growing importance of data science and the
    increasing role of information professionals in the management and use of data,
    Practical Data Science for Information
    Professionals
    will provide a practical introduction to the topic
    specifically designed for the information community. It
    will appeal to librarians and information professionals all around the world,
    from large academic libraries to small research libraries. By focusing on the
    application of open source software, it aims to reduce barriers for readers to
    use the lessons learned within.





    'If libraries and librarians are to be serious about the ?I? in LIS, then analysing data to find
    meaning for our customers will be a core component of the service offering. David Stuart?s
    book is an excellent entry point to the discipline.'

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    Tartalomjegyzék:

    Contents

    Figures
    Tables
    Boxes
    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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