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Autor(en): 
  • Usman Qamar
  • Muhammad Summair Raza
  • Data Science Concepts and Techniques with Applications 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   i.d.R. innert 5-10 Tagen versandfertig
    Veröffentlichung:  April 2023  
    Genre:  EDV / Informatik 
     
    Data Mining / DataAnalytics / DataScience / DeepLearning / Frequentpatternmining / python / R / regressionanalysis
    ISBN:  9783031174414 
    EAN-Code: 
    9783031174414 
    Verlag:  Springer 
    Einband:  Kartoniert  
    Sprache:  English  
    Dimensionen:  H 235 mm / B 155 mm / D 27 mm 
    Gewicht:  750 gr 
    Seiten:  500 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    This textbook comprehensively covers both fundamental and advanced topics related to data science. Data science is an umbrella term that encompasses data analytics, data mining, machine learning, and several other related disciplines. The chapters of this book are organized into three parts: The first part (chapters 1 to 3) is a general introduction to data science. Starting from the basic concepts, the book will highlight the types of data, its use, its importance and issues that are normally faced in data analytics, followed by presentation of a wide range of applications and widely used techniques in data science. The second part, which has been updated and considerably extended compared to the first edition, is devoted to various techniques and tools applied in data science. Its chapters 4 to 10 detail data pre-processing, classification, clustering, text mining, deep learning, frequent pattern mining, and regression analysis. Eventually, the third part (chapters 11 and 12) present a brief introduction to Python and R, the two main data science programming languages, and shows in a completely new chapter practical data science in the WEKA (Waikato Environment for Knowledge Analysis), an open-source tool for performing different machine learning and data mining tasks. An appendix explaining the basic mathematical concepts of data science completes the book. This textbook is suitable for advanced undergraduate and graduate students as well as for industrial practitioners who carry out research in data science. They both will not only benefit from the comprehensive presentation of important topics, but also from the many application examples and the comprehensive list of further readings, which point to additional publications providing more in-depth research results or provide sources for a more detailed description of related topics.
    "This book delivers a systematic, carefully thoughtful material on Data Science." from the Foreword by Witold Pedrycz, U Alberta, Canada.

      



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