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Data Analytics at Meripustak

Data Analytics by Maheshwari, McGraw Hill

Books from same Author: Maheshwari

Books from same Publisher: McGraw Hill

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  • General Information  
    Author(s)Maheshwari
    PublisherMcGraw Hill
    Edition1st Edition
    ISBN9789352604180
    Pages272
    BindingPaperback
    LanguageEnglish
    Publish YearJanuary 2017

    Description

    McGraw Hill Data Analytics by Maheshwari

    Big Data Analytics
    This book fills the need for a concise and conversational book on the growing field of Data Analytics and Big Data. Easy to read and informative, this lucid book covers everything important, with concrete examples, and invites the reader to join this field. 

    This book is designed to provide a student with the intuition behind this evolving area, along with a solid toolset of the major data mining techniques and platforms. Students across a variety of academic disciplines, including business, computer science, statistics, engineering, and others attracted to the idea of discovering new insights and ideas from data will enjoy this book. The chapters in the book are organized for a typical one-semester course.

    •  Every chapter begins with insightful case-lets from real-world stories invigorating interest of reader 
    •  Provides a running case study across the chapters as exercises
    •  Shows clear learning objectives, review questions, and objective type questions
    •  Covers end-to-end data processing chain, from generation of data to the consumption of data
    •  Covers data mining, web mining, text mining, social analytics, and more
    •  Provides easy tutorials for R Programming & Weka; Primers on Statistics, and Data Modeling included in the book

    About Author:
    Anil Maheshwari, Professor of Management Information Systems and Director of Centre for Data Analytics, Maharishi
    University of Management, Fairfield, Iowa, U.S.A

    Table of Content
    1.  Wholeness of Data Analytics
    2.  Business Intelligence Concepts & Application
    3.  Data Warehousing
    4.  Data Mining
    5.  Data Visualization
    6.  Decision Trees
    7.  Regression
    8.  Artificial Neural Networks
    9.  Cluster Analysis
    10. Association Rule Mining
    11. Text Mining
    12. Naïve-Bayes Analysis
    13. Support Vector Machines
    14. Web Mining
    15. Social Network Analysis
    16. Big Data 
    17. Data Modeling Primer
    18. Statistics Primer
    19. Data Science Careers and Additional Case Studies


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