Cover for Data Science

Data Science

Concepts and Practice

Book • Second Edition2019

Authors:

Vijay Kotu and Bala Deshpande

Data Science

Concepts and Practice

Book • Second Edition2019

 

Cover for Data Science

Authors:

Vijay Kotu and Bala Deshpande

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Book description

Learn the basics of Data Science through an easy to understand conceptual framework and immediately practice using RapidMiner platform. Whether you are brand new to data science or ... read full description

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Table of contents

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  2. Book chapterAbstract only

    Chapter 1 - Introduction

    Pages 1-18

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    Chapter 2 - Data Science Process

    Pages 19-37

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    Chapter 3 - Data Exploration

    Pages 39-64

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    Chapter 4 - Classification

    Pages 65-163

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    Chapter 5 - Regression Methods

    Pages 165-197

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    Chapter 6 - Association Analysis

    Pages 199-220

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    Chapter 7 - Clustering

    Pages 221-261

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    Chapter 8 - Model Evaluation

    Pages 263-279

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    Chapter 9 - Text Mining

    Pages 281-305

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    Chapter 10 - Deep Learning

    Pages 307-342

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    Chapter 11 - Recommendation Engines

    Pages 343-394

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    Chapter 12 - Time Series Forecasting

    Pages 395-445

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    Chapter 13 - Anomaly Detection

    Pages 447-465

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    Chapter 14 - Feature Selection

    Pages 467-490

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    Chapter 15 - Getting Started with RapidMiner

    Pages 491-521

  17. Book chapterNo access

    Comparison of Data Science Algorithms

    Pages 523-529

  18. Book chapterNo access

    About the Authors

    Page 531

  19. Book chapterNo access

    Index

    Pages 533-543

  20. Book chapterNo access

    Praise

    Pages 545-548

About the book

Description

Learn the basics of Data Science through an easy to understand conceptual framework and immediately practice using RapidMiner platform. Whether you are brand new to data science or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions.

Data Science has become an essential tool to extract value from data for any organization that collects, stores and processes data as part of its operations. This book is ideal for business users, data analysts, business analysts, engineers, and analytics professionals and for anyone who works with data.

You’ll be able to:

  1. Gain the necessary knowledge of different data science techniques to extract value from data.
  2. Master the concepts and inner workings of 30 commonly used powerful data science algorithms.
  3. Implement step-by-step data science process using using RapidMiner, an open source GUI based data science platform

Data Science techniques covered: Exploratory data analysis, Visualization, Decision trees, Rule induction, k-nearest neighbors, Naïve Bayesian classifiers, Artificial neural networks, Deep learning, Support vector machines, Ensemble models, Random forests, Regression, Recommendation engines, Association analysis, K-Means and Density based clustering, Self organizing maps, Text mining, Time series forecasting, Anomaly detection, Feature selection and more...

Learn the basics of Data Science through an easy to understand conceptual framework and immediately practice using RapidMiner platform. Whether you are brand new to data science or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions.

Data Science has become an essential tool to extract value from data for any organization that collects, stores and processes data as part of its operations. This book is ideal for business users, data analysts, business analysts, engineers, and analytics professionals and for anyone who works with data.

You’ll be able to:

  1. Gain the necessary knowledge of different data science techniques to extract value from data.
  2. Master the concepts and inner workings of 30 commonly used powerful data science algorithms.
  3. Implement step-by-step data science process using using RapidMiner, an open source GUI based data science platform

Data Science techniques covered: Exploratory data analysis, Visualization, Decision trees, Rule induction, k-nearest neighbors, Naïve Bayesian classifiers, Artificial neural networks, Deep learning, Support vector machines, Ensemble models, Random forests, Regression, Recommendation engines, Association analysis, K-Means and Density based clustering, Self organizing maps, Text mining, Time series forecasting, Anomaly detection, Feature selection and more...

Key Features

  • Contains fully updated content on data science, including tactics on how to mine business data for information
  • Presents simple explanations for over twenty powerful data science techniques
  • Enables the practical use of data science algorithms without the need for programming
  • Demonstrates processes with practical use cases
  • Introduces each algorithm or technique and explains the workings of a data science algorithm in plain language
  • Describes the commonly used setup options for the open source tool RapidMiner
  • Contains fully updated content on data science, including tactics on how to mine business data for information
  • Presents simple explanations for over twenty powerful data science techniques
  • Enables the practical use of data science algorithms without the need for programming
  • Demonstrates processes with practical use cases
  • Introduces each algorithm or technique and explains the workings of a data science algorithm in plain language
  • Describes the commonly used setup options for the open source tool RapidMiner

Details

ISBN

978-0-12-814761-0

Language

English

Published

2019

Copyright

Copyright © 2019 Elsevier Inc. All rights reserved.

Imprint

Morgan Kaufmann

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Authors

Vijay Kotu

Bala Deshpande