Data Science - 2nd Edition - ISBN: 9780128147610, 9780128147627

Data Science

2nd Edition

Concepts and Practice with RapidMiner

eBook ISBN: 9780128147627
Paperback ISBN: 9780128147610
Imprint: Morgan Kaufmann
Published Date: 1st November 2018
Page Count: 550
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Data science is about finding useful patterns in data. Rapid expansion in the volume of information collected by organizations entails a critical need for a framework and toolset to analyze and extract meaningful knowledge from data. Data science offers a set of techniques to uncover hidden patterns and relationships in data, in order to aid decision-making. Data Science presents the basic concepts behind many data science techniques in an easy to follow manner and prepares anyone with a basic grasp of mathematics to implement these techniques in their business, without the need to write programming code. The book uses an open source, GUI based data science tool to illustrate the concepts so that readers can follow the concepts and implement data science algorithms in parallel. The tool is open source, which means that learning data science with this tool is virtually cost free. The content and practical use cases described in this book are geared towards business and analytics professionals who use data. The reader of the book will acquire a comprehensive understanding of different data science techniques, and be prepared to select the right technique for a given data problem and to create a general purpose analytics process

Key Features

  • New edition, containing fully updated content
  • 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


Business and analytics professionals who use data in everyday work settings: Data analysts, business intelligence, data warehousing, business analysts, IT leadership teams, finance and sales operations; students and instructors of data science courses; users of RapidMiner

Table of Contents

  1. Introduction
    2. Data Science Process
    3. Data Exploration
    4. Classification
    5. Deep Learning
    6. Regression Methods
    7. Association Analysis
    8. Recommendation Engines
    9. Clustering
    10. Text Mining (renamed to: Natural Language Processing)
    11. Time Series Forecasting
    12. Anomaly Detection
    13. Feature Selection
    14. Model Evaluation
    15. Efficient Model Execution
    16. Getting Started with RapidMiner


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© Morgan Kaufmann 2019
Morgan Kaufmann
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