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Chapter 1 - Introduction
Pages 1-18 - Book chapterAbstract only
Chapter 2 - Data Science Process
Pages 19-37 - Book chapterAbstract only
Chapter 3 - Data Exploration
Pages 39-64 - Book chapterAbstract only
Chapter 4 - Classification
Pages 65-163 - Book chapterAbstract only
Chapter 5 - Regression Methods
Pages 165-197 - Book chapterAbstract only
Chapter 6 - Association Analysis
Pages 199-220 - Book chapterAbstract only
Chapter 7 - Clustering
Pages 221-261 - Book chapterAbstract only
Chapter 8 - Model Evaluation
Pages 263-279 - Book chapterAbstract only
Chapter 9 - Text Mining
Pages 281-305 - Book chapterAbstract only
Chapter 10 - Deep Learning
Pages 307-342 - Book chapterAbstract only
Chapter 11 - Recommendation Engines
Pages 343-394 - Book chapterAbstract only
Chapter 12 - Time Series Forecasting
Pages 395-445 - Book chapterAbstract only
Chapter 13 - Anomaly Detection
Pages 447-465 - Book chapterAbstract only
Chapter 14 - Feature Selection
Pages 467-490 - Book chapterAbstract only
Chapter 15 - Getting Started with RapidMiner
Pages 491-521 - Book chapterNo access
Comparison of Data Science Algorithms
Pages 523-529 - Book chapterNo access
About the Authors
Page 531 - Book chapterNo access
Index
Pages 533-543 - 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:
- Gain the necessary knowledge of different data science techniques to extract value from data.
- Master the concepts and inner workings of 30 commonly used powerful data science algorithms.
- 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:
- Gain the necessary knowledge of different data science techniques to extract value from data.
- Master the concepts and inner workings of 30 commonly used powerful data science algorithms.
- 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