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...
- 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
Table of contents
- Cover image
- Title page
- Table of Contents
- Copyright
- Dedication
- Foreword
- Preface
- Acknowledgments
- Chapter 1. Introduction
- Chapter 2. Data Science Process
- Chapter 3. Data Exploration
- Chapter 4. Classification
- Chapter 5. Regression Methods
- Chapter 6. Association Analysis
- Chapter 7. Clustering
- Chapter 8. Model Evaluation
- Chapter 9. Text Mining
- Chapter 10. Deep Learning
- Chapter 11. Recommendation Engines
- Chapter 12. Time Series Forecasting
- Chapter 13. Anomaly Detection
- Chapter 14. Feature Selection
- Chapter 15. Getting Started with RapidMiner
- Comparison of Data Science Algorithms
- About the Authors
- Index
- Praise
Product information
- Title: Data Science, 2nd Edition
- Author(s):
- Release date: November 2018
- Publisher(s): Morgan Kaufmann
- ISBN: 9780128147627
You might also like
book
Data Science Bookcamp
Learn data science with Python by building five real-world projects! Experiment with card game predictions, tracking …
book
Data Science: The Hard Parts
This practical guide provides a collection of techniques and best practices that are generally overlooked in …
book
Doing Data Science
Now that people are aware that data can make the difference in an election or a …
book
Principles of Data Science - Second Edition
Learn the techniques and math you need to start making sense of your data Key Features …