Data Science

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...

• 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

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

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