Data Science: Concepts and Practice

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

New edition, containing fully updated contentPresents simple explanations for over twenty powerful data science techniquesEnables the practical use of data science algorithms without the need for programmingDemonstrates processes with practical use casesIntroduces each algorithm or technique and explains the workings of a data science algorithm in plain languageDescribes the commonly used setup options for the open source tool 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