Data Science in Practice

his book approaches big data, artificial intelligence, machine learning, and business intelligence through the lens of Data Science. We have grown accustomed to seeing these terms mentioned time and time again in the mainstream media. However, our understanding of what they actually mean often remains limited. This book provides a general overview of the terms and approaches used broadly in data science, and provides detailed information on the underlying theories, models, and application scenarios. Divided into three main parts, it addresses what data science is; how and where it is used; and how it can be implemented using modern open source software. The book offers an essential guide to modern data science for all students, practitioners, developers and managers seeking a deeper understanding of how various aspects of data science work, and of how they can be employed to gain a competitive advantage.

Table of Contents

1 Data Science: An Introduction

Part I Concepts

2 Artificial Intelligence

3 Machine Learning: A Concise Overview

Part II Application Domains

4 Information Fusion

5 Information Retrieval and Recommender Systems

6 Business Intelligence

Part III Tools

7 Data Privacy

8 Visual Data Analysis

9 Complex Data Analysis

10 Big Data Programming with Apache Spark