Data Science in Practice

This 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

Chapter 1 Data Science: An Introduction

Part I Concepts
Chapter 2 Artificial Intelligence
Chapter 3 Machine Learning: A Concise Overview

Part II Application Domains
Chapter 4 Information Fusion
Chapter 5 Information Retrieval And Recommender Systems
Chapter 6 Business Intelligence

Part III Tools
Chapter 7 Data Privacy
Chapter 8 Visual Data Analysis
Chapter 9 Complex Data Analysis
Chapter 10 Big Data Programming With Apache Spark