Topics in Data Science with Practical Examples

Data Science, sometimes known as methods of processing and analyzing massive data sets (Big Data), is a rapidly evolving field. This book teaches important topics of the emerging data science by providing simple and practical examples in R language. Initial chapters are about data collection and management at large scale, and then data analytics and applying statistical and machine learning models on the collected data are discussed in rest of the book. Ten important topics in data science are explained in ten chapters of this book with practical examples in Oracle SQL, R, Hadoop, and MapReduce. The fundamental of data management such as relational database systems, data mining and distributed computing with practical examples of SQL and implementing Hadoop and MapReduce are detailed in chapters 1 to 3. Regression and statistical analysis, neural networks, support vector machines and machine learning are explained in simple language together with R programming examples, in chapter 4 to 7. Natural language processing, recommendation systems and analyzing social networks graphs are explained in chapters 8 to 10 of this book. Dr. Abdolreza Abhari, a professor of computer science department at Ryerson University, has collected the material of this book after many years of teaching Data Science. With the background in computer science dating back to before the invention of the world wide web, professor Abhari has extensive experience in analyzing web and social network data and creating database systems for the companies and industrial sectors in Europe and North America. His teaching area in academia includes database systems, distributed systems, and data science for graduate and undergraduate students. Although this book is written for professionals and graduated students who have a university or college degree, it is also useful for whoever considers working in the data science industry. Title Page 3 Copyright 4 Acknowledgment 5 Table of Contents 7 Chapter 1: Data Management 8 Chapter 2: Data Mining 44 Chapter 3: Massive Data Sets, Hadoop, and MapReduce 56 Chapter 4: Regression Analysis 77 Chapter 5: Neural Networks 106 Chapter 6: Machine Learning 123 Chapter 7: Recurrent Neural Networks 140 Chapter 8: Text Processing (Natural Language Processing) 152 Chapter 9: Recommendation Systems and Netflix Challenge 171 Chapter 10: Analyzing Social Graphs 184