Includes bibliographical references and index.

Analysis on Mobile Edge Computing
Machine Learning
Artificial Intelligence: Recent Trends
Approaches Toward Deep Learning
Big Data Analytics and Preprocessing
Theory of Probability

Big data consists of petabytes (more than 1 million gigabytes) and exabytes (more than 1 billion gigabytes), as opposed to the gigabytes common for personal devices. As big data emerged, so did computing models with the ability to store and manage it. Centralized or distributed computing systems provide access to big data. Distributed computing means big data is stored and processed on different computers, which communicate over a network. A software framework like Hadoop makes it possible to store the data and run applications to process it. This latest book is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software. The book is primarily intended for statisticians, computer experts, engineers and application developers interested in using big data analytics with statistics. Readers should have a solid background in statistics and computer science.

College of Engineering and Computer Studies Bachelor of Science in Information Technology

In English