In order to carry out data analytics, we need powerful and flexible computing software. However the software available for data analytics is often proprietary and can be expensive. This book reviews Apache tools, which are open source and easy to use. After providing an overview of the background of data analytics, covering the different types of analysis and the basics of using Hadoop as a tool, it focuses on different Hadoop ecosystem tools, like Apache Flume, Apache Spark, Apache Storm, Apache Hive, R, and Python, which can be used for different types of analysis. It then examines the different machine learning techniques that are useful for data analytics, and how to visualize data with different graphs and charts. Presenting data analytics from a practice-oriented viewpoint, the book discusses useful tools and approaches for data analytics, supported by concrete code examples. The book is a valuable reference resource for graduate students and professionals in related fields, and is also of interest to general readers with an understanding of data analytics.
Cloud based Multi-Modal Information Analytics: A Hands-on Approach discusses the various modalities of data and provide an aggregated solution using cloud. It includes the fundamentals of neural networks, different types and how they can be used for the multi-modal information analytics. The various application areas that are image-centric and videos are also presented with deployment solutions in the cloud. Features Life cycle of the multi- modal data analytics is discussed with applications of modalities of text, image, and video. Deep Learning fundamentals and architectures covering convolutional Neural Networks, recurrremt neural networks, and types of learning for different multi-modal networks. Applications of Multi-Modal Analytics covering Text , Speech, and Image. This book is aimed at researchers in Multi-modal analytics and related areas
Beginning with a simple model of the debt/equity impact upon firm value and progressively adding complexity to this model, this book seeks to answer the question, What is the frontier of knowledge with respect to debt/equity alternatives, and could a major paradigm shift affect debt/equity choices? With a view toward providing the reader with a framework for examining debt/equity decisions, this book begins with a simple model of the debt/equity impact upon firm value. Utilizing the paradigm development of capital structure theory to identify the current research frontier of the factors affecting the firm debt/equity position, the authors also extrapolate from the current frontier to outline future opportunities for research and improvements in capital structure analysis. Each chapter begins with a discussion of a central tenet, moves on to a discussion of the theoretical research and empirical evidence pertaining to the tenet, and concludes with a summary of the implications of the paradigm shift for current and future research and practice. A chapter at the end of the book provides an analysis of some unanswered questions in the current frontier of knowledge that may be exploited for further research. One is the strength of signaling of capital structure changes on firm value. A second is a lack of specification for the set of capital structure simultaneous equations. A third emerging issue is the definition of the capital structure within behavioral finance thinking.
In order to carry out data analytics, we need powerful and flexible computing software. However the software available for data analytics is often proprietary and can be expensive. This book reviews Apache tools, which are open source and easy to use. After providing an overview of the background of data analytics, covering the different types of analysis and the basics of using Hadoop as a tool, it focuses on different Hadoop ecosystem tools, like Apache Flume, Apache Spark, Apache Storm, Apache Hive, R, and Python, which can be used for different types of analysis. It then examines the different machine learning techniques that are useful for data analytics, and how to visualize data with different graphs and charts. Presenting data analytics from a practice-oriented viewpoint, the book discusses useful tools and approaches for data analytics, supported by concrete code examples. The book is a valuable reference resource for graduate students and professionals in related fields, and is also of interest to general readers with an understanding of data analytics.
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