Applied Data Analytics : Principles and Applications

Johnson I. Agbinya

ISBN 10: 8770220964 / ISBN 13: 9788770220965

Edité par River Publishers, 2019

A propos de cet article

nach der Bestellung gedruckt Neuware - Printed after ordering - The emergence of huge amounts of data which require analysis and in some cases real-time processing has forced exploration into fast algorithms for handling very lage data sizes. Analysis of x-ray images in medical applications, cyber security data, crime data, telecommunications and stock market data, health records and business analytics data are but a few areas of interest. Applications and platforms including R, RapidMiner and Weka provide the basis for analysis, often used by practitioners who pay little to no attention to the underlying mathematics and processes impacting the data. This often leads to an inability to explain results or correct mistakes, or to spot errors.Applied Data Analytics - Principles and Applications seeks to bridge this missing gap by providing some of the most sought after techniques in big data analytics. Establishing strong foundations in these topics provides practical ease when big data analyses are undertaken using the widely available open source and commercially orientated computation platforms, languages and visualisation systems. The book, when combined with such platforms, provides a complete set of tools required to handle big data and can lead to fast implementations and applications.The book contains a mixture of machine learning foundations, deep learning, artificial intelligence, statistics and evolutionary learning mathematics written from the usage point of view with rich explanations on what the concepts mean. The author has thus avoided the complexities often associated with these concepts when found in research papers. The tutorial nature of the book and the applications provided are some of the reasons why the book is suitable for undergraduate, postgraduate and big data analytics enthusiasts.This text should ease the fear of mathematics often associated with practical data analytics and support rapid applications in artificial intelligence, environmental sensor data modelling and analysis, health informatics, business data analytics, data from Internet of Things and deep learning applications.

N° de réf. du vendeur 9788770220965

Détails bibliographiques

• Titre: Applied Data Analytics : Principles and Applications
• Éditeur: River Publishers
• Date de parution: 2019
• Langue: anglais
• ISBN à 10 chiffres: 8770220964
• ISBN à 13 chiffres: 9788770220965
• Reliure: Buch
• État de l'article: Neu
• Dimensions: 240 L x 161 H x 24 P (en millimètres) 240 millimètres de largeur par 161 millimètres de hauteur par 24 millimètres de profondeur
• Poids: 719 grammes

A propos de ce titre

Synopsis

The emergence of huge amounts of data which require analysis and in some cases real-time processing has forced exploration into fast algorithms for handling very lage data sizes. Analysis of x-ray images in medical applications, cyber security data, crime data, telecommunications and stock market data, health records and business analytics data are but a few areas of interest. Applications and platforms including R, RapidMiner and Weka provide the basis for analysis, often used by practitioners who pay little to no attention to the underlying mathematics and processes impacting the data. This often leads to an inability to explain results or correct mistakes, or to spot errors.

Applied Data Analytics - Principles and Applications seeks to bridge this missing gap by providing some of the most sought after techniques in big data analytics. Establishing strong foundations in these topics provides practical ease when big data analyses are undertaken using the widely available open source and commercially orientated computation platforms, languages and visualisation systems. The book, when combined with such platforms, provides a complete set of tools required to handle big data and can lead to fast implementations and applications.

The book contains a mixture of machine learning foundations, deep learning, artificial intelligence, statistics and evolutionary learning mathematics written from the usage point of view with rich explanations on what the concepts mean. The author has thus avoided the complexities often associated with these concepts when found in research papers. The tutorial nature of the book and the applications provided are some of the reasons why the book is suitable for undergraduate, postgraduate and big data analytics enthusiasts.

This text should ease the fear of mathematics often associated with practical data analytics and support rapid applications in artificial intelligence, environmental sensor data modelling and analysis, health informatics, business data analytics, data from Internet of Things and deep learning applications.

À propos de l?auteur

Prof Johnson I. Agbinya obtained PhD in microwave radar systems from La Trobe University, Melbourne Australia, MSc (Research) in electronic control from the University of Strathclyde Glasgow Scotland and BSc in Electronic/Electrical Engineering from Obafemi Awolowo University (OAU), Ife Nigeria. He is currently Head, School of Information Technology and Engineering at Melbourne Institute of Technology (MIT), Australia. He was Senior Research Scientist at the Commonwealth Scientific Research Organ- isation (CSIRO) for nearly a decade before joining Vodafone Australia as Research Manager where he contributed to the design of its 3G network. He subsequently joined the University of Technology as Senior lecturer in the Faculty of Engineering and IT from where he moved back to La Trobe Uni- versity as Associate Professor. He is currently a full Professor of Engineering at Melbourne Institute of Technology, Australia. Prof Agbinya's service to humanity is focused on training emerging African Scientists, lecturers and technocrats through a number of African countries including Sudan where he is a Research Professor at the Sudan University of Science and Technology in Khartoum, Nelson Mandela African Institute of Science and Technology Arusha Tanzania as an Adjunct Professor until 2018 and Tshwane University of Technology Pretoria South Africa as Adjunct Professor in ICT with a PhD. He was Professor Extraordinaire in Computer Science at the University of the Western Cape in Cape Town and Extraordinary Professor at the University of Witwatersrand Johannesburg. Prof Agbinya is a member of Pan African Australasian Diaspora Network (PAADN), member of the Nigerian Society of Engineering and Fellow of African Scientific Institute (ASI). He has published extensively including ten mobile communication, sensor and data analytics textbooks and over three hundred and fifty journals and conference articles. His technical expertise is in the areas of Mobile Com- munications, electronic remote sensing, signal processing, wireless power transfer, Internet of Things, biometrics, electrical energy and machine to machine communications and artificial intelligence. He is the founder of the annual conferences IB2COM and African conference called Pan African Conference on Science, Computing and Telecommunication (PACT). He also founded the African Journal of Information and Communication Technology (AJICT). He is currently on the editorial boards of several international journals in ICT and sensors, and editorial consultant at River Publishers Denmark.