Challenges and Applications of Data Analytics in Social Perspectives

This book PDF is perfect for those who love Computers genre, written by Sathiyamoorthi, V. and published by IGI Global which was released on 04 December 2020 with total hardcover pages 324. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Challenges and Applications of Data Analytics in Social Perspectives books below.

Challenges and Applications of Data Analytics in Social Perspectives
Author : Sathiyamoorthi, V.
File Size : 53,6 Mb
Publisher : IGI Global
Language : English
Release Date : 04 December 2020
ISBN : 9781799825685
Pages : 324 pages
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Challenges and Applications of Data Analytics in Social Perspectives by Sathiyamoorthi, V. Book PDF Summary

With exponentially increasing amounts of data accumulating in real-time, there is no reason why one should not turn data into a competitive advantage. While machine learning, driven by advancements in artificial intelligence, has made great strides, it has not been able to surpass a number of challenges that still prevail in the way of better success. Such limitations as the lack of better methods, deeper understanding of problems, and advanced tools are hindering progress. Challenges and Applications of Data Analytics in Social Perspectives provides innovative insights into the prevailing challenges in data analytics and its application on social media and focuses on various machine learning and deep learning techniques in improving practice and research. The content within this publication examines topics that include collaborative filtering, data visualization, and edge computing. It provides research ideal for data scientists, data analysts, IT specialists, website designers, e-commerce professionals, government officials, software engineers, social media analysts, industry professionals, academicians, researchers, and students.

Challenges and Applications of Data Analytics in Social Perspectives

With exponentially increasing amounts of data accumulating in real-time, there is no reason why one should not turn data into a competitive advantage. While machine learning, driven by advancements in artificial intelligence, has made great strides, it has not been able to surpass a number of challenges that still prevail

Get Book
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