Big Data analytics: introduction to hadoop, spark and machine learning

By: Raj KamalMaterial type: TextTextPublication details: Chennai Mc Graw Hill 2019Description: 508 pISBN: 9789353164966Subject(s): big data | Electronic data processing | Strategic planning | Business intelligence | data miningDDC classification: 005.74 Summary: Big Data Analytics(BDA) is a rapidly evolving field that finds applications in many areas such as healthcare, medicine, advertising, marketing, and sales. This book dwells on all the aspects of Big Data Analytics and covers the subject in its entirety. It comprises several illustrations, sample codes, case studies and real-life analytics of datasets such as toys, chocolates, cars, and student’s GPAs. The book will serve the interests of undergraduate and post graduate students of computer science and engineering, information technology, and related disciplines. It will also be useful to software developers. Highlights: · Comprehensive coverage on Big Data NoSQL Column-family, Object and Graph databases, programming with open-source Big Data Hadoop and Spark ecosystem tools, such as MapReduce, Hive, Pig, Spark, Python, Mahout, Streaming, GraphX · Inclusion of latest topics machine learning, K-NN, predictive-analytics, similar and frequent item sets, clustering, decision-tree, classifiers recommenders, real-time streaming data analytics, graph networks, text, web structure, web-links, social network analytics. · Follows a hierarchical and teach-by- example approach from elementary to advanced level. · Rich pedagogy · Web supplement includes instructional PPTs, solution of exercises, analysis using open source datasets of a car company, and topics for advanced learning.
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Big Data Analytics(BDA) is a rapidly evolving field that finds applications in many areas such as healthcare, medicine, advertising, marketing, and sales. This book dwells on all the aspects of Big Data Analytics and covers the subject in its entirety. It comprises several illustrations, sample codes, case studies and real-life analytics of datasets such as toys, chocolates, cars, and student’s GPAs. The book will serve the interests of undergraduate and post graduate students of computer science and engineering, information technology, and related disciplines. It will also be useful to software developers.
Highlights:
· Comprehensive coverage on Big Data NoSQL Column-family, Object and Graph databases, programming with open-source Big Data Hadoop and Spark ecosystem tools, such as MapReduce, Hive, Pig, Spark, Python, Mahout, Streaming, GraphX
· Inclusion of latest topics machine learning, K-NN, predictive-analytics, similar and frequent item sets, clustering, decision-tree, classifiers recommenders, real-time streaming data analytics, graph networks, text, web structure, web-links, social network analytics.
· Follows a hierarchical and teach-by- example approach from elementary to advanced level.
· Rich pedagogy
· Web supplement includes instructional PPTs, solution of exercises, analysis using open source datasets of a car company, and topics for advanced learning.

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