000			02746nam a22003137a 4500
003			CUTN
005			20231211120907.0
008			231211b |||||||| |||| 00| 0 eng d
020			_a9789353164966
020			_a9353164966
041			_aEnglish
082			_223 _a519.502855133 _bKAM
100			_aKamal, Raj.
240			_aBig Data Analytics
245			_aBig Data Analytics / _cRaj Kamal.
250			_a1st ed.
260			_aIndia : _bMcGraw Hill Education Pvt Ltd, _c2019.
300			_axxvi, 509 p. : _bill. ; _c24 cm.
505			_t1. Introduction to Big Data Analytics 2. Introduction to Hadoop 3. NoSQL Big Data Management, MongoDB and Cassandra 4. MapReduce, Hive and Pig 5. Spark and Big Data Analytics 6. Machine Learning Algorithms for Big Data Analytics 7. Data Stream Mining and Real-Time Analytics Platform—SparkStreaming 8. Graph Analytics for Big Data and Spark GraphX Platform 9. Text, Web Content, Link, and Social Network Analytics 10. Programming Examples in Analytics and Machine Learning using Hadoop, Spark and Python
520			_aBig 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.
650			_aGraph Analytics
650			_aHadoop
650			_aMapReduce
650			_a Machine Learning
650			_aSpark GraphX Platform
650			_aHive
700			_aSaxena, Preeti.
942			_2ddc _cTB
999			_c40868 _d40868