Big Data Analytics: Introduction to Hadoop, Spark and Machine Learning
Material type: TextLanguage: English Publication details: Chennai: McGraw Hill Education (India) Private Limited, 2019Description: 508ISBN:- 9789353164966
- 005.74 KAM
Item type | Current library | Call number | Status | Date due | Barcode | Item holds | |
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Book | Alliance School of Liberal Arts | 005.74 KAM (Browse shelf(Opens below)) | Available | LA02272 | |||
Book | Alliance School of Liberal Arts | 005.74 KAM (Browse shelf(Opens below)) | Available | LA01270 |
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005.74 GUP Introduction to Data Mining with Case Studies | 005.74 HAN Data Mining: Concepts and Techniques | 005.74 KAM Big Data Analytics: Introduction to Hadoop, Spark and Machine Learning | 005.74 KAM Big Data Analytics: Introduction to Hadoop, Spark and Machine Learning | 005.74 KRO Big Data For Chimps | 005.74 LAK Data Science On The Google Cloud Platform | 005.74 MAH Big Data |
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. Salient Features:• 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.
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