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| Title | Big Data Analytics, 2ed |
| Author | Radha Shankarmani, M. Vijayalakshmi |
| ISBN-13 | 9788126565757 |
| Publication | WILEY |
The goal of this book is to cover foundational techniques and tools required for Big Data Analytics. It focuses on concepts, principles and techniques applicable to any technology environment and industry and establishes a baseline that can be enhanced further by additional real-world experience. This book aims to be a ready reckoner to either a novice or a professional working in the field. Topics covered include Hadoop, MapReduce, Association Rules, Large-Scale Supervised Machine Learning, Data Streams, Clustering, NoSQL systems (Pig, Hive) and Applications including Recommendation Systems, Web and Security.
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The goal of this book is to cover foundational techniques and tools required for Big Data Analytics. It focuses on concepts, principles and techniques applicable to any technology environment and industry and establishes a baseline that can be enhanced further by additional real-world experience. This book aims to be a ready reckoner to either a novice or a professional working in the field. Topics covered include Hadoop, MapReduce, Association Rules, Large-Scale Supervised Machine Learning, Data Streams, Clustering, NoSQL systems (Pig, Hive) and Applications including Recommendation Systems, Web and Security.
Radha Shankarmani, M. Vijayalakshmi is the author of Big Data Analytics, 2ed.
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| Title | Big Data Analytics, 2ed |
| Author | Radha Shankarmani, M. Vijayalakshmi |
| ISBN-13 | 9788126565757 |
| Publication | WILEY |
The goal of this book is to cover foundational techniques and tools required for Big Data Analytics. It focuses on concepts, principles and techniques applicable to any technology environment and industry and establishes a baseline that can be enhanced further by additional real-world experience. This book aims to be a ready reckoner to either a novice or a professional working in the field. Topics covered include Hadoop, MapReduce, Association Rules, Large-Scale Supervised Machine Learning, Data Streams, Clustering, NoSQL systems (Pig, Hive) and Applications including Recommendation Systems, Web and Security.