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Network Data Analytics: A Hands-On Approach for Application Development de K. G. Srinivasa

Network Data Analytics: A Hands-On Approach for Application Development: Computer Communications and Networks

Autor K. G. Srinivasa, Siddesh G. M., Srinidhi H.
en Limba Engleză Paperback – 26 dec 2018
In order to carry out data analytics, we need powerful and flexible computing software. However the software available for data analytics is often proprietary and can be expensive. This book reviews Apache tools, which are open source and easy to use. After providing an overview of the background of data analytics, covering the different types of analysis and the basics of using Hadoop as a tool, it focuses on different Hadoop ecosystem tools, like Apache Flume, Apache Spark, Apache Storm, Apache Hive, R, and Python, which can be used for different types of analysis. It then examines the different machine learning techniques that are useful for data analytics, and how to visualize data with different graphs and charts.
 
Presenting data analytics from a practice-oriented viewpoint, the book discusses useful tools and approaches for data analytics, supported by concrete code examples. The book is a valuable reference resource for graduate students and professionals in related fields, and is also of interest to general readers with an understanding of data analytics.

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Specificații

ISBN-13: 9783030085445
ISBN-10: 3030085449
Pagini: 398
Ilustrații: XXV, 398 p. 155 illus., 117 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.68 kg
Ediția:Softcover reprint of the original 1st ed. 2018
Editura: Springer International Publishing
Colecția Springer
Seria Computer Communications and Networks

Locul publicării:Cham, Switzerland

Cuprins

Part I: Data Analytics and Hadoop.- Chapter 1. Introduction to Data Analytics.- Chapter 2. Introduction to Hadoop.- Chapter 3. Data Analytics with Map Reduce.- Part II: Tools for Data Analytics.- Chapter 4. Apache Pig.- Chapter 5. Apache Hive.- Chapter 6. Apache Spark.- Chapter 7. Apache Flume.- Chapter 8. Apache Storm.- Chapter 9. Python R.- Part III: Machine Learning for Data Analytics.- Chapter 10. Basics of Machine Learning.- Chapter 11. Linear Regression.- Chapter 12. Logistic Regression.- Chapter 13. Machine Learning on Spark.- Part IV: Exploring and Visualizing Data.- Chapter 14. Introduction to Visualization.- Chapter 15. Principles of Data Visualization.- Chapter 16. Visualization Charts.- Chapter 17. Popular Visualization Tools.- Chapter 18. Data Visualization with Hadoop.- Part V: Case Studies.- Chapter 19. Product Recommendation.- Chapter 20. Market Basket Analysis.

Notă biografică

Dr. Krishnarajanagar GopalaIyengar Srinivasa is an associate professor and the head of the Department of IT at C.B.P. Government Engineering College, Jaffarpur, New Delhi, India. His other publications include the Springer book Guide to High Performance Distributed Computing.
 
Dr. Gaddadevara Matt Siddesh is an associate professor at the Department of Information Science and Engineering at Ramaiah Institute of Technology, Bangalore, India.
 
Srinidhi Hiriyannaiah is an assistant professor at the Department of Computer Science and Engineering at Ramaiah Institute of Technology, Bangalore, India.

Textul de pe ultima copertă

In order to carry out data analytics, we need powerful and flexible computing software. However the software available for data analytics is often proprietary and can be expensive. This book reviews Apache tools, which are open source and easy to use. After providing an overview of the background of data analytics, covering the different types of analysis and the basics of using Hadoop as a tool, it focuses on different Hadoop ecosystem tools, like Apache Flume, Apache Spark, Apache Storm, Apache Hive, R, and Python, which can be used for different types of analysis. It then examines the different machine learning techniques that are useful for data analytics, and how to visualize data with different graphs and charts.
 
Presenting data analytics from a practice-oriented viewpoint, the book discusses useful tools and approaches for data analytics, supported by concrete code examples. The book is a valuable reference resource for graduate students and professionals inrelated fields, and is also of interest to general readers with an understanding of data analytics.

Caracteristici

Introduces tools for data analytics, machine learning for data analytics, and for exploring and visualizing data Suitable as both a practical guide and a reference for researchers and students Provides supplementary material, in the form of working source code, on an associated website Includes supplementary material: sn.pub/extras