國立虎尾科技大學 |

Network data analytics = a hands-on approach for application development /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Network data analytics/ by K. G. Srinivasa, Siddesh G. M., Srinidhi H.
Reminder of title:	a hands-on approach for application development /
Author:	Srinivasa, K. G.
other author:	G. M., Siddesh.
Published:	Cham :Springer International Publishing : : 2018.,
Description:	xxv, 398 p. :ill. (some col.), digital ; : 24 cm.;
Contained By:	Springer eBooks
Subject:	Electronic data processing - Distributed processing. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-77800-6
ISBN:	9783319778006

Network data analytics = a hands-on approach for application development /
Srinivasa, K. G.

Network data analyticsa hands-on approach for application development /[electronic resource] :by K. G. Srinivasa, Siddesh G. M., Srinidhi H. - Cham :Springer International Publishing :2018. - xxv, 398 p. :ill. (some col.), digital ;24 cm. - Computer communications and networks,1617-7975. - Computer communications and networks..

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.

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.

ISBN: 9783319778006

Standard No.: 10.1007/978-3-319-77800-6doiSubjects--Topical Terms:

528325
Electronic data processing
--Distributed processing.

LC Class. No.: QA76.9.D5

Dewey Class. No.: 004.6

Network data analytics = a hands-on approach for application development /
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505 0 $a 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.
520 $a 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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