國立虎尾科技大學 |

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

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Network data analytics/ by K. G. Srinivasa, Siddesh G. M., Srinidhi H.
其他題名:	a hands-on approach for application development /
作者:	Srinivasa, K. G.
其他作者:	G. M., Siddesh.
出版者:	Cham :Springer International Publishing : : 2018.,
面頁冊數:	xxv, 398 p. :ill. (some col.), digital ; : 24 cm.;
Contained By:	Springer eBooks
標題:	Electronic data processing - Distributed processing. -
電子資源:	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 /
LDR:03073nam a2200337 a 4500 001 925631
003 DE-He213
005 20180426135934.0
006 m d
007 cr nn 008maaau
008 190625s2018 gw s 0 eng d
020 $a 9783319778006 $q (electronic bk.)
020 $a 9783319777993 $q (paper)
024 7 $a 10.1007/978-3-319-77800-6 $2 doi
035 $a 978-3-319-77800-6
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.D5
072 7 $a UNF $2 bicssc
072 7 $a UYQE $2 bicssc
072 7 $a COM021030 $2 bisacsh
082 0 4 $a 004.6 $2 23
090 $a QA76.9.D5 $b S774 2018
100 1 $a Srinivasa, K. G. $3 897249
245 1 0 $a Network data analytics $h [electronic resource] : $b a hands-on approach for application development / $c by K. G. Srinivasa, Siddesh G. M., Srinidhi H.
260 $a Cham : $c 2018. $b Springer International Publishing : $b Imprint: Springer,
300 $a xxv, 398 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Computer communications and networks, $x 1617-7975
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.
650 0 $a Electronic data processing $x Distributed processing. $3 528325
650 0 $a Machine learning. $3 561253
650 0 $a Big data. $3 981821
650 0 $a Internet of things. $3 1023130
650 1 4 $a Computer Science. $3 593922
650 2 4 $a Data Mining and Knowledge Discovery. $3 677765
650 2 4 $a Big Data. $3 1017136
650 2 4 $a Visualization. $3 574210
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 593924
700 1 $a G. M., Siddesh. $3 1203517
700 1 $a H., Srinidhi. $3 1203518
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer eBooks
830 0 $a Computer communications and networks. $3 890575
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-77800-6
950 $a Computer Science (Springer-11645)