Network Data Analytics

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.

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

Autorentext

Dr Muralidhar Kurni is an associate professor in the Computer Science and Engineering department at Anantha Lakshmi Institute of Technology & Sciences, Ananthapuramu, India. He is a senior member of IEEE and a professional member of ACM, the author of many textbooks, and a reviewer for reputed international journals and conferences. Dr Srinivasa K. G. is a professor at the International Institute of Information Technology, Naya Raipur, Chhattisgarh, India. He is recognized for his innovative teaching practices, outcome-based education, and contributions to cyber-physical systems and fog computing.

Inhalt
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.