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
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.
https://github.com/srinidhi151/Book
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.
Part I: Data Analytics and Hadoop
Introduction to Data Analytics
Introduction to Hadoop
Data Analytics with Map Reduce
Part II: Tools for Data Analytics
Apache Pig
Apache Hive
Apache Spark
Apache Flume
Apache Storm
Python
R
Part III: Machine Learning for Data Analytics
Basics of Machine Learning
Linear Regression
Logistic Regression
Machine Learning on Spark
Part IV: Exploring and Visualizing Data
Introduction to Visualization
Principles of Data Visualization
Visualization Charts
Popular Visualization Tools
Data Visualization with Hadoop
Part V: Case Studies
Product Recommendation
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.
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.
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.
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.
Über den Autor
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. rn Dr. Gaddadevara Matt Siddesh is an associate professor at the Department of Information Science and Engineering at Ramaiah Institute of Technology, Bangalore, India. rn Srinidhi Hiriyannaiah is an assistant professor at the Department of Computer Science and Engineering at Ramaiah Institute of Technology, Bangalore, India.
Inhaltsverzeichnis
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.
Klappentext
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.
rn
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
