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Practical Data Analysis

You're reading from   Practical Data Analysis For small businesses, analyzing the information contained in their data using open source technology could be game-changing. All you need is some basic programming and mathematical skills to do just that.

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Product type Paperback
Published in Oct 2013
Publisher Packt
ISBN-13 9781783280995
Length 360 pages
Edition 1st Edition
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Author (1):
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Hector Cuesta Hector Cuesta
Author Profile Icon Hector Cuesta
Hector Cuesta
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Table of Contents (24) Chapters Close

Practical Data Analysis
Credits
Foreword
About the Author
Acknowledgments
About the Reviewers
www.PacktPub.com
Preface
1. Getting Started FREE CHAPTER 2. Working with Data 3. Data Visualization 4. Text Classification 5. Similarity-based Image Retrieval 6. Simulation of Stock Prices 7. Predicting Gold Prices 8. Working with Support Vector Machines 9. Modeling Infectious Disease with Cellular Automata 10. Working with Social Graphs 11. Sentiment Analysis of Twitter Data 12. Data Processing and Aggregation with MongoDB 13. Working with MapReduce 14. Online Data Analysis with IPython and Wakari Setting Up the Infrastructure Index

Social Networks Analysis


The Social Networks Analysis (SNA) is not a new technique; sociologists have been using it for a long time to study human relationships (sociometry), find communities, and to simulate how information or a disease is spread in a population.

With the rise of social networking sites such as Facebook, Twitter, LinkedIn, and so on, the acquisition of large amounts of social network data has become easier. We can use SNA to get an insight about customer behavior or unknown communities. It is important to say that this is not a trivial task and we will face problems with sparse data and a lot of noise (meaningless data). We need to understand, how to distinguish between false correlation and causation. A good start is by knowing our graph through visualization and statistical analysis.

The social networking sites bring us the opportunities to ask questions that otherwise are too hard to approach, because polling enough people is time-consuming and expensive.

In this chapter...

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