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Practical Data Analysis

You're reading from   Practical Data Analysis Pandas, MongoDB, Apache Spark, and more

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Product type Paperback
Published in Sep 2016
Publisher
ISBN-13 9781785289712
Length 338 pages
Edition 2nd Edition
Languages
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Authors (2):
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Hector Cuesta Hector Cuesta
Author Profile Icon Hector Cuesta
Hector Cuesta
Dr. Sampath Kumar Dr. Sampath Kumar
Author Profile Icon Dr. Sampath Kumar
Dr. Sampath Kumar
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Toc

Table of Contents (16) Chapters Close

Preface 1. Getting Started 2. Preprocessing Data FREE CHAPTER 3. Getting to Grips with 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 Diseases with Cellular Automata 10. Working with Social Graphs 11. Working with Twitter Data 12. Data Processing and Aggregation with MongoDB 13. Working with MapReduce 14. Online Data Analysis with Jupyter and Wakari 15. Understanding Data Processing using Apache Spark

Summary

In this chapter, we worked on how to obtain and visualize a Facebook graph, detect communities, use color nodes, and apply some layouts with Gephi, such as Yifan Hu, Force Atlas, and Fruchterman-Reingold. Then, we introduced some statistical methods to get aggregate information, such as degree, centrality, distribution, and ratio. Finally, we developed our own visualization tool with D3.js, transforming the data from .gdf into JSON.

In the next chapter, we will present a short introduction to the Twitter API to retrieve, visualize, and analyze tweets. Then, we will proceed to perform a sentiment analysis.

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