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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

An overview of visual analytics


Visual analytics can be seen as an integral approach combining visualization, data analysis, and human factors. In order to gain knowledge from data, the visual analytics procedure unites visual analysis methods and automatic processes through human interaction. In many application scenarios, visual or automatic analysis methods were applied after the integration of heterogeneous data sources. Therefore, before performing visual analysis we should clean, normalize, and integrate the heterogeneous data sources. After the data cleaning, the analyst may choose visual analysis methods, wherein visualization helps the analyst to relate with the automatic methods by modifying parameters or selecting other analysis algorithms. At the end, the model visualization can be used to study the findings of the generated models.

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