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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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Tools
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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

Summary


In this chapter, we presented an overview of the data analysis ecosystem, explaining basic concepts of the data analysis process, tools, and some insight into the practical applications of the data analysis. We have also provided an overview of the different kinds of data; numerical and categorical. We got into the nature of data, structured (databases, logs, and reports) and unstructured (image collections, social networks, and text mining). Then, we introduced the importance of data visualization and how a fine visualization can help us in the exploratory data analysis. Finally we explored some of the concepts of big data and social networks analysis.

In the next chapter, we will work with data, cleaning, processing, and transforming, using Python and OpenRefine.

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Practical Data Analysis
Published in: Oct 2013
Publisher: Packt
ISBN-13: 9781783280995
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