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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

Chapter 2. Working with Data

Building real world's data analytics requires accurate data. In this chapter we discuss how to obtain, clean, normalize, and transform raw data into a standard format such as Comma-Separated Values (CSV) or JavaScript Object Notation (JSON) using OpenRefine.

In this chapter we will cover:

  • Datasource

    • Open data

    • Text files

    • Excel files

    • SQL databases

    • NoSQL databases

    • Multimedia

    • Web scraping

  • Data scrubbing

    • Statistical methods

    • Text parsing

    • Data transformation

  • Data formats

    • CSV

    • JSON

    • XML

    • YAML

  • Getting started with OpenRefine

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