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Practical Data Analysis

You're reading from  Practical Data Analysis

Product type Book
Published in Oct 2013
Publisher Packt
ISBN-13 9781783280995
Pages 360 pages
Edition 1st Edition
Languages
Author (1):
Hector Cuesta Hector Cuesta
Profile icon Hector Cuesta
Toc

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 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

Datasource


Datasource is a term used for all the technology related to the extraction and storage of data. A datasource can be anything from a simple text file to a big database. The raw data can come from observation logs, sensors, transactions, or user's behavior.

In this section we will take a look into the most common forms for datasource and datasets.

A dataset is a collection of data, usually presented in tabular form. Each column represents a particular variable, and each row corresponds to a given member of the data, as is shown in the following figure:

A dataset represents a physical implementation of a datasource; the common features of a dataset are as follows:

  • Dataset characteristics (such as multivariate or univariate)

  • Number of instances

  • Area (for example life, business, and so on)

  • Attribute characteristics (namely, real, categorical, and nominal)

  • Number of attributes

  • Associated tasks (such as classification or clustering)

  • Missing Values

Open data

Open data is data that can be used, re...

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