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Python: Data Analytics and Visualization

You're reading from   Python: Data Analytics and Visualization Perform data processing and analysis with the help of python libraries, gain practical insights into predictive modeling and generate effective results in a variety of visually appealing charts using the plotting packages in Python

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Product type Course
Published in Mar 2017
Publisher Packt
ISBN-13 9781788290098
Length 866 pages
Edition 1st Edition
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Authors (4):
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Martin Czygan Martin Czygan
Author Profile Icon Martin Czygan
Martin Czygan
Ashish Kumar Ashish Kumar
Author Profile Icon Ashish Kumar
Ashish Kumar
Kirthi Raman Kirthi Raman
Author Profile Icon Kirthi Raman
Kirthi Raman
Phuong Vo.T.H Phuong Vo.T.H
Author Profile Icon Phuong Vo.T.H
Phuong Vo.T.H
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Toc

Chapter 2. Data Analysis and Visualization

Most visualization stories begin with some question that is oriented towards a topic where the data is being either explored or collected. The question contains the premise to the story and leads us to the point at which the data takes an expedition over the storyline. Such data expeditions that start with a question, for example, How many Ebola deaths were reported in the year 2014? are implemented by a group of people by collaborating with each other. The role of data communicators should be to create an information experience that transforms how their audiences think about their story.

The key parts of the story relate to the process of placing the visualization in a meaningful context. The context provides knowledge that answers questions such as the following:

  • Is there sufficient data?
  • Is there a time frame within which this data exists?
  • Which associable events around the globe will influence this data?

To reiterate, it is important to...

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