In this lesson, you will start by learning about the ideas and results of those considered masters in the art of information visualization. Next, you will learn to work with Matplotlib, which is the standard plotting library for Python programs. You will use an iPython notebook to explore the basic Matplotlib Python statements to make and annotate a simple plot. You also will be introduced to the Seaborn plotting library, which enhances the power of MatPlotlib by simplifying the creation of aesthetically attractive visualizations and introducing new plot types. Finally, you will learn to make and interpret scatter plots by using Matplotlib. Scatter plots can highlight trends or correlations between two or more columns of a given data set.
By the end of this lesson, you will:
- Understand the power of visualizations in conveying information.
- Understand the importance of aesthetics in communicating data.
- Understand the perils of presentation slides to communicate technical information.
- Understand how to use Matplotlib within an iPython notebook to make a simple, inline plot.
- Understand how to use Matplotlib to label the plot axes and to apply a title to a plot
- Understand how to use Matplotlib to change the plot limits to increase the impact of a plot.
- Understand how to use Seaborn within an IPython Notebook.
- Understand how to use Matplotlib to make a scatter plot.
- Understand the difference between positive, negative, and null correlations.
- Understand how to graphically identify outliers.
- Understand how to overplot and label multiple columns on a single scatter plot.
Approximately 3 hours.
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The Beauty of Data Visualizations
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On the Perils of Powerpoint
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Visualizing Statistical Information
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IPython Notebook on Python Plotting
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IPython Notebook on Scatter Plots
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Wikipedia article on Scatter Plots
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The many wonderful articles at Flowing Data
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Wikipedia article on [Plots][plt] which has a lot of information about different plot types.
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Wikipedia entry on Anscombe's quartet