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Welcome to the second module in
the course Applied Plotting, Charting and

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Data Representation in Python.

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In the first module, we touched on
the some of the fundamentals of

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putting together good
information graphics.

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Drawing on influential thinkers in
the field, such Alberto Cairo and

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Edward Tufte.

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In this module, we're going to pivot and
focus on skills for

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building basic charts using
the Python based Matplotlib tool kit.

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Matplotlib is a powerful open source tool
kit for representing and visualizing data.

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Its creator, John Hunter,
was heavily inspired

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by the MATLAB programming environment and
borrows many of these elements.

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Matplotlib can be a bit confusing
to learn from web resources.

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There's a traditional object-oriented API,
but

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there's also a MATLAB-like
scripting model.

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And I've found that it's not always clear
why a given script from a blog post might

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work or not work.

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I'm going to try and
demystify this a bit for

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you by providing a structured
introduction to the toolkit.

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After the architecture introduction,

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we're going to dive right into
creating charts and graphs.

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In this module, we're going to focus
on some of the basics, scatter plots,

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line graphs, and bar charts.

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We're also going to touch on how to
label pieces of your charts to improve

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readability, and draw the user's
attention to specific details.

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And how to manipulate data elements
in a plotting environment.

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From here on out there
will be fewer lectures and

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more emphasis on the module will be put
on the plotting and charting assignments.

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At the end of modules two, three and four,
you should be able to produce publication

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quality charts and
figures using a breadth of data.

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Let's dive in.