Data Visualization: Matplotlib Seaborn Plotly and Altair

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Stunning visualizations

Requirements

  • Basics of Python

Description

The section on Pandas gives you an introduction to data cleaning, data processing and data manipulation operations you can undertake – ranging from transformation between data types, obtaining summary information about your dataset, obtaining aggregate values.

The section on Matplotlib, one of the most established plotting libraries introduces you to the basics of figure and axes level objects, and some of the most commonly used plots to examine distributions and relationships between variables, or change in a variable over time.

The section on Seaborn introduces you to plotting categorical plots, relational plots, numerical plots, distributions and multiples where color, size, shape and facets support conveying rich informational insight which can support you in visual data exploration, and in data storytelling.

The section on Altair offers you an introduction to yet another powerful Python Visualization library with a wide range of plotting abilities, allowing you to plot the more common plots, but also more advanced visualizations.

By the end of this course:

You will walk away with an understanding of different data types.

You will be able to study summary data about your dataset, and discern relationships between variables

You develop your instincts on how to convey insight about your data to different stakeholders at varying levels of detail, depending on where they stand in the decision-making hierarchy.

Note: We use Google Colab in this course

Who this course is for:

  • Data science aspirants who want to maximise the plotting flexibilities offered by Matplotlib, Seaborn and Altair;
  • Candidates in business intelligence/data analytics roles looking to present complex insights to decision-makers and stakeholders
  • Anyone who wants to generate plots rich with information by using Seaborn in under two lines of code!

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