About this video

This video course is a beginner’s introduction to data visualization, and the techniques and libraries which can be leveraged with the Python language to achieve this. The end goal is to teach analysts and data scientists how they can visually represent complex sets of data using Python.
The video course introduces visualization concepts so viewers can analyze large and small sets of data using libraries such as Matplotlib, IPython, and so on.

This course primarily employs the IPython environment and matplotlib, with the following structure:

  • Introduce key data visualization libraries (matplotlib and so on.) and cover data importing/exporting (CSV, Excel, JSON and so on).
  • Introduce real-world data sets (to be visualized in the video).
  • Visualization types/techniques (bar chart, histogram, scatter plot, geospatial, and so on); demonstrate how to customize visualizations.
  • Introduce intermediate topics to create more advanced visualizations and using complex techniques, such as real-time data visualization.

By the end of the course, you will be able to demonstrate visualizations with interesting, real-world data sets.

The code bundle for this video course is available at- https://github.com/PacktPublishing/Learning-Python-Data-Visualization

Style and Approach

This course will push you to develop high-quality reliable reports using Python to help you derive solutions using minimal code and fewer bugs and align with business requirements. You may have previously worked on Python and learned the basic syntax, but this course focuses on visualizing data to create real-world solutions. With concise, clearly explained concepts, you’ll acquire the knowledge you really need from this course.

Publication date:
April 2018
3 hours 9 minutes

About the Author

  • Benjamin Walter Keller

    Benjamin Walter Keller is currently a PhD candidate at McMaster University and gained his BSc in physics with a minor in computer science from the University of Calgary in 2011. His current research involves numerical modeling of galaxy evolution over cosmological timescales. As an undergraduate at the U of C, he worked on stacking radio polarization to examine faint extragalactic sources. He also worked in the POSSUM Working Group 2 to determine the requirements for stacking applications for the Australian SKA Pathfinder (ASKAP) radio telescope. He is particularly interested in questions involving stellar feedback (supernovae, stellar winds, and so on) and its impact on galaxies and their surrounding intergalactic medium.

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