Course Code: pythondatavisualization
Duration: 14 hours
Prerequisites:
  • An understanding of data science concepts
  • Python programming experience

Audience

  • Data analysts
  • Data scientists
Overview:

Data visualization is the practice of transforming data into graphical and visual representations for analyzing patterns or trends. This course focuses on creating data visualizations using Python and gaining practical insights for common use cases.

This instructor-led, live training (online or onsite) is aimed at data analysts and data scientists who wish to use Python to build interactive data visualizations directly from the code.

By the end of this training, participants will be able to:

  • Set up the necessary development environment to start creating data visualizations with Python.
  • Understand the data visualization core concepts, use cases, and tools.
  • Explore the different libraries (Matplotlib, Seaborn, Bokeh, and Folium) available in Python.
  • Learn how to create line plots, statistical graphs, geo-spatial, and other complex data visualizations with Python.
  • Know the best practices and techniques for presenting and interpreting data.

Format of the Course

  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.

Course Customization Options

  • To request a customized training for this course, please contact us to arrange.
Course Outline:

Introduction

  • Overview of data visualization core concepts
  • Visualization techniques and tools

Getting Started

  • Installing the Python libraries (Matplotlib, Seaborn, Bokeh, and Folium)
  • Use cases and practical examples

Creating Line Plots and Graphs with Matplotlib

  • Creating basic line plots
  • Adding styles, axis, and labels
  • Combining multiple plots
  • Creating bar charts, pie charts and histograms

Building Complex Visualizations with Seaborn

  • Visualizing Pandas DataFrame
  • Plotting bars and aggregates
  • Implementing KDE, Box, and Violin plots
  • Analyzing statistical distributions

Making Visualizations Interactive with Bokeh

  • Plotting with basic glyphs
  • Creating layouts for multiple visualizations
  • Styling and visual attributes
  • Adding interactivity (interactive legends, hover actions, and widgets)
  • Implementing linked selections

Visualizing Geospatial Data with Folium

  • Plotting interactive maps
  • Using layers and tiles
  • Adding markers and paths

Troubleshooting

Summary and Next Steps

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