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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

Requirements

  • Familiarity with data science concepts.
  • Experience with Python programming.

Audience

  • Data analysts.
  • Data scientists.
 14 Hours

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