Data Visualization with Python Training Course
Data visualization involves converting data into graphical and visual formats to analyse patterns or trends. This course concentrates on developing data visualizations using Python and acquiring practical insights for typical applications.
This instructor-led, live training (available online or onsite) is designed for data analysts and data scientists who wish to use Python to construct interactive data visualizations directly from the code.
By the conclusion of this training, participants will be capable of:
- Configuring the necessary development environment to begin creating data visualizations with Python.
- Understanding the fundamental concepts, use cases, and tools of data visualization.
- Exploring the various Python libraries (Matplotlib, Seaborn, Bokeh, and Folium) that are available.
- Learning how to produce line plots, statistical graphs, geo-spatial, and other complex data visualizations with Python.
- Gaining knowledge of the best practices and techniques for presenting and interpreting data.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practice opportunities.
- Hands-on implementation within a live-lab environment.
Course Customization Options
- To request 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
Requirements
- An understanding of data science concepts
- Python programming experience
Audience
- Data analysts
- Data scientists
Open Training Courses require 5+ participants.
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Testimonials (1)
workshops, practical examples
Martin Stuparek - Orange Slovensko, a.s.
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