Data Visualization with Python Training Course
Data visualization involves converting data into graphical and visual forms to analyze patterns or trends. This course emphasizes creating data visualizations using Python and gaining practical insights for common use cases.
This instructor-led, live training (available online or onsite) is designed for data analysts and data scientists who want to use Python to build interactive data visualizations directly from 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 core concepts, use cases, and tools of data visualization.
- 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.
- Many 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
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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