Data Visualization with Python Training Course
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
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 (5)
Exchange, listening and explanations
Romain LECREUX - Inetum
Course - Advanced Grafana
Machine Translated
The perfect adaptation to my needs
Thomas - AXA Wealth Services
Machine Translated
I liked Pablo's style, the fact that he covered a lot of subjects from report design , customization with html to implementing simple ML algortithms. Good balance theoretical information / exercices. Pablo really covered all topics i was interested in and gave comprehensive answers to my questions.
Cristian Tudose - SC Automobile Dacia SA
Course - Advanced Data Analysis with TIBCO Spotfire
Actual application of spotfire and all basic functions.
Michael Capili - STMicroelectronics, Inc.
Course - Introduction to Spotfire
Real world knowledge from someone in the industry
Matthew Cerbas - Shield Consulting Solutions, Inc.
Course - Grafana
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