This course helps participants to gain a deep understanding of the principles of analytical design and how to use different data imagery tools.
Course Content
- • Python Data Visualization: Introduction
- • Lesson 1: Human Perception
- • Topics
- • 1.1 Understanding Color Theory
- • 1.2 Overview of Human Vision
- • 1.3 Color Schemes
- • Lesson 2: Analytical Design
- • Topics
- • 2.1 Understand the Fundamental Principles of Analytical Design
- • 2.2 Describe the Fundamental Tools of Visualization
- • 2.3 Advantages and Disadvantages of Different Chart Types
- • Lesson 3: Data Cleaning and Visualizion with Pandas
- • Topics
- • 3.1 DataFrames and Series
- • 3.2 GroupBy and Pivot Tables
- • 3.3 Merge and Join
- • 3.4 The Plot Function
- • 3.5 Demo
- • 3.6 Time Series
- • 3.7 Bar Plot Demo
- • Lesson 4: Matplotlib
- • Topics
- • 4.1 Fundamental Components of a matplotlib plot
- • 4.2 Explore the matplotlib API
- • 4.3 Demo
- • 4.4 Stylesheets
- • 4.5 Demo
- • 4.6 Mapping
- • 4.7 Demo
- • Lesson 5: Matploltib Animations
- • Topics
- • 5.1 Matploltib Animation API
- • 5.2 Func Animation
- • 5.3 Animation Writers
- • 5.4 Demo
- • Lesson 6: Jupyter Widgets
- • Topics
- • 6.1 ipywidgets as Interactive Browser Controls
- • 6.2 Simple Wdget Use
- • 6.3 Widget Customization
- • 6.4 Demo
- • Lesson 7: Seaborn
- • Topics
- • 7.1 Understand the Structure of seaborn
- • 7.2 Understand the Differences with matplotlib
- • 7.3 Explore the Seaborn API
- • 7.4 Demo
- • Lesson 8: Bokeh
- • Topics
- • 8.1 Basic Plotting with Bokeh
- • 8.2 Advanced Plotting
- • 8.3 Networks
- • 8.4 Demo
- • Lesson 9: Plotly
- • Topics
- • 9.1 Basic Plotly
- • 9.2 3D and Animated Plots
- • 9.3 Demo
- • Summary
- • Python Data Visualization: Summary
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