Data Visualization with Python in Excel

Course details

In this course, Excel MVP George Mount shows you how to enhance your data visualization skills by merging the familiar environment of Excel with Python's powerful libraries. Explore visualization possibilities, from basic plots to dynamic, AI-assisted insights, enabling you to manage projects, track trends, and impress your employer. Get hands-on practice with foundational libraries such as Matplotlib, Seaborn, and Plot 9, each offering unique plotting advantages. Discover how to leverage Excel's latest Python integration features and how AI can reduce your workload with code completions and customization options. When you complete this course, you'll be adept at blending Python's robust visualization capabilities with Excel's accessibility, providing you with a versatile toolkit for data storytelling.

Learning objectives
Compare and evaluate different Python visualization libraries (Matplotlib, Seaborn, and Plotnine), assessing their strengths, weaknesses, and most appropriate use cases for various data visualization challenges in Excel.
Design and generate advanced, customized data visualizations using Python libraries, including creating multi-layered plots, statistical graphics (like box plots with swarm overlays), and dynamic charts that integrate seamlessly with Excel, demonstrating proficiency in transforming raw data into insightful visual representations.
Implement Python visualization strategies to solve practical data analysis problems, such as developing moving average charts, creating dynamic performance measures, and using AI-assisted tools like Copilot to generate and refine sophisticated data visualizations that enhance decision-making processes.
Articulate the core principles of data visualization, including the Grammar of Graphics, the advantages of using Python for Excel visualizations, and the fundamental techniques for transforming raw data into meaningful, communicative visual narratives.
Assess AI-generated Python visualizations, demonstrating the ability to identify areas for improvement, customize color scales, add meaningful annotations, and refine axis labels to enhance the clarity and communicative power of data representations.

Concepts

0. Introduction

01 - Unlocking the power of Python visualizations in Excel
02 - What you should know before starting this course

1. Getting Started with Python Visualizations in Excel

03 - Why use Python for data visualization
04 - Creating your first Python plot in Excel
05 - Challenge - Customize your first plot
06 - Solution - Customize your first plot

2. Matplotlib - The Foundation of Python Visualizations

07 - Understanding the basics of Matplotlib
08 - Creating custom plots with Matplotlib
09 - Challenge - Create a multipanel plot with Matplotlib
10 - Solution - Create a multipanel plot with Matplotlib

3. Seaborn - Statistical Visualizations Made Simple

11 - Introduction to seaborn and its advantages
12 - Creating statistical plots with seaborn
13 - Challenge - Create and customize a seaborn visualization
14 - Solution - Create and customize a seaborn visualization

4. Plotnine - Harnessing the Grammar of Graphics

15 - Getting to know Plotnine and the grammar of graphics
16 - Creating layered visualizations with Plotnine
17 - Challenge - Build a multilayer visualization with Plotnine
18 - Solution - Build a multilayer visualization with Plotnine

5. Practical Use Cases for Python in Excel Visualizations

19 - Building a moving average chart
20 - Creating dynamic measures with Python in Excel
21 - Challenge - Combine use cases for real-world scenarios
22 - Solution - Combine use cases for real-world scenarios
23 - Using Copilot to generate Python visualizations
24 - Challenge - Enhance AI-generated visualizations
25 - Solution - Enhance AI-generated visualizations

Conclusion

26 - Python, AI, and the future of data visualization in Excel