Data visualization with Python

In this course, you'll learn how to visualize data in Python in a compelling and easy-to-understand way.

From the basics with Pandas and Matplotlib to elegant statistical plots with Seaborn and interactive dashboards with Plotly - step by step you will learn the most important tools of modern data visualization.

Discover best practices, create your own visualizations and take your analyses to a new level - in a practical way so that what you learn can be applied directly in everyday life.

1. A Refresher on Data Analysis with NumPy and Pandas

• Overview: Aim and procedure of the course
• Data analysis with NumPy        
  • Arrays, data types, indexing
  • Relevance for numerical data visualization
• Level-up with pandas        
  • Series and DataFrames
  • Import data (CSV, Excel)
  • Applied Data Exploration (head, describe, info, isnull, value_counts)
  • Simple corrections
• Mini-exercise: First analysis of a real data set (e.g. Titanic, Iris)

2. basics of visualization with Matplotlib

• Philosophy of Matplotlib
• Plot types:        
  • Line, scatter, bar, histogram, boxplot
• Axes, titles, legends, colors, styles
• Subplots and layouts
• Saving plots (PNG, PDF, SVG)
• Mini-exercise: Create different plots with real data

3. data analysis and visualization with Seaborn

• Why Seaborn? High-level vs. low-level APIs
• Plot types:        
  • count plot, box plot, violin plot, histogram, scatter plot, pair plot, heat map
• Context analysis:        
  • Visualize correlations (heatmap)
  • Visualizing Relational and Categorical Data
• Design and Style Customization
• Mini-exercise: Explorative data analysis with Seaborn

4. visual storytelling and best practices

• Difference between exploratory and explanatory visualization
• Visual principles:        
  • Colors, axes, scaling, annotations
• Common Pitfalls: What Makes a "Bad" Graphic?
• Introduction to Design Guidelines (Data-to-Ink Ratio, Tufte Principles)
• Mini-Exercise: Improving Poor vs. Good Visualization

5. interactive visualization with Plotly

• Why interactive visualization?
• Introduction to Plotly Express        
  • px.scatter, px.bar, px.line, px.histogram, px.box
• Interactive features: Zoom, hover, tooltips
• Colors, facets, animations
• Mini exercise: Attractive and interactive dashboards with Plotly Express

6 Plotly Graph Objects & Dash Introduction

• Plotly Graph Objects (GO) vs. Express        
  • Flexibility and customization
• Introduction to Dash (Conceptual)        
  • Building a simple dashboard
• Layouts and callbacks (demo)
• Mini-exercise: Visualize a small interactive report

7. further tools & special visualizations

• Altair: Declarative Visualization
• Bokeh: Interactive Web Plots
• Geopandas & Folium: Maps and Geographic Data
• Wordclouds & Networks
• When to use which tool? Overview & comparison table

8th mini project

• Mini-project (individually or in small groups):        
  • Analyze, visualize and present data
  • Use at least 2 libraries
• Goal: Communicate insights from data visually
• Short presentations & feedback round
• Conclusion: Q&A, tips for further learning

Requirement:

Basic knowledge of Python programming is a plus

Additional info: 

The following must be installed by the participants in advance:

• Python (at least version 3.10+)
• A Python IDE (e.g. PyCharm or VSCode)
• Sufficient host rights to be able to install packages with pip