Applied Plotting, Charting & Data Representation in Python Course Syllabus

Overview: This course provides a balanced, practical introduction to data visualization in Python, combining foundational design theory with hands-on coding. Learners will gain proficiency in creating clear, impactful visualizations using Matplotlib, Seaborn, and Pandas. The curriculum spans four core modules and a final project, totaling approximately 22 hours of content. Each module blends conceptual understanding with coding exercises, culminating in a capstone project where learners apply their skills to real-world datasets. Ideal for those with basic Python and Pandas knowledge seeking to enhance their data storytelling abilities.

Module 1: Principles of Information Visualization

Estimated time: 3 hours

• Visualization design principles from Edward Tufte, including data-ink ratio
• Understanding Cairo’s visualization wheel and its application
• Recognizing truthful vs. misleading charts
• Hands-on peer-reviewed exercise critiquing misleading visualizations

Module 2: Basic Charting

Estimated time: 7 hours

• Working with real-world CSV data in Python
• Creating line plots and scatterplots using Matplotlib
• Overlaying scatter plots on line charts for comparison
• Plotting weather records and highlighting recent outliers visually

Module 3: Charting Fundamentals (Advanced)

Estimated time: 8 hours

• Building subplots for multi-chart layouts
• Creating histograms and boxplots for distribution analysis
• Generating heatmaps to show correlation and intensity
• Introduction to animations and basic interactive visualizations

Module 4: Applied Visualizations

Estimated time: 4 hours

• Using Seaborn for statistical plotting and styling
• Leveraging Pandas for built-in plotting methods
• Selecting appropriate chart types for effective storytelling

Module 5: Final Project

Estimated time: 5 hours

• Define a research question using at least two datasets
• Create a comprehensive visualization to answer the question
• Apply design principles and tools (Matplotlib, Seaborn, Pandas) learned throughout the course

Prerequisites

• Basic knowledge of Python programming
• Familiarity with Pandas for data manipulation
• Understanding of fundamental data structures and data types

What You'll Be Able to Do After

• Apply visualization design heuristics to create clear and truthful charts
• Use Matplotlib to produce line plots, scatterplots, bar charts, and overlays
• Create advanced visualizations including histograms, boxplots, heatmaps, and subplots
• Leverage Seaborn and Pandas for statistical plots and clean styling
• Develop a complete data visualization project using real-world datasets