Lecture Notes: Introduction to Data Visualization

Introduction

• Speaker: Jessica Pucci
• Session: Study Hall Data Literacy by Arizona State University and Crash Course
• Focus: Understanding data visualization and potential distortions

The Importance of Data Visualization

• Analogy: "A picture is worth a thousand words"
• Anscombe's Quartet: Demonstrates different datasets with identical statistical properties but different visualizations
• Purpose: Visualizations help spot connections, patterns, trends, and outliers that may not be apparent in raw data
• Caution: Poor visualization can obscure data truths

Types of Data Visualizations

• Line Chart: Used for time series data to show trends over periods
• Pie Chart: Focuses on proportions and parts of a whole
• Bar Chart: Compares different categories (e.g., bird colors)
• Histogram: Shows distribution of a continuous variable using bins
  • Importance of appropriate bin size
• Scatterplot: Examines correlation between two variables
• Maps: Visualize spatial distribution of data

Choosing the Right Visualization

• Objective: Decide the best way to represent the data's story
• Example: Weather data involving temperature and rainfall
  • Use histograms for distribution
  • Line charts for trends
  • Scatterplots for investigating relationships

Tools for Visualization

• Software: Data Wrapper, Google Data Studio, Tableau, Python, R
• Considerations: Emphasize relevant data parts effectively without misleading

Data Visualization Distortions

• Axis Manipulation: Can distort perception of data
  • Breaking the vertical axis or not starting at zero
• Baseline Adjustments: Can mislead in various chart types
• Pie Chart Miscalculations: Incorrect total percentages
• Text Influence: Sensational headlines affecting perception

Chart Design Pitfalls

• Chart Junk: Unnecessary design elements (lines, colors, symbols)
• Use of 3D Graphics: Often unnecessary and misleading
• Color Choices: Consider accessibility (color blindness)

Conclusion

• Key Takeaway: Data literacy involves careful questioning and visualization analysis
• Future Sessions: Will cover data collection more deeply

Closing

• Call to Action: Subscribe and continue learning about data literacy
• Produced by: ASU and Crash Course at Complexly
• Resource Links: Available in video description