Data Visualization Fundamentals - A Practical Guide

Understand aspect ratio and scale choices that affect how trends are perceived in data visualization.

Learn how color influences interpretation and how to choose effective palettes in professional chart design.

Design inclusive charts using accessibility principles and color vision considerations

Learn how to guide attention using annotations without cluttering the visualization.

Understand how clear labeling improves interpretation and avoids confusion

Learn how to communicate projections and estimates responsibly in data visualization

Remove unnecessary elements and improve clarity using professional decluttering methods.

A summary of key design rules that improve chart clarity and impact.

Learn how to define the core message before designing your visualization.

Discover how to sequence charts logically to build persuasive data stories.

Understand when to use a focused chart and when a dashboard is more appropriate.

Learn how scaling and visual exaggeration can distort meaning.

Understand how visual change can misrepresent numerical change.

Avoid false conclusions by understanding the difference between correlation and causation.

Recognize hidden biases in datasets that affect visual interpretation.

Learn how improper scaling and comparison can mislead audiences.

Understand the 45-degree banking principle and its impact on trend perception.

Learn when adjusting axes improves clarity — and when it becomes misleading.

Understand how to visualize uncertainty using standard deviation, confidence intervals, and margins of error.

A concise summary of common visual distortions to avoid.

Overview of the most common charts used in professional data visualization.

Line Chart: The gold standard for showing trends over time, focusing on clarity and continuous data flow.

Area Chart: Learn how to highlight the "volume" of data over time by filling the space beneath your trend lines.

Bar Charts: The ultimate tool for comparison; learn when to go horizontal, vertical, or stacked.

Scatterplots: Master the relationship between two variables to identify correlations and outliers in your dataset.

Bubble Chart: Add a third dimension of data by using size to represent volume on a standard scatterplot.

Histogram: Learn how to visualize frequency and distribution to understand the "shape" of your data.

Density Plot: A smoother alternative to the histogram for identifying where values are most concentrated.

Pie Charts: Understand the limitations of "part-to-whole" visuals and when (if ever) you should actually use them.

Waterfall: Master the "Bridge" chart used in finance to explain the journey from a starting total to a final result.

Combo Charts: How to overlay bars and lines to show two different metrics in a single, cohesive visual.

Maps: Discover how to anchor your data to geographic locations to reveal regional patterns and clusters.

A summary of which chart type to choose based on the specific question you are trying to answer.

Practice extracting insight from raw data.

Design a chart that clearly communicates your insight.

Craft an action title that reflects your analytical conclusion. Do all finishing touches to the chart we created (combo chart)

Strengthen your analytical reasoning before visualizing

Design the chart, use axis breaks to showcase data clearly on the waterfall chart and turn your analysis into a persuasive data story headline.

Thank you and congratulations for reaching the end. What's next?

This is a bonus lecture for the course