Do you know why is it so important to create a simple yet informative data visualization?
How your design choices impact the visuals?
How small details – like text alignment – influence the overall visualization?
How gestalt principles guide you?

In episode 3 of this mini-series, I discuss the impact of simplified data visualization and how to achieve it.

Specifically, I address these factors that affect your visualization:
  • Clutter
  • Mis-alignment
  • Too many elements like
    • Colour
    • Fonts
    • Annotations
    • Header, subheader, sub-subheader
  • “Unnecessary” annotations
    • File names
    • Source data
    • Program name
    • Validation status
  • Understand gestalt principles
    • Proximity
    • Similarity
    • Enclosure
    • Continuity
    • Connection
    • Closure

Learn more on how to create simple but effective data visualization by listening to this podcast and share this with others who might learn from it!

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I want to help the community of statisticians, data scientists, programmers and other quantitative scientists to be more influential, innovative, and effective. I believe that as a community we can help our research, our regulatory and payer systems, and ultimately physicians and patients take better decisions based on better evidence.

I work to achieve a future in which everyone can access the right evidence in the right format at the right time to make sound decisions.

When my kids are sick, I want to have good evidence to discuss with the physician about the different therapy choices.

When my mother is sick, I want her to understand the evidence and being able to understand it.

When I get sick, I want to find evidence that I can trust and that helps me to have meaningful discussions with my healthcare professionals.

I want to live in a world, where the media reports correctly about medical evidence and in which society distinguishes between fake evidence and real evidence.

Let’s work together to achieve this.