Python for Data Science - Data Visualization
Three Different Data Visualization Types
- Data storytelling - for presentations to organizational decision makers
- Make it easy for the audience to get the point
- Your data visualization should be:
- Clutter-free
- Highly focused
- Intended audience:
- Nonanalysts
- Nontechnical business managers
- Product types:
- Static images
- Simple interactive dashboards
- Optimal graphics:
- Area Charts
- Bar Charts
- Line Charts
- Pie Charts
- Cloropleths
- Point Maps
- Data showcasing - for presentations to analysts, scientist, mathematicians, and engineers
- Showcase lots of data so your audience members can think for themselves
- Your data visualization should be:
- Highly contextual
- Open ended
- Intended audience:
- Analysts, quants
- Engineers, mathematicians, scientists
- Product types:
- Static images
- Interactive dashboards
- Optimal graphics:
- Area Charts
- Bar Charts
- Line Charts
- Cloropleths
- Point Maps
- Histograms
- Scatter Plots
- Scatter Plot Matrices
- Raster Maps
- Data art - for presentations to activists or to the general public
- Use your data visualization to make a statement
- Your data visualization should be:
- Attention getting
- Creative controversial
- Intended audience:
- Idealists, dreamers, artists
- Social activists
- Product types:
- Static images
- Optimal graphics:
- Line Charts
- Graph Networks
- Cloropleths
- Something Weird and Artistic...
Communicating with color and context
Color should be used:
- Strategically
- Sparingly
- Consistently
You want to use color to draw attention to the parts of the visualization that matter, and away from the parts that don't
Creating Context
- How: add data on additional metrics that are relevant to the datasets you're showing, trendlines, colors, and annotations.
- Why: meant to give audience some deeper perspective and insight into what's happening
- When :useful in data showcasing