This one day course develops a strong foundation in Python frameworks for data visualization (principally Matplotlib, Seaborn, Bokeh and Datashader). Topics include statistical plots, working with time series and real time data, web visualizations, and big data visualizations. The course mixes presentation with hands on exploration in roughly equal parts.

What You’ll Learn

  • How to construct two-dimensional graphics (e.g., line/scatter/bar plots, surface plots, heatmaps, etc.)
  • How to produce statistical plots of multivariate datasets
  • How to generate interactive dashboard applications with live data streams
  • How to generate visualizations of Big Data (in excess of one million points)

Topics Covered

  • Graphics and plotting terminology and best practices
  • Core features of MatplotlibBokeh, and Seaborn packages
  • Annotation and customization of graphics objects
  • Interactive charts
  • Big data visualization pipeline with datashader

Who Should Attend

Data analysts and practitioners wanting to automate or improve their data visualization workflows.

This course has a limit of 20 participants.


Participants need to be well versed with array based computation in Python using NumPy and tabular data manipulation with Pandas. A robust knowledge of statistical plotting techniques is an asset but not mandatory.