Presented By
O’Reilly + Cloudera
Make Data Work
29 April–2 May 2019
London, UK

Visually communicating statistical and machine learning methods

Michael Freeman (University of Washington)
12:0512:45 Wednesday, 1 May 2019
Secondary topics:  Visualization, Design, and UX
Average rating: ****.
(4.18, 11 ratings)

Who is this presentation for?

  • Data scientists, managers, visualization specialists, and anyone else interested in learning how to better communicate statistical and machine learning techniques
  • Level


    What you'll learn

    • Learn how to design a visual representation of your statistical and machine learning methods


    As statistical and machine learning techniques become part of nearly all data-driven organizations, practitioners have a responsibility to communicate about the assumptions and mechanics of their approaches (not just their conclusions). The abundance of statistical packages in languages such as R and Python have made these analysis strategies easier than ever to implement. However, there’s been comparably little development in the platforms for communicating the assumptions, limitations, and intentions of these models to broader audiences. This communication gap greatly diminishes the usability of pertinent data insights.

    In order to make accurate and responsible data-driven decisions, decision makers need to have a more robust understanding of the analytical processes applied to their data. Practitioners are responsible for creating resources for amplifying the understanding of the individuals that consume our data. Visualization provides a powerful tool not just for expressing our data, but for expressing our methods as well.

    Michael Freeman details a process for designing and building visual representations of statistical and machine learning concepts. Drawing upon keynote examples of visual explanations of analytical techniques, Michael introduces a set of steps for visually communicating complex topics. By isolating specific concepts and mapping them to a data structure, analysts are able to visualize the underlying concepts of interest. Leveraging core concepts in visualization theory, you’ll learn optimal processes for selecting visual encodings to express analytical concepts. Whether you’re using D3.js or a whiteboard, identifying a data structure that represents a concept can better enable you to express ideas to your specific audience.

    Photo of Michael Freeman

    Michael Freeman

    University of Washington

    Michael Freeman is a senior lecturer at the Information School at the University of Washington, where he teaches courses on data science, data visualization, and web development. With a background in public health, Michael works alongside research teams to design and build interactive data visualizations to explore and communicate complex relationships in large datasets. Previously, he was a data visualization specialist and research fellow at the Institute for Health Metrics and Evaluation, where he performed quantitative global health research and built a variety of interactive visualization systems to help researchers and the public explore global health trends. Michael is interested in applications of data visualization to social change. He holds a master’s degree in public health from the University of Washington. You can find samples from his projects on his website.

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    Picture of Michael Freeman
    Michael Freeman | LECTURER
    23/05/2019 16:39 BST

    All presentation resources are available here!

    Rebecca Hile |
    22/05/2019 16:21 BST

    Hi Michael, very much enjoyed your session. Do you plan on sharing your slides or a link ?