Building a Great Data Visualization Portfolio


This workshop will focus on key skills for making great data visualization whether you're doing it for exploratory or explanatory purposes. You'll see how different roles do data visualization, techniques for displaying complex data types in actionable ways, as well as understanding principles of design and collaboration in the data visualization creation process.

Session Outline:

Session 1: Principles of Effective Explanatory and Exploratory Data Visualization
Understand the differences between exploratory and explanatory approaches in data visualization, with an emphasis on the complex data structures typical to data science. This includes visualizing uncertainty, significance and anomalies.

Session 2: Differences in Data Visualization Approaches Based on Role
Most workshops on making good data visualization assume that all charts are built for the same purpose but that's not the case in modern data-driven organizations. This session will describe the differences in approaches to data visualization by data scientists, data engineers and analysts with an emphasis on techniques that can be adopted from across these roles to make any chart more effective.

Session 3: Collaboration in the Production of Data Visualization
Data scientists typically think of themselves as working alone to produce charts but that perspective can limit your effectiveness. By seeing the pain points in interactions between stakeholders and peer collaborators, you'll learn how to make data visualization that has an impact in the moment but which also has an impact on the organization beyond a single decision point.

Background Knowledge:

Working knowledge of how to create data visualization products using tools or code.
Experience with non-numerical data and patterns.


Elijah Meeks is a co-founder and Chief Innovation Officer of Noteable, a startup focused on evolving how we analyze and communicate data. He is known for his pioneering work in the digital humanities while at Stanford, where he was the technical lead for acclaimed works like ORBIS and Kindred Britain. He was Netflix's first Senior Data Visualization Engineer, and while at Netflix and Apple worked to develop the charting library Semiotic as well as bring cutting-edge data visualization techniques to analytical applications for stakeholders across the organization including A/B testing, conversation flows, algorithms, membership, people analytics, content, image testing and social media. He is a prolific writer, speaker and leader in the field of data visualization and the co-founder and first executive director of the Data Visualization Society.

Open Data Science




Open Data Science
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Cambridge, MA 02142

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