Data Visualization with Claude Code

Hosted by Shane Butler, Sravya Madipalli, and Hai Guan

Wed, Mar 25, 2026

7:00 PM UTC (1 hour)

Virtual (Zoom)

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AI Evals for Product Development
Shane Butler
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What you'll learn

Apply a 3-question framework before picking a chart

Ask what decision the chart supports, what comparison it shows, and who's reading it—before touching any tool.

Use Claude to reason through visualization choices

Prompt Claude to diagnose your dataset and explain why one chart type fits the question better than another.

Build a polished dashboard with Claude Code in minutes

Turn a raw CSV into a multi-panel executive dashboard with annotated charts and a written recommendation-in one session.

Know what AI can and can't decide for you

AI executes visualizations instantly—but the question your chart answers is still your call. That judgment is the skill.

Why this topic matters

Most visualization mistakes aren't design problems—they're question problems. Teams spend hours polishing charts that don't support the decision they need to make. This lesson teaches the judgment to choose the right chart before you build it, then shows how Claude Code executes it in minutes. Less time in tools. More clarity in meetings.

You'll learn from

Shane Butler

Principal Data Scientist at Ontra

Shane Butler is a Principal Data Scientist at Ontra, where he leads evaluation strategy for AI product development in the legal tech domain. He has more than ten years of experience in product data science and causal inference, with prior roles at Stripe, Nextdoor, and PwC. Shane is also the co-host of the AI podcast Data Neighbor, where he interviews product, data, and engineering leaders who are pioneering the next generation of data science and analytics in an AI-driven landscape.

Sravya Madipalli

Senior Manager, Data Science @Superhuman (Prev. Grammarly)| Ex-Microsoft

Sravya Madipalli is a Senior Manager of Data Science with 14+ years of experience helping teams make better decisions with data. She has built and led data science and product analytics teams at Microsoft, eBay, Nextdoor, and Superhuman (prev. Grammarly), working closely with product, engineering, marketing, and leadership. Her expertise spans experimentation, metrics design, modeling, analytics, and translating complex user behavior into clear, actionable insights.

Hai Guan

Head of Data at Ontra

Hai Guan leads the data organization at Ontra, the leading legal tech AI solutions for private markets. He previously led Data Science & Analytics at LinkedIn, Nextdoor, Pinterest, and Meta. He's spent a decade teaching product development teams how to ask questions that actually drive decisions—and now teaches how to combine that judgment with AI to move 10x faster.

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