Data Neighbor

Cohort 1

Cohort 2

Bootcamp 1

Stop measuring outputs. Start measuring outcomes, then convert them to revenue. I’m attaching our simple framework below: "The AI Impact Chain". It’s an intuitive approach anyone can grasp, backed by measurement methods that have been used through decades of data science, statistics, and econometrics.

Guide: Forecast Revenue with AI Eval Metrics

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AI demands a new set of metrics. It requires evolving unreliable deterministic metrics to a systematic analytics process that rigorously ties probabilistic AI output to user value and business impact. In Part I of this guide, we establish the foundational system of metrics required for deterministic applications where the same input reliably produces the same output. In Part II, we walk through how to redesign that measurement framework for probabilistic AI Systems.

Guide to Redesigning Metrics for AI Products

Most AI features fail because the link between model accuracy and business value is broken. This worksheet aligns Product and Data Science teams to map the causal chain: Quality → Behavior → Business Outcome. Includes templates for "Impact Math" forecasting, pre-mortems, and decision gates. Verify your feature's value before you write a single line of code. Download now to turn models into measurable wins.

AI Impact Chain - Planning Worksheet

Leave with a working AI analyst system on your laptop (skills, agents, data connections) and a finished analysis ready for Monday.

Principal Data Scientist @ Ontra 

Shane Butler

Principal Data Scientist @ Ontra | Ex-Stripe, Nextdoor, PwC, Appfolio

Data Science Leader |Ex-Microsoft

Sravya Madipalli

Data Science Leader@Superhuman (Prev. Grammarly)| Ex-Microsoft, eBay, Nextdoor

Head of Data @ Ontra AI | Ex-LinkedIn

Hai Guan

Head of Data @ Ontra | Ex-Nextdoor, LinkedIn, Pinterest, Meta

This is a hands-on weekend bootcamp (4 hours Saturday, 4 hours Sunday) where you build AI analysts powered by Claude Code. Not a lecture series. Not a demo you watch. You set up your own environment, build custom skills and agents, connect to real data, and leave with a working system that turns business questions into charts, narratives, and presentations in minutes.No Python. No SQL. No coding of any kind. Claude Code writes all the code. You direct the analysis.By Sunday evening, you will have a complete AI analyst repo on your computer: 11 skills, 15 agents, data connections, and a finished analysis you can share with your manager Monday morning. Everything runs locally. No vendor lock-in. No proprietary platform. Just a repo you own.AI is changing how teams make decisions with data. The bottleneck is no longer the analysis — it is knowing how to set up the tools. This bootcamp gives you that setup in a weekend. You will not learn about agentic systems. You will build one.

Build agentic data analysts for your company in one weekend. No code.

Set up Claude Code as your personal AI analyst environment

Build custom skills that encode analytics best practices as markdown

 Build agents that automate multi-step analysis workflows

Produce a complete analysis with charts, narrative, and slide deck

Connect your own company data and analyze it before you leave

Product managers who are tired of waiting in the data team's queue. Stop waiting. Analyze it yourself.

Data scientists who want to multiply their output. You know how to do the analysis manually. Now automate it.

Engineers who want to make data-driven decisions without hiring a data team.

Learn practical, hands-on methods to evaluate, measure, and improve AI products using data science to make better product decisions.

Principal Data Scientist, AI Evaluations

Stripe

Nextdoor

PwC India

Ontra

AppFolio

Principal Data Scientist, AI Evaluations at Ontra

This course equips you with the capability to measure, diagnose, and improve AI features in production. Specifically, it is the evolution of product data science and analytics for a world where AI product behavior is probabilistic, multi-step, and harder to observe. The focus is on measuring whether AI features actually deliver user value and business impact, and knowing what to do when they do not.If you already have product analytics foundations, the course shows how to adapt those foundations for AI systems. You do not need to be an AI specialist. Anyone can learn this. This however is not a foundations course about AI. You will get the most out of it if you are actively working on, or about to ship, AI features in a real product.-&gt; -&gt; -&gt; Join our <a target="_blank" rel="noopener noreferrer nofollow" href="https://join.slack.com/t/dataneighbor/shared_invite/zt-3ox4drc3r-3g0ptSthytkgv0sD6ovvqg">AI Builders Slack</a> community Work through AI eval design, learn Agentic Analytics workflows, and get feedback on what you’re building from other AI builders. <a target="_blank" rel="noopener noreferrer nofollow" href="https://join.slack.com/t/dataneighbor/shared_invite/zt-3ox4drc3r-3g0ptSthytkgv0sD6ovvqg">Click Here to Join Slack Community!</a>

Master the data-driven workflow behind reliable, high-quality AI products.

Make AI systems observable for product evaluation

Diagnose AI failures that block user success

Measure AI impact on user value and business outcomes

Operationalize evaluation across development, release, and production

Make better product decisions and drive AI product impact

You will get the most out of it if you are actively working on, or about to ship, AI features in a real product.

Real-World AI Product Experience

Product Managers: Shipping AI but can't prove impact or define good? Get a repeatable eval plan with metrics and decision rules.

Data Scientists and Analysts: Evals feel ad hoc and hard to trust? Build a system that links offline quality to user and business outcomes.

Engineers and ML Engineers: Evals live in notebooks and regressions slip? Operationalize with checks, monitoring, and tight feedback loops.

Transform from consumer of analytics to independent operator - asking sharp questions, validating answers, shipping decisions.

Microsoft

eBay

Grammarly

Superhuman

LinkedIn

Pinterest

Ask Better Questions, Make Faster Decisions - with AI as Your Analyst

Frame Problems Like a Senior Data Scientist

Design Metrics That Actually Drive Decisions

Diagnose Root Causes Without Guessing

Design Experiments That Ship

Turn Analysis Into Action With AI as Your Copilot

Synthesize Insights and Prioritize for Impact

Product Managers, engineers, and operators who need analytical independence and frameworks to measure success and drive decisions.

Designers and researchers stepping into strategy roles who need to quantify impact, run experiments, and influence with data-driven insights

Data professionals who want to frame better questions, delegate technical execution to AI, and drive impact with agentic analytics tools.

Run and Analyze Experiments with CIaude Code

Senior DS Leader, Ex-Microsoft

Head of Data at Ontra, Ex-LinkedIn

Principal Data Scientist at Ontra

Learn a practical framework to design experiments that are aligned to real decisions, not just statistical outcomes.

Running experiments with a clear, decision-focused framework

Use AI to review experiment setup, metrics, assumptions, and results to assess trustworthiness and actionability.

How to run experiments using a clear, decision-led framework

Learn how AI helps surface design flaws, metric issues, and interpretation risks, to know when to ship, iterate, or stop

Identify gaps and risks before acting
on experiments with AI

Learn Agentic Analytics workflows and AI Eval design. Get feedback on what you’re building from other AI builders.

Join AI Builders Slack Community. Link: bit.ly/ai-connect

Storytelling & Presentation Building Using Claude Code

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

Head of Data at Ontra

Use a 4-point checklist to cut data dumps into focused narratives with clear asks.

Structure data findings into executive stories

Go from raw data to a complete slide deck with charts, narrative, and recommendations in minutes.

Build stakeholder presentations with Claude Code

Use an AI story structurer to identify the so-what, choose the right evidence, and craft a clear ask.

Plan your data story before building the deck

Build Custom Annotation UIs for AI Evals

Know when you need annotations for ground truth, production review, and continuous discovery.

Identify where annotation fits in the AI evaluation lifecyle

Choose the right interface patterns so reviewers can judge quality without fighting the UI.

Design annotation tasks that match the evaluator’s workload

Specify what the UI must show, what data it reads and writes, and how to standardize to make work is repeatable.

Define a lightweight “custom UI” blueprint that scales

Data Visualization with Claude Code

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

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

Apply a 3-question framework before picking a chart

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

Use Claude to reason through visualization choices

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

Build a polished dashboard with Claude Code in minutes

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

Know what AI can and can't decide for you

Design an AI Evaluation Plan

Decompose a real AI feature into inputs, context, outputs, UX, and outcomes, and list what to evaluate at each step.

Break an AI feature into evaluable steps

For each step, specify what evidence would justify ship, iterate, or stop, and what “evidence” is misleading.

Define evidence that supports a ship decision

Spot what’s missing in logging, annotation, or metrics, then capture it as clear next steps in the plan.

Identify gaps that block confident evaluation

Design Product Analysis with Claude Code

Use Claude Code to formalize the decision, success metrics, guardrails, and “what changes our mind” thresholds.

Turn vague requests into decision-grade briefs

Generate a structured 1-pager: scope, cohorts, hypotheses, methods, outputs, plus risks and mitigations.

Build a full analysis design doc in Claude Code

Surface confounders, selection effects, baseline gaps, and scope creep. Add checks that reduce risk of failure.

Validate the plan in Claude Code before execution

Crack Product Sense & Analytics Interviews with AI

Senior DS Leader (Ex-Microsoft)

Practice product sense and analytics questions with AI that pushes, probes, and mirrors real interviews.

Work with AI as an interview prep partner

Learn how to use AI to evaluate your responses across scoping, metrics, tradeoffs, and decisions

Assess your interview skills and pinpoint gaps

Structure answers consistently and use AI to test, refine, and improve them across repeated practice.

A clear framework for product sense & analytics interviews

Measure AI Impact with Causal Inference

Use it when you cannot randomize but still need decision-grade evidence for product or business impact.

Know when you need causal inference

Panel regression, diff-in-diff, and propensity matching: what each assumes and when each is appropriate.

Choose among three practical methods

Learn simple checks to spot confounding risk and communicate uncertainty without hand-waving.

Stress-test credibility before you trust it

Analyze Product Data w/ Claude Code Opus 4.6

Analyze conversion or retention by segment, spot where drop-offs happen, and quantify where impact is coming from.

Run a deep dive analysis in Claude Code

Pressure-test the analysis with sanity checks and sensitivity cuts so conclusions hold up in reviews.

Validate results and catch common AI analysis errors

Translate the analysis into structured presentation of “what changed, why, who it affects, and what we should do next”.

Turn findings into a clear product recommendation

Frame Questions That Drive Decisions with AI

Distinguish between questions that lead to actionable insights and questions that just generate busywork.

Identify questions that drive decisions over noise

Learn the 3-part framework that transforms fuzzy instincts into specific, decision-driving questions that AI answers.

Apply the framework transforming ideas to AI-ready analysis

Master the meta-workflow for using AI to refine questions, sanity-check outputs, and know when to trust AI.

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