Data Within Reach

Read a SQL Query Without the Overwhelm

Care

athenahealth

Harvard Business School Online

Paint Nite

Emma Boroson

Data engineer, MBA, and educator -- teaching analytics without the overwhelm

Explain what a SQL query is doing and what business question it answers, even if you didn't write it.

Read a real SQL query and explain what it's answering

Spot filters, joins, and conditions that change results before trusting the numbers

Identify what data is included or excluded

Use a simple, repeatable method to get your bearings in any SQL query you're faced with at work.

Approach any SQL query with a clear process

Research asks. This listens. Find what people feel before they can name it — triangulate with receipts, then validate what matters.

AI Research Leader •Ex-Meta, Uber, Apple

Uber

Apple

Google

Canvas8

Abi Awomosu

AI Research Leader • Ex-Uber, Meta •  Bestselling Author of 'How Not to Use AI'

Cohort 6

Cohort 7

Most leaders don’t struggle with ideas or data. They struggle with knowing what matters before it’s obvious or too late.Most AI research courses teach extraction: summarize quicker, automate the asking. But AI doesn’t know what’s true. It knows what’s likely. Treat it like a truth machine and you get research slop at scale — confident outputs, weak evidence.The Billion Person Focus Group® does the opposite: you use AI as a listening partner to access patterns from places where people speak honestly—across public and internal evidence streams: forums, comments, support tickets, sales calls, product data. Triangulate. Bring receipts. Then hand off what you find to surveys, interviews, or experiments.Discovery first. Validation second.This isn't "move fast and break things." It's see first—build once.In two days, you’ll apply this to a real high-stakes decision and leave with:<ul><li>Visual market landscape maps</li><li>3–5 decision-ready hypotheses</li><li>A ready-to-run Listening OS — agents, workflows, cookbooks — customized to your market</li><li>A validation plan you can run on Monday</li></ul>And you keep everything — for the next launch, the next client, the next pivot. Stop renting insights. Start owning the capability.

See what your market really needs—before you build, launch, or scale. 

See your market as it actually exists— not how dashboards flatten it

Identify emerging market trends before they become mainstream.

Discover  the 'unknown unknowns' — what your market feels but can't say

Build AI advisors who challenge you — not yes-machines who confirm

Move at AI speed without losing the human in the data

Stop renting insights or endless AI tools — build internal intelligence you own

Access to Claude (preferably paid). Some familiarity with AI tools — you've used ChatGPT, Gemini or similar. No technical skills required.

Access to Claude and familiarity with AI tools

Strategic Leaders (Consultants, Heads of Marketing, Product, Strategy) who need to spot opportunities before competitors or de-risk big bets

Entrepreneurs and Innovators who want to stop guessing at product-market fit and start building for needs that actually exist

Senior Practitioners (Designers, Marketers, Strategists, Researchers) who want to ground their work in real signal — not assumptions

Stop Getting Overruled: Communication frameworks that position your recommendations as strategic decisions, not design preferences

January Cohort

Medium.com's Top Design Writer.

Kai Wong

Medium's Top Design Writer| 13 Years Designing in Low Maturity Organizations

December Cohort

February Cohort

You Know What Users Need. You've built the solution. So Why Does It Get Ignored? You've done the research. You've identified the problems. You've designed the solution.But it still gets treated like a 'suggestion', and de-prioritized.You're not here to win arguments: you're here to help people. But you can't help anyone if your work never makes it to production.The problem isn't your design. It's your communication.As one Design Director told me: "Designers get shot down, not because they do bad work, but because they can't communicate to businesses."That's what Design Storytelling with Data teaches: translating design work into business impact that gets you hired, trusted, and promoted.In this 4-week program, you'll learn to:<ul><li>Connect design decisions to business outcomes executives track</li><li>Create data visualizations that make problems impossible to ignore</li><li>Craft case studies that show strategic thinking, not just process</li><li>Present with frameworks that get teams to take action</li></ul>Your work is valid. You've seen the impact. All you need to do is translate your work into a narrative that gets buy-in.

Solid Design ideas, but no Buy-in? Translate Design Work into Business Outcomes.

Connect Design Decisions to Product Metrics

Translate Product Goals Into Measurable Signals and Metrics

Create Data Visualizations to illustrate critical problems

Identify Key Design Solutions that should be top Priority

Tell Effective Stories and Deliver Persuasive Presentations

Designers who want to use data to bypass office politics and prove their value immediately

Designers tired of watching 'strategic' roles like PMs get promoted while their insights are ignored

Designers in low-UX maturity environments where relationship-building isn't enough

As a data professional, you learn to use tools, crunch numbers, develop attractive charts, and create engaging presentations. But ... - What if your analysis does not hold water? 🌊- What if your conclusions are wrong? 😫- What if your audience gets the wrong message? ❗️ Mistakes in data reasoning can have disastrous consequences for yourself and your organization: loss of trust, reduced credibility, wrong decisions, and even harm. - Learning a new tool won't help- Learning to code won't help- Learning to create pretty charts won't help- Learning to tell more engaging "data stories" won't help What you need is a systematic framework to think effectively with and about data. WHAT YOU'LL LEARN1. How to spot issues in the data2. How to calculate the right numbers3. How to design the right representations4. How to design truthful framings THE COURSE INCLUDES- 6 live meetings (recorded and shared)- 5 pre-recorded video lectures and quizzes- 5 hands-on group activities- 1 final hands-on project (Note: The course does not require knowledge of any specific data processing or visualization tool.) WHO WILL BENEFIT MOST FROM THIS COURSEData professionals and enthusiasts who want to sharpen their data reasoning skills and produce more truthful charts. This includes a broad swath of professionals such as data analysts, data scientists, scientists and researchers, visualization designers, product managers, and data journalists. WHO THIS IS NOT FORThis course is not designed for:- Learning specific tools or libraries (Tableau, Power BI, Plotly, D3.js, etc.)- Learning how to make charts more aesthetically pleasing (even though that is important) The course is also unsuitable for individuals who prefer to work at their own pace and are not interested in interacting with others. The live meetings require everyone to be actively engaged in discussions, conversations, and collaborative work.

Gain confidence. Avoid costly mistakes.

Course syllabus

If your events are recurring and at the same time, it might be easiest to use a single line item to communicate your course schedule to students

Tuesdays & Thursdays

Feel free to type out dates as your title as a way to communicate information about specific live sessions or other events.

May 7, 2022

Schedule items can also be used to convey commitments outside of specific time slots (like weekly projects or daily office hours).

Weekly projects

Course schedule

Data analysts and data scientists who want to create more robust data insights and presentations.

Visualization designers who want to avoid misleading or confusing their readers.

Data enthusiasts who want to become better data thinkers and avoid being misled by data visualizations.

Who is this course for

What you’ll get out of this course

Learn a framework to systematicaly identify and avoid data reasoning and communication mistakes.

Framework for Effective Data Thinking

Design data visualizations that reduce the chances of confusing or misleading your audience.

Practical Data Visualization Skills

Enrich and test your knowledge with many practical examples, live hands-on activities, and a final project.

Hands-on Experience

Students interact and discuss a lot in this course. It's engaging, stimulating and fun!

Fun Interactions!

Spot and avoid data fallacies. Gain confidence, build trust, and protect your credibility with truthful data visualizations.

Enrico Bertini

Cohort 2

Cohort 3

Become the go-to data professional who prevents bad data, not just fixes it

Data Quality Leader

Strava

Komodo Health

Salesforce

Fitbit

Shailvi Wakhlu

Data leader at Meta with two decades improving data quality

Fall Cohort

Spring'26

Bad data is everywhere—and if you work with data, you’ve felt the pain.📉 Broken dashboards 🔁 Conflicting metrics 🧯 Late-night pipeline debugging 🗣️ Re-explaining numbers you know are wrongOver time, this erodes trust in your data—and in your work.Most data professionals are stuck in reactive mode. You fix issues after they’ve already caused damage, only to see the same problems resurface weeks later. That’s not a skill issue—it’s a systems issue.This course helps you change that.You’ll learn how to approach data quality systematically, not ad hoc:<ul><li>🔍 Identify where and why data quality issues originate</li><li>🧪 Diagnose problems quickly using structured SQL-based techniques</li><li>🛡️ Put guardrails in place so errors are caught before they impact decisions</li></ul>Just as importantly, you’ll learn how to make data quality visible and valuable. You’ll practice communicating the business impact of bad data so this work gets prioritized—not ignored.The result? ✅ Less firefighting 📊 More trust in your data 🚀 Greater confidence in the insights you deliverIf you’re ready to stop chasing bad data and start preventing it, this course gives you the playbook.

Stop firefighting bad data: prevent, diagnose, and fix it systematically

Prevent data quality issues before they reach stakeholders

Diagnose and debug data issues using a repeatable SQL approach

Assess data health across the full data lifecycle

Automate data quality checks to protect pipelines

Reduce firefighting and increase trust in your data

Influence teams to prioritize data quality initiatives

Data Analysts &amp; Data Scientists who want to prevent bad data instead of constantly fixing errors and dealing with misleading insights.

Data Engineers who want to build reliable pipelines with automated quality checks to ensure a trustworthy, high-integrity data environment.

Data Practitioners who need to secure company-wide buy-in for data governance initiatives that drive better business decisions.