Data Within Reach

Making Sense of SQL (even if you're not an engineer)

Care

athenahealth

Harvard Business School Online

Paint Nite

Emma Boroson

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

Translate a real SQL query into plain English so you’re not guessing, when it shows up at work.

Follow what a SQL query is actually doing

Learn where to start, and how to spot the few parts of a SQL query that tell you what's really going on

Know where to focus (and what you can ignore)

Build confidence and credibility to keep up and participate when SQL comes up in the conversation

Stop feeling lost or intimidated

Students will learn the AI literacy to evaluate AI ideas, surface risk, and make sound judgments without becoming technical specialists.

Modernizing Business Analysis

Angela Wick

25+ years practicing, shaping, and teaching analysis, product, and requirements.

AI Literacy for Business Analysts

AI usage, productivity, and implementation risk often come from poor assumptions about what AI can and cannot do. It also stems from poor analysis and organizational dynamics in which stakeholders ask for solutions that are either too specific or too generic, and pressure teams to deliver them, rather than solving real problems.Analysis professionals have a huge role in this dynamic that can change everything!Analysts are increasingly expected to participate in AI decisions, yet most training focuses on tools or stays abstract. This course fills the gap in understanding AI capabilities: Not from a technical point of view, but from a capability point of view; business uses and patterns of functional usage.You will build a grounded understanding of AI capabilities, limits, and failure modes, and learn how requirements shift toward hypotheses, experiments, and continuous outcomes in AI-enabled work.This course explores the skills of:🎯 Identifying the right AI use cases for specific business scenarios🎯 Distinguishing between deterministic and probabilistic systems and deciding where each should be applied in a workflow🎯 Questioning and communicating AI use case fit and risks

Giving BAs AI Fluency & Literacy to Use, Analyze, and Recommend AI Solutions

Major AI capability types and their appropriate uses

GenerativeAI is just one piece of AI, and much more than "chat".

What AI is good at and where it fails

Generative AI and its Data Sources (Public vs. Private)

How requirements shift to hypotheses and experiments

Where humans must remain accountable in AI-enabled systems

Business Analysts, Product Owners, Business Systems Analysts, Systems Analysts, Business Process Analysts, and similar roles.

BA Leaders redefining roles for AI-enabled teams

Business Leaders, Project and Product Professionals pulled into AI initiatives without clear accountability

Gain Strategic Engineering Advantage in the age of AI

Product Leader with 15+ years experience

Mihaela Draghici

Engineering Leader 20 years experience

Daniel Ghilea

Engineering Leader 20+ years experience

PublicisSapient Cohort

June Cohort

A half-day workshop, designed for senior engineers who want move from the "how" of engineering to the "why" of business impact and get a competitive advantage in a time when AI is making pure execution less differentiated. ----At senior level, the ceiling is rarely technical. You’re a strong engineer. You're already great at execution.You write solid code and review code at a high standard.You solve hard technical problems.You design scalable systems. You ship complex features.ButYou may struggle to:<ul><li>Connect architecture decisions to revenue or risk.</li><li>Challenge roadmap choices using business language.</li><li>Influence prioritisation without positional authority.</li><li>Translate technical language into strategic urgency.</li><li>Participate meaningfully in discovery discussions.</li></ul>The result?That limits your influence, your growth, and your impact as a strategic contributor.Because no one trained you in product thinking from an engineering lens. To move into strategic alignment, you need to stop treating product sense as a "PM skill" and start considering it as a technical optimisation for business impact.This workshop is designed to bridge the gap between code and commercial reality.

Learn how to connect technical decisions to business impact, speak clearly in strategy talks, and influence what gets built, not just how.

Understand what product sense really means

Understand the hierarchy of decision layers: Strategy To Roadmap To Backlog

How to choose and implement the right metrics

Speak the language of product and business confidently

Senior Engineers &amp; Tech Leads already making architecture decisions who want more influence over product and roadmap decisions.

Aspiring Staff / Principal Engineers who need to move from execution to shaping technical and product strategy.

Engineers stepping into leadership who are expected to contribute to business and roadmap discussions, but were never taught how.

Master SQL for real-world data science interviews

Seasoned data scientist & educator

Atlassian

Pinterest

Dropbox

Google

eBay

Anirban Bhattacharyya 

Seasoned Data Scientist with 10+ years solving product analytics at scale.

Alpha Cohort

Many aspiring data scientists know basic SQL, but struggle when faced with the kind of messy, ambiguous, business-driven questions that come up in interviews. The challenge is rarely just writing syntax. It is knowing how to translate a product question into the right metric, choose the right grain of analysis, join data correctly, avoid double counting, and explain your reasoning clearly.That gap can hold back otherwise strong candidates. You may understand SQL concepts in theory, but still feel stuck when asked to calculate retention, analyze an experiment, or debug a flawed query in real time.This workshop is designed to close that gap. Instead of teaching SQL as a list of commands, it teaches SQL the way it is actually used in data science interviews: through realistic product, growth, and analytics problems. Students will build confidence solving hands-on exercises that reflect real interview settings, from fundamentals to advanced topics like window functions and recursive CTEs.The goal is to help students move beyond memorizing syntax and become interview-ready problem solvers who can write clean SQL, reason through edge cases, and communicate like strong data scientists.

Master SQL interviews, think like a data scientist, and turn messy business questions into clear, confident analysis.

Solve SQL interview questions with structure:

Translate business problems into metrics

Write cleaner analytical SQL

Use joins, windows, cohorts, and funnels

Avoid common interview mistakes

Explain your approach with confidence

Aspiring Data Scientists Learners preparing for data science and analytics interviews who want to build strong, practical SQL skills.

Analysts Moving Upmarket Data and business analysts who know basic SQL and want to tackle more advanced, interview-style problems.

Product-Focused Data Professionals Data scientists and product analysts who want to sharpen SQL for funnels, retention, experimentation

Ask the right questions and spot the hidden landmines in AI data analysis -- without code or math.

CoS, data scientist, and UPenn prof.

DP 140th

GW University

William & Mary

Nicky Bell, PhD

Chief of Staff | 2x data team builder | 10 years teaching experience

Founding Cohort

<h2>Is This You? </h2><ul><li>You're a Chief of Staff or BizOps leader expected to analyze and report data that shapes executive decisions.</li><li>You can build a spreadsheet, but you have no formal data training and don't see yourself as a "data person."</li><li>AI didn't take your data work away, but suddenly the room rewards a fluency you were never trained for.</li><li>You looked at other courses and they all wanted to teach you to code.</li></ul>This course teaches the judgment that makes AI-generated analysis trustworthy. You'll see blind spots, ask sharper questions, and become the operator the C-suite trusts when AI is on the agenda.Own your analysis and command the room.*****🎓 What You'll Get<ul><li>1:1 coaching — 30 minutes to map your organization's data challenges and leave with an actionable blueprint.</li><li>Live audits of real AI-generated outputs — submit a report from your own data and watch 2–3 anonymized examples get evaluated and rebuilt, live.</li><li>More than 7 hours of video + classroom instruction.</li><li>Early access to Circuit — Apply your new judgment with one click, free to Founding Cohort members.</li></ul>

PhD-level judgment, without the PhD-level pain.

Spot the bias and hallucinations that AI can't catch itself.

Direct AI to produce analysis you can stand behind. 

Separate correlation from cause and suggest the right next move.

 Workshop your real AI-generated reports live with the cohort.

Compound your career as AI reshapes the org.

We'll take a first-principles approach to data analysis. You'll learn the mindset of a data person -- and the outputs will follow naturally.

Curiosity about the process of data analysis, not just outputs.

If you've used ChatGPT, Claude, or any LLM for simple tasks like summarizing notes, you're ready.

Basic familiarity with AI tools.

We'll provide datasets for examples, but you'll get more value applying these concepts to your own work.

Access to some data.

Chiefs of Staff, BizOps leaders, and generalists responsible for guiding data-driven decisions without a dedicated analyst counterpart.

Operators who want to leverage AI for data analysis, but don't know how to trust the results.

Anyone who has ever said, "I'm not a data person." Data isn’t about code or math. Data is about judgment, and judgment can be learned.

Agentic AI

Coding with AI

AI Workflows

Claude Code

OpenClaw

Vibe Coding

AI Evals

AI Transformation

RAG & Search

AI for PMs

AI for Engineers

AI for Designers

AI for Marketers

AI for Founders

Product Sense

Product Discovery

User Research

Prototyping

Growth

Analytics

Tech Foundations

Strategy

Influence

Leadership

Career Growth

Machine Learning

LLM Ops

Context Eng

Security

System Design

Figma

Design Systems

Visual Design

Design Strategy

Automation

Content Marketing

Demand Gen

Go-to-Market

Product Marketing

Positioning

Social Media

Brand

B2B Marketing

SEO & AEO

AI for Leaders

AI Governance

Communication

Management

People Operations

Exec Presence

Storytelling

Goal-setting

Personal Brand

Fundraising

Everyone

Operators

Data Scientists

Business Analysts

User Researchers

Customer Success

Project Managers

HR Professionals

Sales People

Lawyers

Finance

Investors

Real Estate

Educators

Creators

Introduction to Making Sense of SQL

Speaker Introduction and Audience Roles

What is SQL? Understanding the Basics

The Core of SQL: Select, From, and Where

Decoding Your First SQL Query Step-by-Step

A Three-Step Framework for Reading Any Query

Analyzing a More Complex SQL Query

Upcoming SQL Course and Contact Information

Q&A Session

Product managers who drive strategy, development, and launch of products.

Engineers and engineering leaders who build, deploy, and scale software systems.

Broad professional audience — content relevant across many roles and functions.

Business analysts defining requirements and bridging business and technical teams.

Using data and metrics to measure performance, run experiments, and inform decisions.

Building technical literacy — understanding APIs, databases, system architecture, and engineering tradeoffs.

Making Sense of SQL (even if you're not an engineer)

What you'll learn

Why this topic matters

You'll learn from

Emma Boroson