Coming Soon workshop
AI Project Lab: From Messy Data to Exec-Ready Reports
AI
Data Analysis
Data Prep
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Most AI-assisted analysis skips a critical step: making sure the data is actually trustworthy before drawing conclusions. It's easy to get a polished, confident-sounding brief back from an AI tool and never realize it was built on missing values, duplicate records, or inconsistent categories. Great analysis built on bad data is still bad analysis.
In this session, you'll work through a real messy employee engagement survey from start to finish — using AI to define the business problem, profile and clean the data, validate it, analyze it, and write up the findings in a brief a stakeholder could act on. You'll leave with a five-step prompting framework you can apply to any dataset you get handed at work.
What you'll learn
Define the business question before touching the data
Knowing what you're actually trying to answer shapes every step that follows, and AI can help you get there faster than starting with the data itself.
Profile a dataset for common data quality issues using AI
Missing values, inconsistent categories, and duplicate records are easy to miss and expensive to ignore — this step catches them before they corrupt your analysis.
Write structured prompts that produce consistent results on messy data
Using constraints, output formats, and examples of what "good" looks like, you'll build prompts that work reliably across different datasets, not just this one.
Turn AI-assisted analysis into a brief a stakeholder can act on
A clear, well-structured brief is the deliverable that matters — you'll practice producing one that connects findings directly to the business question you started with.
Who should join this workshop?
Analysts who work with real-world data and want a structured way to use AI without sacrificing data quality
Professionals in HR, operations, or people analytics who regularly handle survey or feedback data
Anyone who uses AI for data work but gets inconsistent results and wants a repeatable process they can trust
Prerequisites
No prior experience with AI tools or data analysis required
Access to any AI chat tool, free or paid (Claude, ChatGPT, Copilot, Gemini, etc.)
Basic familiarity with spreadsheets (Excel or Google Sheets) is helpful but not required
This is a live, hands-on session and active participation is expected
Mo Chen
Data & AI Educator, Data with Mo
Mo Chen is a data and analytics professional with experience across finance, risk, investment banking, and business insights. After earning his MSc in Finance & Economics from the University of St Andrews in 2018, he moved into investment banking as a Data & Analytics Manager. Since 2023, Mo has grown a major online presence creating data and career content, reaching 200K+ YouTube subscribers, 8M+ views, and 50K+ LinkedIn followers.
