CC03 · Claude Code for Data & Research
Turn data questions into answers without writing a single script from scratch.
For data workers and researchers with coding skills who want to dramatically accelerate their analysis workflow. Claude Code sits between data and questions and does the heavy lifting — cleaning, transforming, analysing, visualising. You steer; it builds.
Next sessionDates TBD — register interest
Register interest
“It's a tool in the hands of an expert versus a learner. AI tends to over-generalise and come to assumptions that aren't in the paper. An experienced researcher wouldn't make that mistake, but students might use five or six summaries and join bad ideas together.”
“The value is in checking. Getting it to generate things is one part, but the real value is bouncing ideas off it and getting detailed feedback — not just where you've gone wrong, but how you can improve. That's where students could get a lot of value.”
Who it's for
For data workers and researchers with coding skills who want to dramatically accelerate their analysis workflow. Claude Code sits between data and questions and does the heavy lifting — cleaning, transforming, analysing, visualising. You steer; it builds.
- You work with data weekly — CSV, JSON, databases, research datasets
- You can code (Python or R most likely) but you're tired of writing the boilerplate
- You want reproducible workflows, not one-off scripts
- You're a researcher who wants AI-augmented analysis without losing rigour
What you'll leave with
✓A working data pipeline or analysis workflow built during the session
✓The ability to interrogate datasets conversationally using Claude Code
✓A reproducible research workflow you can apply to your own data
✓Understanding of when AI analysis helps — and when it misleads
What we cover
1. Data sources
Working with CSV, JSON and databases via Claude Code — pick your poison.
2. Cleaning + transformation
Conversational data prep. No more boilerplate scripts.
3. Analysis + visualisation
Statistical analysis and charts driven by what you ask, not what you remember the syntax for.
4. Literature + synthesis
Review and synthesis workflows for research-heavy work.
5. Reproducibility
CLAUDE.md project context so your analysis is repeatable, not magic.
6. Limits + ethics
When AI analysis helps and when it misleads — the honest read.
Format & prerequisites
Duration
3.5 hours
Format
Live online (Microsoft Teams)
Prerequisite
CC01 Claude Code Foundations or equivalent comfort with Claude Code
Bring
Laptop with Claude Code installed + a sample dataset of your own (or use ours).
Facilitator
Dr Dennis Wollersheim
CTO · Co-founder · Facilitator · LinkedIn
PhD in Computer Science, Bachelor of Social Work — decades of experience turning messy data into meaningful insight, first with traditional tools, now with AI as a force multiplier. Dennis built the AI systems that run Real Minds AI: MCP servers, vault management, multi-agent workflows, automated state machines, a transcript/RAG pipeline, and an AI log collector. He doesn't just teach Claude Code — he lives in it.
“I found the present and presenter both interesting and engaging. I have learnt that there is more to AI than I first thought and how it could benefit my work life.”