An AI assistant for long documents, analysis and code, with Claude Code and Cowork on paid plans.
Freemium
Data scientist
ChangingAI writes analysis code, charts and rough conclusions on request, while the data scientist owns framing the question, data quality and the honesty of the conclusions. Demand for the profession is growing: companies need people who build AI into their work with data.
Where this job sits on the map
In the same group: 78 of 100 professions.
What changes
AI writes the Python and SQL for routine analysis.
Managers ask questions of the data in plain language, and the data scientist checks the answers and prepares the groundwork.
There's more work on data quality and on evaluating AI models.
AI drafts, the person decides
How the work splits here: AI prepares a draft, the person checks it and makes the call.
What AI does
- Writes code for cleaning data and analysis
- Builds charts and rough dashboards
- Explains a result in plain language for a report
- Suggests hypotheses and ways to test them
You check itRead it, check the facts, fix it. Without this check the draft goes nowhere.
What stays with the person
- Framing the question: what the business actually wants to know
- Checking that the data is complete and the conclusion isn't forced
- Responsibility for decisions made on the basis of the analysis
- Handling personal information in line with privacy rules
What to learn first
6 lessons from the course, in order. Start with the first one.
- How to write a good promptUserHow to give AI a task: the five parts of a good prompt, Plan Mode, and how to refine an answer instead of starting over.Start here
- AI ethics and safety: hallucinations, attacks, biasUserHallucinations, prompt injection, bias, privacy and copyright: how not to trust AI blindly.
- Product analytics with AI: PostHog, Mixpanel and smart insights without a data analystBuilderProduct analytics with AI: PostHog and HogQL, an automated weekly report, churn prediction and A/B tests.
- RAG: Retrieval Augmented GenerationBuilderRAG: how to teach Claude to answer from your own documents using embeddings and a vector database.
- Evals: skills that improve themselvesEngineerEvals for skills: tests, pass rate, checking that the skill triggers and a data-driven improvement loop.
- Fine-tuning: when prompts aren't enoughBuilderWhen a prompt is enough, when you need RAG and when you need fine-tuning: a decision tree, LoRA and where to train.
Which tools to use
Upload a spreadsheet or connect a database, ask in plain words, and get analysis and charts.
FreemiumOpen-source BI: dashboards and plain-language questions about your data through Metabot.
FreemiumGoogle's free dashboards, plus a Gemini-powered chat with your data in Looker Studio Pro.
Freemium
Ready-to-use materials
What each column means, where the data has errors, the main points in 5 bullets and questions for the author of the table.
FreePaste an export to get a data check, metrics with formulas, the 3 biggest changes and 3 actions.
Free- What never to send to AIChecklist
A 30-second check before you hit send: what to remove, how to anonymize and what to do if it's already gone out.
Free - How to check an AI answerChecklist
Assess the risk, spot the signs of a made-up answer and run a 5-minute check before you act on what AI says.
Free
How to earn with AI
We don't promise income: results depend on your niche, your market and your work.
- Job
Hand AI the draft code and charts, and spend your time framing questions and checking conclusions.
- Service
A dashboard and a regular review of the numbers for a small business: sales, customers, advertising, with plain-language explanations.
- Product
A ready-made analysis template for one industry, such as a sales breakdown for a coffee shop or an online store.
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Checked: October 2026