Highlights
- Explain in plain language how generative AI predicts text, and why this makes it fluent but not necessarily accurate
- Recognise the causes of hallucinations, bias and bland, “average” answers in AI outputs
- Test AI-generated summaries and analysis against the original data and sources
- Challenge AI confidence and spot claims that cannot be supported
- Write prompts that give AI the right context, constraints and evidence
- Use AI to explain data and findings clearly to non-technical audiences
- Apply data ethics, privacy and responsible-use principles to everyday AI work
- Build a repeatable checklist for verifying AI outputs before sharing them
Course Details
Module 1: What Is Happening Behind the Prompt
- How generative AI tools process your input and produce a response
- Why fluent answers are not the same as correct answers
- Where Copilot, Claude and similar tools fit into data-driven work
- Hands-on exercise: explore how small changes to a prompt change the answer
Module 2: Why AI Gets Things Wrong
- Hallucinations: what they are and why they happen
- Bias in training data and how it appears in outputs
- Why AI answers can drift towards the generic or the average
- Hands-on exercise: find and classify errors in a set of AI-generated summaries
Module 3: Testing and Validating AI Outputs
- Comparing AI summaries and analysis with the source data
- Asking AI to show its evidence, and checking whether it holds up
- Probing confidence, ambiguity and unsupported claims
- Hands-on exercise: build a validation checklist and apply it to an AI-produced analysis in Excel
Module 4: Communicating Data and Decisions
- Using AI to draft clear, accessible explanations of data
- Tailoring tone and detail for different audiences
- Keeping accuracy intact when simplifying
Hands-on exercise: produce and verify a short briefing from a dataset
Module 5: Responsible and Ethical Use
- Privacy, confidentiality and what not to paste into an AI tool
- Transparency, accountability and human oversight
- Setting team guidelines for AI use
- Wrap-up: personal action plan and team checklist
Who should attend
This course suits anyone who produces, reads or communicates data at work, including:
Data Analysts who use AI to speed up analysis and reporting
Researchers and Research Officers who need to trust what they cite
Business Intelligence Specialists building AI into reporting workflows
Managers and Decision-Makers who rely on AI-assisted analysis
Data and Analytics Teams setting standards for AI use
It is also useful if your role doesn’t involve AI directly, because AI-influenced data and summaries are increasingly arriving on your desk.
Prerequisites
No knowledge of statistics or machine learning is assumed. You should be comfortable with basic Excel: working with cells, simple formulas and charts.
What You Need
A laptop or computer with Microsoft Excel installed, and access to an AI assistant such as Microsoft Copilot or Claude for the exercises. Please check with us if you are unsure about access or licences before booking.
Feedback
4.8 out of 5 average
"Our tailored course provided a well rounded introduction. It covered topics that we needed to know. The instructor genuinely cared about our learning. We felt supported from start to finish and left with knowledge that truly mattered to our work." Brian Leek, Data Analyst, May 2024
“JBI did a great job of customizing their syllabus to suit our business needs and also bringing our team up to speed on the current best practices. ” Brian F, Team Lead, RBS, Data Analysis Course, 20 April 2022