Technology Training · Cohort
AI & Data Literacy
Use these tools well, and know where they fail
- Real answers out of spreadsheets and SQL
- Charts and statistics read critically
- How language models work — and how they fail
- Privacy, disclosure and academic-honesty rules that apply to you
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Talk to an advisor about AI & Data Literacy.
Free consultation, no obligation.
What this program is
AI tools arrived in workplaces and classrooms faster than anyone's judgement about them. The result is a split between people who refuse to touch the tools and people who paste anything into them and forward the output unchecked. Both are failure modes, and both come from not understanding what these systems actually do.
This course builds the underlying literacy. Half of it is data: getting real answers out of a spreadsheet, writing SQL against a real database, and reading charts and statistics with enough scepticism to notice a misleading axis or a survivorship-biased sample. The other half is AI: how a language model generates text, why it produces confident and wrong answers, how to prompt and — more importantly — how to verify, and what should never be pasted into a third-party tool.
It is tool-agnostic and vendor-neutral. Techniques are demonstrated across mainstream assistants, and nobody is sold a subscription.
The six modules
Data you already have
Spreadsheets past the basics — cleaning, lookups, pivots and summarising honestly — plus what makes a dataset trustworthy and the questions to ask before believing a number.
Asking a database a question
SQL from scratch: selecting, filtering, joining and aggregating, practised against a real dataset. Enough to answer your own questions instead of queueing for someone who can.
Statistics for reading, not for exams
Averages and distributions, correlation versus causation, sampling and selection bias, base rates, and the standard ways a chart misleads. Worked on real published charts, including bad ones.
How language models actually work
Tokens, prediction, context windows, training data and why a model produces fluent false statements without any awareness of doing so. Enough mechanism to predict where a tool will fail.
Working with AI tools well
Prompting for real tasks, giving context, iterating, and structured verification — the discipline of checking output against a source before it goes anywhere. Covers where these tools genuinely help and where they cost more time than they save.
Privacy, ethics and the rules that bind you
What must never be pasted into a third-party tool, confidentiality and client data, bias and its consequences in hiring and credit contexts, disclosure norms, and academic-honesty rules for students.
Format and logistics
- Live online, small cohort
- Weekly 90-minute sessions
- 6 modules
- Vendor-neutral; a spreadsheet and a free database are enough
- None beyond everyday computer use
- Available as a private cohort for organisations
What you leave with
- The ability to answer your own data questions with a spreadsheet or SQL
- A verification routine you apply to AI output before you rely on it
- A written personal or team policy for what goes into third-party tools
- Enough mechanism to predict where these tools will be wrong
Pricing
Pricing for this program depends on format, cadence and length, so we quote it on the consultation call rather than publishing a number that would be wrong for most families. Ask an advisor — the call is free and there is no obligation.
Common questions
Please read before enrolling
Training and mentoring only. We are not an accredited institution and do not issue degrees; certification is awarded by the vendor, not by KPM Academy, and employment outcomes are not guaranteed.
What Employers Actually Check
Six sections, one sitting
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