AI for Data Analysts
capability building,
designed for your organisation.
A custom-built corporate programme for data analysts, BI developers, analytics consultants, business analysts, marketing analysts, finance analysts, product analysts, and senior analysts moving toward analytics-engineering roles. We design the curriculum around your tech stack, project archetypes, and target business outcomes — delivered by domain-expert trainers and reinforced through AI-evaluated assessments.
A modular syllabus, built to be tailored.
Below is our reference curriculum. Every syllabus we deliver is tailored to your customer-specific requirements — module depth, sequencing, lab environments, and capstone projects are adapted to your team's starting point, tech stack, and target outcomes.
- AI tools for analysts: ChatGPT (Code Interpreter), Claude (long-context), Gemini (Workspace + Code)
- BI platform AI: Tableau Pulse AI, Power BI Copilot, Looker Studio AI, ThoughtSpot, Sigma AI
- Specialist analytics tools: Hex, Mode AI, dbt Copilot, Cube
- What's mature, what's emerging, what's hype
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Demonstrable skills your team will apply on live projects.
Apply AI across the analyst workflow
Exploration, analysis, dashboarding, storytelling, presentation — with role-specific patterns.
Use Code Interpreter for Python-grade analysis
Statistical analysis, ML, visualisation — without coding from scratch.
Generate validated SQL with NL2SQL
Schema-aware natural-language queries with automated validation discipline.
Pass GSDC AI for Data Analysts certification
Two attempts; cohort first-attempt pass rate 90%.
Compress analysis cycles by 40-60%
Documented time savings on exploration, dashboard creation, narrative production.
Move toward analytics-engineering roles
Equipped for analytics engineer transitions with AI-augmented productivity.
Where your team is now vs where they'll be after the programme.
Where most teams start
- ·Strong SQL and Excel skills but limited working AI fluency in the analyst workflow
- ·Defaults to manual exploration and analysis rather than AI-augmented patterns
- ·Cannot reliably use Code Interpreter / Advanced Data Analysis for non-coder Python analysis
- ·Limited fluency with NL2SQL — using natural language to generate validated SQL
- ·No working framework for AI-augmented dashboarding (Tableau Pulse AI, Power BI Copilot, Looker Studio AI)
- ·Limited discipline around AI verification — risks publishing AI-generated insights without challenging them
Where they'll arrive
- ✓AI-augmented analyst workflow — exploration, analysis, dashboarding, storytelling — across the full analyst day
- ✓Code Interpreter mastery for Python-grade analysis without writing Python from scratch
- ✓NL2SQL fluency — natural-language SQL generation with validation discipline
- ✓BI tool AI fluency — Tableau Pulse AI, Power BI Copilot, Looker Studio AI, ThoughtSpot
- ✓Verification discipline — catches AI-generated errors before they hit stakeholders
- ✓Storytelling at speed — from data to narrative in 1/3 the time
Built for L&D outcomes, not seat counts.
Prompt discipline, not prompt luck
Learners move from trial-and-error prompting to named patterns such as role prompting, few-shot, prompt chaining, and self-critique.
Reusable team assets
The programme produces Custom GPTs, reusable workflow templates, and a shared prompt library that teams can govern and scale.
Daily productivity workflows
Labs focus on email, reports, slides, meetings, spreadsheets, research synthesis, and role-based business assignments.
Measured time savings
Capstone workflows document recurring task compression, review-cycle reduction, and before/after productivity improvements.
Responsible enterprise use
Learners practise confidentiality, IP, bias detection, verification checklists, and safe-use protocols before adoption at scale.
Sustainment built in
30-day, 60-day, and 90-day check-ins help learners keep pace as ChatGPT features and frontier models evolve.
A four-milestone path from skill gap to client-ready.
Foundation & baseline
Establish a working mental model of ChatGPT, frontier models, tokens, context windows, hallucination risks, and model-selection trade-offs.
Prompt engineering labs
Learners practise CRISPE, SPEAR, role prompting, constraint-led prompting, few-shot prompting, self-critique, and prompt iteration on real work scenarios.
Custom GPTs & workflow automation
Each learner builds reusable GPTs and connects ChatGPT to productivity tools for email, documents, spreadsheets, meetings, and research workflows.
Capstone & sustainment
Learners demonstrate a personal AI productivity system and continue with prompt-of-the-week, model-of-the-month, and 30/60/90-day check-ins.
Want this curriculum aligned to your tech stack and project archetypes?
Why enterprise teams choose the B2B engagement model.
Domain-expert trainers, not professional presenters.
"My job isn't to teach ChatGPT as a tool — it's to help professionals build repeatable AI workflows, verify the output, and reclaim hours from routine work."
Taught by people who've actually shipped the work.
Built for L&D leaders and their learners.
Who this is for
- ·Knowledge workers who want to apply ChatGPT productively in their daily workflows
- ·Business analysts, consultants, marketing professionals, project managers, and individual contributors
- ·Teams that use ChatGPT for occasional drafting but need reliable, business-grade outputs
- ·Managers looking to establish team-wide prompt standards and safe-use protocols
- ·Organisations that want to automate repetitive work across email, spreadsheets, calendars, and documents
Pre-requisites
- ·No coding prerequisite for business and productivity tracks
- ·Basic familiarity with workplace tools such as email, documents, spreadsheets, slides, and meetings
- ·Willingness to bring real recurring tasks into labs for workflow redesign
- ·Enterprise cohorts should align data-handling expectations before learners use company or client information
Trusted by L&D leaders across the world.
"The programme moved our team from random prompting to a repeatable method. The prompt library and Custom GPTs became assets we could actually reuse."
"The most useful part was workflow automation. Learners took their weekly reports, meeting recaps, and research tasks and reduced hours of repetitive effort."
"Responsible use was handled practically. The team finally understood what can be pasted, what must be masked, and how to verify output before sending it."
Questions L&D teams ask before signing.
AI will automate repetitive reporting tasks, but skilled data analysts will remain highly valuable for business interpretation, strategic insights, decision-making, and AI-assisted analytics workflows.