Artificial Intelligence & Generative Tools for Analysts is a structured track of 12 lessons that build a complete, interview-ready understanding of the topic. Work through them in order, then use the quiz and flashcards in each lesson to revise.
What this course covers
- Where AI Genuinely Helps an Analyst, and Where It Must Not - An honest split between the tasks AI accelerates and the ones it must never own.
- How Large Language Models Work, at the Level You Must Explain - Tokens, context and prediction - enough to answer the interview question well.
- Embeddings, Vector Search & Retrieval-Augmented Generation - How semantic search works and why retrieval grounds a model in your own data.
- Prompting for Analysis: Patterns That Produce Reliable Output - Prompt patterns for analysis, summarising and code, with the failure modes to expect.
- AI-Assisted SQL & Python: Getting Code You Can Trust - Prompting for query code, plus the review checklist before anything is run on real data.
- Generative Reporting & Natural-Language Querying - Asking questions of data in plain English, and what it means for dashboards.
- Why Conversational Analytics Needs a Semantic Layer - Why a governed metric definition is the difference between a useful answer and a wrong one.
- Agentic Analytics: Agents That Query, Analyse and Report - What agents do well, where they fail, and which parts the analyst still owns.
- Validating AI Output: The Analyst's Quality Checklist - A systematic check on AI-produced numbers, logic and code before you stake your name on it.
- Using NotebookLM to Research a Company Before an Interview - Turning filings, reports and this compendium into a personal question bank.
- Will AI Replace Analysts? How the Role Is Actually Changing - Which tasks are being automated, which are becoming more valuable, and how to answer this.
- Case Study: The Same Analysis Done With and Without AI - One task done both ways, with the time saved and the errors introduced measured.