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Complete learning course

Data Analyst + AI

Verified analytics with AI, SQL, and spreadsheets

AI-assisted EDA, SQL, structured outputs, verification, privacy, and dashboards.

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8–14 weeks3 modules8 lessons4 assessments

Module 1

Shared AI core

Model limits, evidence discipline, and verification for analytical work.

OutcomeThe analyst distinguishes model output from verified evidence.

  1. AI literacy and model limits

    Core

    Capabilities, hallucinations, context limits, privacy, and responsible use.

    Material →

  2. Prompt and context engineering

    Core

    Instructions, constraints, examples, and output verification.

    Prerequisites: AI literacy and model limits

    Material →

  3. Structured outputs and evaluation

    Core

    Response schemas, deterministic checks, and test cases.

    Prerequisites: Prompt and context engineering

    Material →Resource →

Module 2

AI-assisted analysis

EDA, SQL, spreadsheets, and structured findings with explicit verification.

OutcomeA reproducible analytical workflow with traceable assumptions and evidence.

  1. Structured analytical outputs

    Core

    Assumptions, metrics, findings, confidence, and machine-checkable result structure.

    Material →

  2. Verification and reconciliation

    Corepractice + assessment

    Recalculation, source checks, anomaly review, and reconciliation across outputs.

    Material →Practice →Assessment →

  3. Project: verified AI analytics workflow

    Projectpractice + assessment

    Dataset, SQL, findings, charts, and an evidence-backed analytical report.

    Material →Practice →Assessment →

Module 3

Privacy and communication

PII handling, reproducibility, lineage, and decision-ready communication.

OutcomeAn analysis package with data lineage, reviewability, and decision-ready outputs.

  1. Privacy and PII

    Corepractice + assessment

    Data minimization, masking, access policy, and safe use of sensitive analytical data.

    Material →Practice →Assessment →

  2. Milestone: decision-ready AI analysis

    Milestonepractice + assessment

    Verified numbers, charts, assumptions, sources, and a reviewable decision brief.

    Material →Practice →Assessment →

Final practice and assessment

The course ends with a practical artifact and an acceptance rubric. The result is considered complete after the acceptance criteria are met, not merely after reading the materials.

Verification and reconciliation →Project: verified AI analytics workflow →Privacy and PII →Milestone: decision-ready AI analysis →Open roadmap →