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  • Start any day: Day 1 opens when you enrol
  • 1–2 hrs a day, in your own time
  • Reviewed work and interview prep
  • Individual feedback every week
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Career track 05This job is changing

Reporting Analyst, becoming Analytics Automation Lead

Excel or spreadsheet analysis, SQL basics, dashboards, data interpretation and business reporting.

Choose this trackSee the 30 days
AI skillsRAGAI agentsLLMOpsEvalsAI safetyGuardrails
An analytics dashboard of charts on a laptop
days
30days
checkpoints
4checkpoints
interview-prep days
2interview-prep days

What's changing in this role

Recurring reports and simple data requests are increasingly answered by natural-language tools. Value moves to defining metrics, data quality and explaining what numbers mean.

What you carry over

  • + Knowing where the data is wrong
  • + Business context behind each metric
  • + Stakeholder trust

Who it is for

Built for people like you.

  • MIS and reporting executives
  • Graduates targeting analyst roles
  • Operations and finance staff who work with data

Skills covered

What you will practise.

  • Spreadsheet analysis
  • SQL basics
  • Dashboards
  • Data cleaning
  • Data interpretation
  • Business reporting
  • AI-assisted analytics
A team gathered around a laptop, talking through a piece of work

30-day overview

Every day, planned.

Four weekly phases of daily problems, an assessment after each week, and interview prep on Days 29–30. Open a week to see every day.

Enroll now
Week 1Days 1–7FoundationsSpreadsheet and data foundationsCore analysis skills, and what self-serve AI tools now handle.
  1. Day 1Cleaning a messy dataset: Fix duplicates, blanks and inconsistent formats in sales data.
  2. Day 2Lookups and pivots: Answer five business questions with lookups and pivot tables.
  3. Day 3Choosing the right chart: Visualise the same data three ways and pick the clearest.
  4. Day 4Metric definitions: Define 'active customer' so two teams agree.
  5. Day 5Asking AI about data: Use an AI tool to analyse a sheet and verify its answers.
  6. Day 6What self-serve tools answer: Audit a report pack for what AI can already produce.
  7. Day 7Checkpoint: Report pack auditAudit a reporting pack for what a self-serve tool answers, and what still needs an analyst.Practical task + short written reasoning, scored against a rubric
Week 2Days 8–14Practical executionSQL and metric ownershipQuerying data yourself and owning definitions so answers stay consistent.
  1. Day 8SELECT, WHERE, GROUP BY: Query an orders table to answer weekly sales questions.
  2. Day 9JOINs: Combine customers and orders without double counting.
  3. Day 10Data quality checks: Write queries that catch missing and invalid records.
  4. Day 11Contested metric: Resolve two conflicting revenue numbers.
  5. Day 12Semantic layer basics: Document metrics so AI tools answer consistently.
  6. Day 13Writing the insight: Turn a table of numbers into three clear sentences.
  7. Day 14Checkpoint: Metric defenceDefine a contested metric and defend the definition with SQL evidence.Practical task + short written reasoning, scored against a rubric
Week 3Days 15–21Real-world problem solvingDashboards and automationBuilding dashboards and replacing manual reporting cycles.
  1. Day 15KPI dashboard: Build a one-page dashboard for a sales head.
  2. Day 16Automating a monthly report: Replace a manual copy-paste process with a repeatable one.
  3. Day 17Anomaly investigation: Explain a sudden 30% drop in sign-ups.
  4. Day 18Generated narratives: Draft commentary with AI and correct what it gets wrong.
  5. Day 19Stakeholder request triage: Handle five conflicting data requests.
  6. Day 20Presenting findings: Present an analysis to a non-technical audience.
  7. Day 21Checkpoint: Automated cycleReplace one manual reporting cycle with an automated pipeline.Practical task + short written reasoning, scored against a rubric
Week 4Days 22–28Portfolio and interview readinessGovernance and portfolioData quality, trust and packaging your analysis work.
  1. Day 22Data lineage: Document where each dashboard number comes from.
  2. Day 23Guardrails for AI answers: Decide which questions AI tools may answer.
  3. Day 24Business case: Use data to recommend a decision.
  4. Day 25Portfolio: dashboard: Finalise and document your dashboard project.
  5. Day 26Portfolio: walkthrough: Present your analysis in 5 minutes.
  6. Day 27Interview drill: case study: Solve a data case study under time pressure.
  7. Day 28Checkpoint: Governed systemPresent a governed, automated reporting system.Practical task + short written reasoning, scored against a rubric

Weekly checkpoints

Four checkpoints. Real feedback.

Each one is marked on your reasoning, not just the output: and you defend it.

  1. 7After Day 7Report pack auditAudit a reporting pack for what a self-serve tool answers, and what still needs an analyst.
  2. 14After Day 14Metric defenceDefine a contested metric and defend the definition with SQL evidence.
  3. 21After Day 21Automated cycleReplace one manual reporting cycle with an automated pipeline.
  4. 28After Day 28Governed systemPresent a governed, automated reporting system.
A woman presenting her work to colleagues in a meeting room

Expected outcomes

What you will be able to do.

  • Clean and analyse data in spreadsheets
  • Write SQL queries to answer business questions
  • Build clear dashboards
  • Define and defend metrics
  • Communicate insights to stakeholders

Role-specific projects

Evidence you can show.

  1. 01KPI dashboard built on cleaned data with SQL queries
  2. 02Automated monthly report with metric definitions and data-quality checks

Interview preparation

Practise the questions Reporting Analyst interviews actually ask.

Preparation runs through Week 4, then the most-asked questions on Days 29–30. Here is what you will be asked, and what a strong answer looks like.

  • Days 29–30Interview prep after Week 4
  • Three kinds of questionTechnical, scenario, behavioural
  • Model answersWhat a strong answer shows
  1. 01Technical

    “What is the difference between an INNER JOIN and a LEFT JOIN?”

    A strong answer shows

    Correct explanation with an example of when each matters.

  2. 02Technical

    “How would you find duplicate records in a table?”

    A strong answer shows

    GROUP BY with HAVING, or window functions, plus how to decide which to keep.

  3. 03Scenario

    “Sales dropped 20% last month. How do you investigate?”

    A strong answer shows

    Checks data quality first, then segments by product, region and channel.

  4. 04Scenario

    “Two teams report different revenue numbers. What do you do?”

    A strong answer shows

    Traces definitions and sources, then agrees one documented definition.

  5. 05Behavioural

    “Tell me about an insight that changed a decision.”

    A strong answer shows

    Clear context, analysis and business impact.

  6. Topics covered

    • Excel/Sheets functions
    • SQL
    • Dashboards and visualisation
    • Data cleaning
    • Business case studies
    • Communicating insights
    Enroll now