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Career track · Data Analyst

Become a data analyst for the AI era: reports, dashboards and SQL, with AI doing the first pass.

AI now writes SQL and builds charts from a sentence. The analysts who matter are the ones who can check it, define the metrics it relies on and explain what the numbers mean.

Or get your personal AI playbook now

What changes

Your work, re-mapped.

How AI is changing this work today, task by task. Your assessment maps it onto your own week.

Tasks AI can already do for you.

  • Writing routine SQL from scratch
  • Building standard charts by hand
  • Manual data cleaning steps
  • Recurring report formatting

The curriculum

Ten modules.
One path through them.

Your assessment orders these for your level and goals and skips what you already know. Open any module to see what it covers.

01The analyst role, rewritten by AI5 topics

What AI automates in analytics, and the skills that become more valuable.

  1. Text-to-SQL and BI copilots
  2. What still needs an analyst
  3. Analytics career paths
  4. Building a portfolio
  5. How to learn with AI without depending on it
02SQL you can audit5 topics

Write and, more importantly, check SQL, including SQL an AI wrote.

  1. Joins, aggregations and window functions
  2. Reading AI-generated queries
  3. Common silent errors
  4. Query performance basics
  5. Testing queries against known answers
03Python for analysis5 topics

Clean, explore and analyse data with Python and an AI pair programmer.

  1. pandas and Polars essentials
  2. DuckDB for fast local analysis
  3. Notebooks with AI assistance
  4. Cleaning messy real-world data
  5. Reproducible analysis
04Statistics for real decisions5 topics

Know when a difference is real and when it's noise.

  1. Descriptive statistics
  2. Sampling and confidence
  3. A/B test analysis
  4. Correlation vs causation
  5. Explaining uncertainty simply
05Metrics and the semantic layer5 topics

Define metrics once so people and AI agents give the same answer.

  1. What a semantic layer is
  2. Metric definitions and governance
  3. dbt models and tests
  4. Why AI answers depend on definitions
  5. Documenting data for AI
06BI dashboards with AI copilots5 topics

Build dashboards people use, with AI handling the busywork.

  1. Dashboard design principles
  2. Power BI and Tableau AI features
  3. Natural-language questions over data
  4. Row-level security basics
  5. Maintaining dashboards
07AI-assisted analysis workflows5 topics

Use AI agents for exploration while you stay in control.

  1. Prompting for analysis
  2. Verifying AI-generated insights
  3. Analysing text: reviews and tickets
  4. Automating recurring analysis
  5. Provenance: showing your working
08Data storytelling5 topics

Turn findings into decisions.

  1. Structuring an analysis story
  2. Charts that persuade honestly
  3. Writing executive summaries
  4. Presenting to non-technical audiences
  5. Handling pushback
09Domain analytics5 topics

Apply analysis to the areas that hire most.

  1. Product and growth analytics
  2. Marketing and funnel analysis
  3. Finance and operations reporting
  4. Customer and churn analysis
  5. Picking your domain
10Portfolio and job search5 topics

Show employers you can do the modern analyst job.

  1. Three portfolio projects
  2. Case-study write-ups
  3. Analytics interview preparation
  4. Take-home assignments
  5. A 60-day job plan

Outcomes

By the end, you can.

  • 01Write and audit SQL, including queries an AI generated
  • 02Define metrics in a semantic layer that people and AI share
  • 03Build dashboards with AI copilots and natural-language querying
  • 04Present analysis that leads to a decision

Tools and techniques you'll use

  • SQL
  • Python (pandas, Polars, DuckDB)
  • Power BI Copilot
  • Tableau
  • Looker with Gemini
  • dbt semantic layer
  • Excel
  • ChatGPT, Claude and Gemini

Who it's forAspiring analysts, Excel power users, and working analysts who want to stay ahead.

The course is opening soon

Be first in.

Join the waitlist for the Data Analyst course. One email when it opens, nothing else.

Questions

Good to know.

Is data analysis still a good career with AI?

Yes, but the job is changing. Routine query-writing and chart-building are being automated, while checking results, defining metrics and explaining what the data means are worth more. The course is built around that shift.

Do I need to know programming first?

No. We start with SQL and introduce Python step by step, using AI as a tutor while making sure you understand the code yourself.

When does the course open?

The full course is being built now and opens to the waitlist first. Join with your email and we'll tell you as soon as it's ready.

How is the course personalised?

Before the course, a five-minute assessment, by text or by voice with Iris, our AI guide, learns your role, daily work, skills and goals. Your personal playbook then orders these modules for your level and goals and skips what you already know.

What can I do while I wait?

Take the assessment now. You get your personal AI playbook straight away, with the tasks in your work AI can take over, the ones it speeds up, and your learning path.