The foundation of every AI-first organisation. Master the technical skills, then apply them inside a realistic banking simulation where the data is real, the stakeholders push back, and nothing advances until the work is good enough.
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Who it's for
No SQL, no Python, no technical background. You want a rigorous path into a data career without spending £10K on a bootcamp that can't guarantee you'll get a job.
You work in finance, consulting, operations, or marketing. You can read a spreadsheet. You want to go further — real analysis, real data, the confidence to sit in a room with a data team and hold your own.
You've done the courses. You can write queries. But you've never been in the room when a senior stakeholder pushes back on your analysis. That's exactly what this track gives you.
Part one
32 learning nodes across 5 modules. You start with a diagnostic that maps what you already know — so you skip what you've mastered and focus on what you haven't. AI generates every explanation, every practice problem, every worked example — personalised to your background, your goals, and where you're getting stuck.
Every concept is followed by live practice. Real SQL running against a real database. Real Python in the browser. You don't watch someone else do it. You do it.
Data types, spreadsheet fundamentals, data quality, business metrics, descriptive statistics, commercial problem framing
SQL foundations, aggregation, subqueries & CTEs, analytical thinking, data viz principles, Power BI, exploratory data analysis
Python fundamentals, Pandas, NumPy, data cleaning, advanced analysis, statistical modelling
Advanced SQL analytics, predictive modelling, portfolio project
Part two
A fictional mid-tier UK retail bank. 3.2 million customers. 85 branches. And cracks forming beneath the surface that nobody has found yet — until you start looking.
You join the data analytics team reporting to Tom Brennan. First task: build a commercial dashboard for James Okafor (Head of Commercial Banking). You learn who the stakeholders are, how they work, what they actually want versus what they ask for.
The CEO sends a company-wide email about customer churn. Your brief escalates. Cross-functional stakeholders start wanting answers. You're the one who has to find them — in 49 million rows of data, with no clear instruction on where to look.
A fintech competitor is acquiring Meridian's customers. There's a suspicious cluster of transactions in Birmingham that the bank's own fraud detection hasn't caught. The CEO is now accessible directly. The pressure is different.
The lending decision model shows statistically significant disparity in approval rates by postcode, correlating with demographic patterns. You've found it. Now what do you do? Who do you tell? How do you write it? There's no clean answer — which is exactly the point.
The people you'll work with
Your day-to-day manager. Practical, slightly overworked, supportive but expects competence. Sends your daily briefs and shields you from unreasonable stakeholder requests. Will catch issues in your work before they go upward.
Enthusiastic, ambitious, calls people "mate." Wants growth metrics and commercial dashboards. Changes his mind constantly. Expands scope the moment you deliver. Exactly like a real Head of Commercial.
Formal, precise, intimidating. References PRA regulations. Signs off with "Regards, Rachel." Asks clarifying questions before giving you anything. Will challenge your methodology, your sample size, and your assumptions.
Strategic, concise, expects high quality. Becomes directly accessible in Phase 3. She sends rare messages — each one matters. Hates being given options without a recommendation. "What's your recommendation?" is her default response.
+ Dev Kapoor (Marketing), Liz Pearce (Compliance), Aisha Begum & Ravi Sharma (your AI analyst colleagues)
On completion
Analyses, dashboards, and documents you built, submitted to real stakeholders, had pushed back, revised, and finally accepted. Not exercises. Yours.
SQL · Python · Statistics · Visualisation · Communication · Commercial Judgement — scored from your actual performance, not a final exam.
Your timeline, stakeholder quotes, skill progression, first query vs final query. Something to show an employer that certificates genuinely cannot match.
Ready to start
One-time payment. Lifetime access to the track. 30-day money-back guarantee.
Or try Day 1 free — no card required — before you commit.
No. The Knowledge Engine starts with a diagnostic, then builds from wherever you are. Many learners start with zero SQL or Python experience. The first module covers data fundamentals and works up gradually. If you already know SQL, the diagnostic will credit that and skip ahead.
It means you don't move to the next concept until you've genuinely understood the current one. The AI assesses your understanding through active practice — not a checkbox or a time limit. If you're still shaky on SQL aggregation, you don't move to subqueries. It takes longer sometimes. But you don't have the gaps that undermine you later.
Meridian Bank is fictional — but the data is real. 49 million rows across 10 tables in Google BigQuery, designed with authentic banking data patterns. The fraud cluster, the churn patterns, the lending model bias — they're real patterns in real data. You query them using actual SQL in a real query environment.
Yes — coaching sessions (£50/30 mins) can be added at checkout or purchased separately at any point during your track. Your coach sees your actual work before the session — your queries, your documents, your stakeholder messages. Sessions are with practitioners from KPMG, Deloitte, HSBC, and iO-Sphere's expert network.