Data Science Academy
Our training arm. Cohort-based programmes in data analytics, business intelligence, machine learning and generative AI for working professionals, corporate teams and graduates.
Six tracks, one teaching method.
Data Analytics & Power BI
Data preparation, star-schema modelling, DAX, visual design, row-level security and publishing. Foundation and advanced tracks.
Machine Learning with Python
Python for analysis, the full ML lifecycle, model evaluation, and moving a model into production and keeping it there.
Generative AI & LLMs
Prompting, retrieval over your own documents, fine-tuning fundamentals, evaluation, cost and risk, and the governance to adopt it safely.
SQL & Data Engineering
Querying, transformation, pipelines and warehouse fundamentals, the layer most analytics teams are missing.
Data for Monitoring & Evaluation
Indicators, collection design, analysis and results-based reporting for programme and M&E staff.
Leading with Data
A short executive track: reading analytics critically, questioning the numbers your teams present, and judging where AI is worth funding.
Nobody learns to swim from a manual.
Every module is anchored to a problem someone is actually paid to solve. Participants work with real, messy datasets from day one, get one-to-one mentorship from engineers who ship this work for clients, and are reviewed weekly rather than tested once at the end.
Five commitments we make to every cohort
- Real problems. Briefs come from live client work and partner organisations, not textbook exercises.
- Real data. Messy, incomplete and occasionally contradictory, because that is what waits at work.
- One-to-one mentorship. Each participant is paired with a practising engineer or analyst for the whole cohort.
- Weekly review. Work is shown and critiqued every week, so nobody discovers they are lost in week nine.
- A capstone that counts. Presented to a panel including practitioners from industry.
Something you can show an employer.
Each participant, or small team, takes one problem from question to working artefact: a deployed dashboard, a trained and evaluated model, a data pipeline, or an LLM assistant grounded in a real document set.
They defend it on
- The decision it improves, and for whom
- Whether the data actually supports the claim
- Technical choices, and what was rejected
- How it would run and be maintained in production
Closed cohorts for employers
We run private cohorts for a single organisation, where every capstone tackles a problem from your own backlog. You finish the programme with trained staff and a set of working prototypes that stay with you.
Who joins our cohorts
Formats
The problem it solves
Adverts ask for SQL and Power BI; applicants arrive with certificates and no portfolio. Analysts who can build a chart cannot build a data model. Staff are sent on a five-day course and nothing changes when they get back to their desks.
Tools and platforms
Python, pandas and scikit-learn, SQL, Power BI and DAX, Git, Jupyter, Docker and open-source LLM tooling. Participants work in the same stack our engineers use on client projects, not a simplified teaching environment.
What makes us different
Every cohort works on real problems with real, messy data, is mentored one to one by practising engineers, is reviewed weekly rather than tested once, and finishes with a capstone project defended in front of a panel.
Tell us what you are trying to get right.
Most of our work begins with a short scoping call about a decision someone is tired of guessing at. There is no charge for it, and no obligation at the end.