For students
Pick a brief below, or bring the problem statement your department handed you. We architect it with you, review every commit, deploy it to a real URL, and rehearse the viva until no question surprises you.
See student support5 scoped briefs
3 mini · 2 major
This is the consumption end of the data stack: SQL that answers a question, a model that makes the answer repeatable, and a chart only at the very end. Data engineering owns whether the data arrives — on time, uncorrupted; this domain owns what it means once it is here: the grain, the metric definition, the segment cut, the confidence interval. Everyone has a dashboard, so a project only counts if it is defensible — student work fails here by being decorative: a Power BI page built straight on a flat CSV, with no fact grain, no baseline to beat, and a conclusion the data does not actually support.
Two audiences, one engineering standard
Pick a brief below, or bring the problem statement your department handed you. We architect it with you, review every commit, deploy it to a real URL, and rehearse the viva until no question surprises you.
See student supportSemantic and dimensional modelling in dbt, metric definitions that stop two dashboards disagreeing about revenue, BI builds and migrations in Power BI or Tableau, and classical forecasting, experiment design and readouts — the layer above ingestion, where a number has to resolve to one meaning.
Discuss a company buildMini projects
Three to four weeks. Narrow enough to finish, deep enough that you learn the decision that actually matters.
Raw order CSVs modelled into a star schema in Postgres with a Power BI report on top — where the hard part is fixing the fact grain so measures stop double-counting across joins.
You walk away with
A full experiment analysis — pre-registered plan, power simulation, peeking cost, effect size with an interval — ending in a written defence of what the result does and, more importantly, does not prove.
You walk away with
A monthly SKU demand forecast benchmarked against the seasonal-naive baseline it has to beat, published as a dashboard that tracks forecast error rather than just the forecast.
You walk away with
Major projects
Ten to twelve weeks. Architecture, trade-offs, failure modes, deployment, and evidence—the project that carries an interview.
Raw sources modelled through dbt into governed dimensional marts with a metric catalogue and a BI layer on top — where the hard part is the non-additive metrics that break the moment someone sums them.
You walk away with
A behavioural investigation over a large public event dataset — where the funnel leaks, which cohorts come back, what a past change actually did — ending in a recommendation list ranked by estimated impact and effort.
You walk away with
Explore more
Bring us a product brief, a business problem, or a final-year project. We’ll turn it into a clear scope, a working build, and a handover you fully own.
Prefer email? info@tenzok.in