Go from spreadsheets to production-grade pipelines. Statistics, SQL, Python, and the kind of model-building that actually ships — not the kind that lives in a Jupyter notebook forever.
Every module pairs theory with hands-on application. By the end, you'll have shipped real work in each one.
Distributions, hypothesis testing, A/B tests. The math you'll actually use, not the math textbooks teach.
Window functions, query optimization, real-world database design. Practice on production-grade datasets.
Pandas, NumPy, scikit-learn — and the data wrangling reality between them.
Matplotlib, Plotly, Tableau. Build dashboards stakeholders actually use.
Regression, classification, clustering. When to use what, and how to know if it's working.
Embed with a data team — running alongside the curriculum. Build a real model. Get it into production.
Design A/B tests, calculate sample size, interpret results without lying with statistics.
End-to-end: from raw data to deployed model. Including the boring middle bits.
Build dashboards and reports that drive decisions, not ones that get ignored.
Portfolio of shipped work, real references, and an interview prep that focuses on the actual job.
This track opens later this year. Drop your details and we'll let you know the moment intake opens.