Catalogue 2027
Programs
Three programs, one rubric. Every graduate leaves with a defended capstone, a reviewed portfolio, and ten minutes of stage time in front of hiring partners.
Data Analytics Immersive
The flagship. From spreadsheets to stakeholder-ready dashboards in one quarter — SQL, Python, statistics, and the craft of telling a story a committee will actually act on.
Who it's for
Career-changers, analysts without formal training, and anyone told to "just look at the data."
What you can do at the end
- Query million-row tables in SQL without apology
- Build reproducible analysis pipelines in Python
- Design dashboards executives read to the end
- Ship a capstone reviewed by a hiring panel
Data Engineering Practicum
Pipelines that survive Monday. Ingestion, modelling, orchestration, and the unglamorous operational discipline that keeps a warehouse honest at 3 a.m.
Who it's for
Backend engineers, BI developers, and analysts ready to own the whole pipeline end to end.
What you can do at the end
- Model a warehouse with dbt and star-schema discipline
- Orchestrate daily pipelines with Airflow
- Stream events and handle late, dirty, duplicate data
- Run a platform on cloud infrastructure with cost alarms
Machine Learning Foundations
Statistics first, models second. A rigorous, math-honest introduction to applied ML for people who intend to deploy — not merely demo.
Who it's for
Working analysts, engineers, and quantitative professionals who need ML that survives production review.
What you can do at the end
- Frame a business problem as a validable prediction task
- Train, tune, and honestly evaluate gradient-boosted models
- Avoid the leakage and sampling traps that sink pilots
- Hand a model to engineering as a reviewed service
Not sure which one?
Come to an open lab night — Thursdays, 6 to 9 pm, third floor. Sit in on a working session, ask faculty anything, and leave with a recommendation you can argue with.
