Using data to help students and a university decide
A university holds a great deal of data about how students are doing. The work was turning it into something faculties and support staff could act on, early enough to matter.
The problem
Business Intelligence Services was the university’s central analytics and reporting capability. It held a lot of institutional data, but the questions that mattered, which students may need support, which interventions help, what academic choices make sense, were hard to answer from reporting alone.
Why it mattered
South African university funding is tied to student progression and completion, so better decisions have both a human and an institutional consequence. Earlier visibility means support can be proactive rather than a response to a failure that has already happened.
The context
A central analytics function serving faculties, schools, planning and student-support staff, many of whom came from traditional institutional-research backgrounds rather than machine learning. Anything built had to be usable and maintainable by them.
What I did
I developed recommendation and analytics systems supporting institutional planning and student success, and built the reporting and analytics workflows around them in Python, SQL and Power BI. I built a clustering-based recommendation engine serving the Faculty of Humanities. I also facilitated 7 analytics and machine learning workshops, training 76+ technical and non-technical staff and students, so the capability did not sit with one central team.
What changed
Faculties, schools and planning gained useful information rather than raw institutional data, supporting earlier and better-targeted academic and support decisions. The recommendation engine is associated with over R2 million a year in government subsidy impact.
Who benefited
Students, through better academic decisions and earlier support; faculty and planning staff, through visibility they could act on; and the 76+ people trained, who kept the skills.
What remained
Analytics and recommendation systems in institutional use, and trained people. This is where a pattern starts that runs through everything since: transfer the capability, do not just deliver the artefact.
Technical context
Python, SQL, clustering and recommendation methods, Power BI reporting and analytics workflows.
Related
Decision support for municipalities
Municipalities held data relevant to performance and service delivery, but not in a form that supported planning or operational decisions.
WorkTurning environmental data into something people can use
Climate and environmental questions need satellite and geospatial data at petabyte scale, too large to treat as an ordinary dataset, and almost no organisation can process it alone.
WritingHow I Learned to Build My Own Python Libraries (From Curiosity to Real Work)
creating from 1st principles
WorkTfilterspy
Real sensors lie. GPS, radar, vehicle trackers and IoT devices all produce noisy readings, and you need an honest estimate of what is actually happening underneath the noise.