Topic
Data science
Data science here means the whole path from a question to a decision someone acts on, inside real organisations: a university deciding where students need support, a municipality seeing where service delivery breaks, a national network deciding which of 15,000 sites needs attention first, and an insurer understanding physical risk across its portfolio. The writing covers the other half of the job, which is building the capability around the models: the roadmap from analyst to someone trusted with production systems, and what a modern data scientist now needs beyond modelling.
2 work · 9 writing · 1 talks and sessions · Wikidata Q2374463
Work
- Decision support systems for municipalities at the CSIRDjango based predictive analytics and decision support systems serving 17 municipalities, including the City of Cape Town, built at the CSIR.
- Student success analytics and recommendations at WitsAnalytics and a clustering based recommendation system for student success at Wits, with over R2 million a year in subsidy impact attributed to it.
Writing
- Stage 1: Find Out What Is TrueThe first stage of building a data function is not technology. Write down what you have and create a rhythm. When we did, we found models that were simply wrong.
- Stage 3: Give the Work a Clear PathIt used to take about a year to get a model into use. Now it fits in a two week sprint. The change came from a shared workspace and one agreed path, not from cleverer people.
- Stage 4: When People Push Back, Show ThemThe technology was not the hardest part. People were. How we got through pushback and red tape, by showing value in action, and how a crisis became our best argument.
- Stage 5: Trust Your People, Then Let GoThe last step is the hardest: stop being the person everything depends on. How trust moved us from waiting for things to break to stopping problems early, with a 90 day plan.
- What the Road Makes PossibleWhy walk this road at all? Once the foundations are in place, one team can help a business know its customers, keep them, price fairly, understand big risks and work faster.
- Who You Need on the TeamWho belongs on a data team, who to hire first, what to do when the budget runs out, why graduates matter, and where the team should report.
- Where the Towers Are. A Map of What Africa Already HasPart one argued that Africa's cell towers could be a data centre spread across the land. This part asks the obvious next question: where are they? Using open, crowdsourced data on 565,000 mobile cells, it maps South Africa and the continent, finds that the towers follow the people and the roads, and shows why a map of people and a map of land tell two very different stories.
- How to Build a Data Science Function From Scratch: A RoadmapA five stage roadmap for building a data science, data engineering or AI function from scratch: what you will find, what to do first, what pushback to expect, and how to know when to move on.
- The Modern Data Scientist: A Roadmap for the Age of AI AgentsThe job hasn't disappeared. The mental model has changed. Here's what the roadmap looks like for data scientists and engineers entering (or navigating) the AI agent era, with a South African lens.
Talks and sessions
Related topics
Subjects that share work with this one. Those with their own page are linked.
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- Data engineering
- AI education
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- Python
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- MLOps
- Public sector
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