Data Infrastructure and Architecture
Design and maintain the infrastructure that makes analysis possible: the modern data stack and its pipelines.
Data & Decision Science
PublishingThis department publishes coverage today — every story links out to its own source.
What this department watches
This department watches the machinery underneath decisions: the data stack and the pipelines that feed it, the dashboards people actually steer on, and the statistical practice that separates a defensible result from a confident one. Where a claim rests on a number, this department is interested in how that number was produced.
- The data stack, pipelines, and where they break
- Dashboards and metric definitions people actually use
- Statistical and experimental practice, including its misuse
- Data quality, lineage, and reconciliation
- AI-assisted analysis, and where it quietly guesses
- The business metrics companies steer and report on
The questions it keeps open
These are the open questions this department works. They are questions, not conclusions — what comes back gets published with its sources attached.
- When a copilot writes the query, where does it break — and which of those failures are silent rather than loud?
- What is the current honest state of plain-language-to-query on a real, imperfect warehouse schema, as opposed to a clean demo one?
- Which parts of data cleaning and quality management can be handed to a model, and which still require someone who knows the business?
- How are teams defining and governing metrics now that anyone can generate a chart in seconds — who owns the definition?
- What is AI actually changing about experimental design and how results get read, versus what only sounds like it changed?
- What does a data role look like when the query-writing is automated — what are teams hiring for instead?
How the research runs
1 · Read the real sources
AIU's research agents work from a published register of trusted sources rather than from memory — the register itself is open, so you can see what is being read and how often it is checked.
2 · Carry the method and the source
A finding is only useful if you can check it. Every item carries the source it came from and the date that source published, and links straight out to the original — so a claim can be verified rather than taken on trust.
3 · Feed the coursework
What this department reads is what keeps the coursework current — and the coursework decides what is worth watching next. The two are meant to inform each other, which is why the curriculum below is on this page rather than somewhere else.
The curriculum behind it
The major
Turn data into decisions with AI — the practical alternative to a statistics degree, built around real dashboards and real pipelines.
Business Intelligence and Dashboard Design
Build production dashboards stakeholders genuinely use, on metric definitions that hold up.
Statistical Analysis and Experimental Design
Turn data into defensible decisions: experiment design, hypothesis testing, intervals, and regression.
Follow this department
This department is publishing now — read the edition, or put your name down to be told when its research goes further.
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