Advanced course
Language Data Engineering
The quiet work that decides how good a language model gets.
Source, clean, annotate, and evaluate language data for low-resource and dialect-rich settings.
Enroll and begin ↗The course
“The quiet work that decides how good a language model gets.”
Researchers, data leads, and engineers working at the language layer. Every module is organized around applied evidence rather than passive completion.
Curriculum / 04 modules
From first question
to working outcome.
01Sourcing and consent+
Where language data comes from, what it costs, and what consent requires.
- Source mapping
- Consent models
- Collection risk
02Annotation at scale+
Guidelines, agreement, calibration, and workflows that hold up.
- Guideline design
- Agreement and calibration
- Workflow quality
03Evaluation for dialects+
Build suites reflecting real language use where standard benchmarks fail.
- Benchmark gaps
- Coverage design
- Evaluation in context
04Bias, coverage, and audit+
Find quiet dataset failures before they become model behavior.
- Bias pathways
- Coverage audits
- Remediation decisions
What changes
You leave able to own the whole engagement.
- 01Design annotation pipelines
- 02Build dialect-sensitive evaluation suites
- 03Reason about bias, coverage, and consent
Who it is for
Strong builders moving closer to the problem.
Researchers, data leads, and engineers working at the language layer.
Before you begin
- Working familiarity with NLP or ML
- Comfortable with Python and data tooling
Enrollment open
Start with the first lesson.
Enroll to save progress, resume across devices, and keep your course work connected to your learner profile.
Enroll and begin ↗