Probability
1–2Probability trains the habits statistics depends on — not trivia for exams, but reusable technique for later studios, labs or clinics.
A statistics curriculum is a sequence, not a shopping list. Early courses build shared vocabulary; middle years introduce methods; the final stretch demands a project that can fail in public.
Elective freedom varies: some systems lock professional accreditation hours; others allow wide minors. The courses below are the common spine, not every university's catalogue.
Core courses
Inference
1–2Inference trains the habits statistics depends on — not trivia for exams, but reusable technique for later studios, labs or clinics.
Regression
1–2Regression trains the habits statistics depends on — not trivia for exams, but reusable technique for later studios, labs or clinics.
Experimental design
2–3Experimental design trains the habits statistics depends on — not trivia for exams, but reusable technique for later studios, labs or clinics.
Bayesian methods
2–3Bayesian methods trains the habits statistics depends on — not trivia for exams, but reusable technique for later studios, labs or clinics.
Computational stats
2–3Computational stats trains the habits statistics depends on — not trivia for exams, but reusable technique for later studios, labs or clinics.
Survey sampling
3–4Survey sampling trains the habits statistics depends on — not trivia for exams, but reusable technique for later studios, labs or clinics.
Time series
3–4Time series trains the habits statistics depends on — not trivia for exams, but reusable technique for later studios, labs or clinics.
Common tracks
Biostatistics
A biostatistics emphasis usually appears after foundations, when students choose seminars, labs or studios that deepen one problem family inside statistics.
Official statistics
A official statistics emphasis usually appears after foundations, when students choose seminars, labs or studios that deepen one problem family inside statistics.
ML/stats
A ml/stats emphasis usually appears after foundations, when students choose seminars, labs or studios that deepen one problem family inside statistics.
Econometrics
A econometrics emphasis usually appears after foundations, when students choose seminars, labs or studios that deepen one problem family inside statistics.
Skill map
Inference94
Experimental design90
Coding (R/Python)86
Communicating uncertainty82
Data cleaning80
Ethics78
Inference
Inference is practised weekly in statistics programmes; the percentage is relative strength among skills on this page, not a grade.
Experimental design
Experimental design is practised weekly in statistics programmes; the percentage is relative strength among skills on this page, not a grade.
Coding (R/Python)
Coding (R/Python) is practised weekly in statistics programmes; the percentage is relative strength among skills on this page, not a grade.
Communicating uncertainty
Communicating uncertainty is practised weekly in statistics programmes; the percentage is relative strength among skills on this page, not a grade.
Data cleaning
Data cleaning is practised weekly in statistics programmes; the percentage is relative strength among skills on this page, not a grade.
Ethics
Ethics is practised weekly in statistics programmes; the percentage is relative strength among skills on this page, not a grade.
How you learn
Lectures & readings
Large-group framing plus primary texts or problem sets that define the week's vocabulary.
Labs / studios / clinics
Supervised practice where mistakes are expected and feedback is specific.
Seminars
Small-group argument; silence is expensive because the group notices.
Capstone / thesis
A public synthesis — defence, exhibition, clinic portfolio or engineered prototype.
Treat the catalogue as a map of practised skills, not a brand promise. The department that grades hard and returns work fast usually teaches more than the one that advertises prestige alone.
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