Probability
1–2Probability trains the habits statistics depends on — not trivia for exams, but reusable technique for later studios, labs or clinics.
Darker cells mean a higher score for this topic on that metric.
Academic rigor86
Selectivity70
Employability88
Creativity52
Math intensity90
People-facing44
LessMore
Last reviewed Sources & creditsMedia creditsMethodology
Quick answers
How long does a typical Statistics degree take?
Most bachelor pathways run about 4 years of full-time study, though professionally accredited or longer first degrees can exceed that, and some systems split into 3+2 Bologna structures.
Is Statistics mostly theoretical or practical?
Expect both. Foundations lean theoretical; later years push computing labs, consulting projects and seminar critiques. The balance depends on accreditation and department culture.
Do I need strong mathematics?
Math intensity on this site is scored 90/100 relative to other majors. That is a signal, not a gate — check the specific programme's calculus and statistics requirements.
How selective is entry?
Selectivity here is 70/100 relative to the other majors catalogued — a composite of typical grade barriers and competition, not a single exam cut-off.
What communities should I join?
Start with the departmental society, then look for national student chapters and one serious online forum where practitioners share primary sources rather than memes.
Are the "voices" real reviews?
They are editorial composites grounded in common, checkable student and alumni patterns — attributed by role and place, not anonymous star ratings or fabricated celebrities.
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.
Keep exploring
More in STEM & Engineering
💻
Computer Science
Algorithms, systems and software — how machines are made to reason, store and communicate.
Employability 94 ⚡Electrical Engineering
Circuits, fields and signals — powering, sensing and communicating the physical world.
Employability 88 🔧Mechanical Engineering
Forces, materials and machines — designing things that move, bear load and last.
Employability 86 🌉Civil Engineering
Infrastructure that societies stand on — bridges, water, roads, cities and codes.
Employability 84 ⚗️Chemical Engineering
Turning reactions into plants — scale, safety and efficiency from molecule to factory.
Employability 82 🛰️Aerospace Engineering
Flight in air and vacuum — structures, propulsion, guidance and certification.
Employability 76 📊Data Science
Statistics, computing and domain sense — turning messy data into decisions that hold up.
Employability 92 🧮Mathematics
Proof, structure and abstraction — the language that other sciences borrow.
Employability 70 ⚛️Physics
Matter, energy, space and time — from lab benches to the edge of the observable.
Employability 68 🧪Chemistry
Molecules and reactions — how substances change, bind and are made safely.
Employability 72