Analytics & BI
Decision dashboards at scale.
Darker cells mean a higher score for this topic on that metric.
Academic rigor78
Selectivity72
Employability92
Creativity66
Math intensity84
People-facing48
LessMore
Each bar is a 0-100 atlas score for this topic, not a timeline.
- 011
- 022
- 033
- 044
Last reviewed Sources & creditsMedia creditsMethodology
Quick facts
4 yearsTypical bachelor
STEM & EngineeringField
84Math intensity
92Employability*
48People-facing*
72Selectivity*
Data science joins statistics, computing and domain judgement — cleaning messy tables, choosing models you can defend, and refusing patterns that do not survive a holdout. Ethics and data provenance are part of the syllabus, not a footnote.
Campus life is seminar rooms, shared GPU queues and project studios more than wet labs. Capstones look like reproducible analyses or deployed pipelines; portfolio artefacts matter in hiring.
Roles include analyst and scientist titles across industry, public sector and research. Scores are relative among the forty majors on this site.
The profile
- Academic rigor78
- Selectivity72
- Employability92
- Creativity66
- Math intensity84
- People-facing48
Employability signal
Typical bachelor years: 4
Scores are relative across the forty majors on this site — not absolute rankings of universities.
Popularity & Demand →78Academic rigor
72Selectivity
92Employability
66Creativity
84Math intensity
48People-facing
Industries this major feeds
Sectors where graduates and research from this field show up most often — plates from Wikimedia Commons.
AI products
Models shipped into products.
E-commerce
Recommendation and pricing.
Health data
Clinical and public datasets.
People who shaped the field
Checkable figures whose work still frames how the discipline is taught and practised.
John Tukey
Exploratory analysis
1915–2000
Henry Hering (1814-1893) · Public domain
Florence Nightingale
Data for reform
1820–1910
Henry Hering (1814-1893) · Public domain
Geoffrey Hinton
Deep learning
1947–
Cmichel67 · CC BY-SA 4.0
Hadley Wickham
Tidy data tools
1979–
Hadley Wickham · CC BY-SA 4.0Seven ways into this major
Compare with neighbours
Same-field head-to-heads built from the six score axes.
Frequently asked questions
What do you study in Data Science?
Probability — Probability trains the habits data science depends on — not trivia for exams, but reusable technique for later studios, labs or clinics.
What does a Data Science career path look like?
Foundation years — 0–2 — Build vocabulary and habits; seek near-peer tutoring before gaps compound.
How should I prepare for Data Science?
Practise the entry craft — Problem sets, portfolios, essays or auditions that mirror first-year reality — not only entrance exams.
How long does a typical Data Science 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 Data Science mostly theoretical or practical?
Expect both. Foundations lean theoretical; later years push computing labs, project studios and critique seminars. The balance depends on accreditation and department culture.
Do I need strong mathematics?
Math intensity on this site is scored 84/100 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 72/100 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.
Similar majors
Why labels are thematic — not a ranking of better majors.
Scores are browsing aids for curriculum and career lanes, not a league table of worth.
🔧
Mechanical Engineering
Forces, materials and machines — designing things that move, bear load and last.
Employability 86 Same field neighbour
💻
Computer Science
Algorithms, systems and software — how machines are made to reason, store and communicate.
Employability 94 Same field neighbourMore 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 🧮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 📉Statistics
Uncertainty made usable — design, inference and the ethics of claiming a pattern is real.
Employability 88