Popularity and employability are not the same. A major can be crowded because it is fashionable, or scarce because it is hard — both show up in different metrics.
Figures below are relative signals for comparison on this site, anchored to qualitative regional patterns rather than a single global ranking table.
Popularity over time
Relative interest in the major as a study choice, 0–100 within this page's own scale.
In many systems data science was a narrower pathway, often tied to state or industrial demand rather than mass enrolment.
Expansion of higher education widened access; data science tracked national development priorities and graduate unemployment fears differently by region.
Global league tables and professional licensing began to reshape how students ranked the major against peers.
Digital platforms and labour-market dashboards made enrolment swings more visible; data science felt those swings where skills mapped cleanly to job ads.
Pandemic disruption, AI tools and credential inflation pushed students to ask harder questions about return on study time — not only brand.
Headline signals
Employability signal
Relative among Tool-Lifes majors; not a placement rate.
Selectivity signal
How contested entry typically is versus peers on this site.
Rigor signal
Workload and conceptual difficulty relative to the catalogue.
People-facing
How much of the trained work involves clients, patients, audiences or teams.
Where interest is strongest
United States
Prestige hierarchies and tuition shape demand; outcomes diverge by institution more than title.
South Korea
Entrance competition and parental strategy strongly influence major choice.
Germany
University vs Fachhochschule / dual study options change what the same major means.
Brazil
Public university seats remain highly contested for many fields.
India
Entrance ecosystems and private colleges create wide quality spreads under one label.
Nigeria
Accreditation and professional exams often matter more than the bachelor brand alone.
Outlook
Expect continued pressure for programmes to show skills artefacts — portfolios, clinics, published code, audited projects — not only transcripts.
Automation will reshape tasks inside data science unevenly; judgement, accountability and domain context remain harder to replace than routine production.
Use demand pages to compare trade-offs, not to chase last year's fad. Fads move faster than curricula.
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 🧮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