GATE DA vs GATE CS 2027: Which Paper Should You Write?

For NITs, IIITs and other institutes admitting through CCMT, our count of the official CCMT 2026 programme-vs-GATE-paper seat-mapping tool (checked September 2026) found DA mapped to 194 programme entries at 67 institutes — including NIT Trichy Data Analytics, NITK Surathkal Computational and Data Science, and AI/Data-science programmes at NIT Delhi, Durgapur and Raipur, MNNIT and IIIT Allahabad. Several NIT CSE M.Techs (NIT Warangal, MNIT Jaipur, NIT Delhi, Goa, Raipur and Silchar, PEC Chandigarh) accept DA too; NIT Trichy's CSE does not. The mapping is per qualifying degree, so check your own degree against each programme.

The overlap is real but smaller than it looks. Roughly a third of the 2026 DA paper — its programming, algorithms and database questions — is ground a CS candidate already covers. The rest is either probability, linear algebra and machine learning at a depth CS never asks, or AI and data-warehousing topics CS does not include. Going the other way, a DA candidate would need operating systems, networks, computer organisation, compilers and theory of computation to write CS — about half of the CS syllabus.

Fewer candidates does not mean easier. DA's smaller field is also thinner at the top, which is why 50 marks put a DA candidate around rank 770 in 2026 but a CS candidate around rank 3,650. What matters is how many seats in your target programmes are filled from each paper — and for AI and data-science M.Techs that is mostly DA.

If you are a CS candidate thinking of adding DA, or a DA candidate eyeing CS, this is the extra syllabus in each direction, read off the two official syllabi. The shared middle — data structures, algorithms and databases — you prepare once.

The asymmetry is worth noticing. For a CS graduate, the step to DA is mostly mathematics and machine learning — topics that reward steady practice over four months. For a DA candidate without a CS degree, the step to CS is five or six systems subjects, each a semester course in its own right. That is why "write both" is realistic mainly for CS graduates.

Writing both makes sense if you want the widest set of options — CSE M.Techs through CS, AI and data-science M.Techs through DA — and you already have a CS degree, because the extra syllabus for each is then manageable. It makes less sense if you are from a non-CS background: preparing all of CS on top of DA is roughly a second exam, and you would be spreading four months across two papers.