GATE DA — Preparation, Tests and Notes

GATE DA (Data Science and Artificial Intelligence) is a GATE 2027 paper for M.Tech, MS and PhD admission to India's data science and AI programmes. This overview covers eligibility, exam pattern, syllabus, cutoffs and MastersUp's adaptive GATE DA 2027 preparation track.

Description

Placement Statistics

## Placement Statistics Verified, GATE-DA-specific placement statistics are not yet publicly available: the first DA-admitted M.Tech cohorts (which joined programmes like IIT Madras's WSAI in mid-2024) are only now completing a standard two-year programme, so department-level placement reports for this exact pathway haven't been published as of this writing. [NEEDS VERIFICATION: GATE DA M.Tech placement statistics by institute and year] The closest verifiable, comparable data point comes from a related but distinct cohort: IIT Guwahati's Mehta Family School of Data Science and AI, which graduated its first batch — admitted via JEE for the BTech programme, not GATE DA — in 2025, with an officially reported 91% placement rate and recruiters including Google, Microsoft, and Warner Bros Discovery. This is useful as an indicator of strong, established industry demand for graduates of dedicated data-science-and-AI schools in India generally, but should not be read as a GATE DA placement figure, since the admission route, degree level, and student pool are different. Historical placement trend narratives specific to GATE-DA-driven M.Tech/MS programmes will become available as more institutes' cohorts graduate over the next one to two admission cycles. Until departmental data is published, prospective candidates evaluating placement strength should look at the host department's broader AI/ML research output and existing (JEE-admitted or general-GATE-admitted) placement history as an indirect signal, while treating any specific DA-cohort salary or placement-percentage figure circulating online with caution unless it cites an official institute source.

Course Overview

## Course Overview GATE DA is not a college degree in itself — it is one of 30 subject papers of the Graduate Aptitude Test in Engineering (GATE), a national-level exam jointly conducted by IISc Bangalore and the IITs on behalf of the National Coordination Board–GATE, Ministry of Education. Clearing the DA paper gives you a GATE score, valid for three years, that you use to apply for postgraduate seats — M.Tech, MS (Research), and PhD — in data science, machine learning, and artificial intelligence at IITs, IISc, NITs, IIITs, and other GFTIs. DA was introduced in 2024, making it one of the newest GATE papers, built specifically to test the mathematics, programming, and machine learning foundations that data science and AI roles demand — distinct from the more classical computer-science theory tested in GATE CS. The paper covers seven core subjects (probability and statistics, linear algebra, calculus and optimisation, programming and data structures, database management, machine learning, and artificial intelligence) plus a General Aptitude section common to all GATE papers. Academically, it is aimed at final-year and graduated students of engineering, science, and related disciplines — not only computer science graduates — who want to move into data-science-and-AI-focused postgraduate research or, increasingly, PSU and industry recruitment. GATE 2027, the exam cycle currently open for registration, is being organised by IIT Madras and will be held across six days in February 2027. On MastersUp, "this course" refers to the structured, adaptive GATE DA preparation track — verified notes, topic-wise practice, and mock tests aligned to the current DA syllabus — not a university programme; the actual degree is awarded by whichever institute admits you after the exam.

Entrance Exam Details

## Entrance Exam Details GATE DA follows the same overall GATE framework as every other paper — computer-based test, GOAPS registration, common result window — but several details are specific to this paper. Unlike GATE CS, EC, or ME, DA has no separate "Engineering Mathematics" section; probability, linear algebra, and calculus are examined as core DA subjects in their own right, which means they carry direct sectional weight rather than being a smaller bolt-on section. Because DA was introduced only in 2024, GATE 2027 will be its fourth edition, so the previous-year-question pool (2024, 2025, and 2026 papers) is thinner than for legacy papers like CS or ME that have decades of archives — worth building into any preparation plan for this specific paper. DA is also not tied to a single feeder branch: candidates from CS, electronics, electrical, mathematics, statistics, and even non-engineering science backgrounds sit the paper, provided they build the required programming and ML foundations, unlike papers tied more tightly to one engineering discipline. At the admission stage, DA has an unusual eligibility pattern: some flagship programmes — notably IIT Madras's M.Tech in Data Science and AI at the Wadhwani School of Data Science and AI (WSAI) — accept only a valid GATE DA score and no other GATE paper for that specific seat. Other departments, such as IIT Hyderabad's Department of Artificial Intelligence, treat DA as one of several eligible papers (alongside CS, EC, EE) rather than the sole route in. This means the "one national cutoff opens every door" pattern common to older papers doesn't fully apply to DA — which programmes accept it, and whether it's the only accepted paper or one of several, varies by department and needs checking on each institute's own admission page before finalising a target list.

Exam Pattern

## Pattern Details GATE DA is a 3-hour computer-based test (CBT) worth 100 total marks across 65 questions, split into General Aptitude (15 marks) and the core Data Science and Artificial Intelligence section (85 marks). The exam is held in either a forenoon (9:30 AM–12:30 PM) or afternoon (2:30 PM–5:30 PM) session, with GATE 2027's DA paper scheduled within the exam's six-day window in February 2027, and the specific session/date assigned per candidate. Three question types appear throughout: MCQ (single correct answer, with negative marking), MSQ (one or more correct answers, no negative marking, no partial credit), and NAT (typed numeric answer, no negative marking). Questions carry either 1 or 2 marks each. The negative-marking rule applies only to MCQs: −1/3 mark for a wrong 1-mark MCQ and −2/3 mark for a wrong 2-mark MCQ; MSQ and NAT questions never lose marks for a wrong attempt, only gain nothing. One pattern detail specific to DA versus several older GATE papers: there is no separate Engineering Mathematics section, so probability, linear algebra, and calculus are tested as core DA subjects with direct sectional weight rather than as a smaller standalone block. Scores are converted to a normalised GATE score (out of 1000) that adjusts for difficulty variation across sessions, using a formula published in the official GATE Information Brochure each year — always refer to that year's brochure for the exact normalisation method and any pattern updates, such as GATE 2027's additional verification steps (live facial verification, DigiLocker document integration) introduced at the registration stage rather than in the exam pattern itself.

Skills Learning Outcomes

## Skills and Learning Outcomes Preparing for GATE DA builds a specific, named skill set rather than vague "analytical thinking." On the mathematics side: probability distributions and statistical inference (hypothesis testing, estimation), linear algebra used directly in ML (eigenvalues/eigenvectors, singular value decomposition, vector spaces, matrix decompositions underlying PCA and SVMs), and calculus-based optimisation (gradient descent, convex optimisation, Lagrange multipliers). On the computing side: Python programming, core data structures and algorithms (arrays, trees, graphs, sorting/searching, complexity analysis), and database management and warehousing (SQL, normalisation, schema design, OLAP concepts). On machine learning and AI specifically: supervised methods (linear/logistic regression, decision trees, SVMs, ensemble methods), unsupervised methods (clustering, dimensionality reduction), neural network and deep learning fundamentals, and core AI techniques — informed and uninformed search, adversarial search, propositional and predicate logic, and reasoning under uncertainty (conditional independence, exact inference via variable elimination, approximate inference via sampling). These are the same building blocks used in real data science and applied-AI roles, which is why GATE DA preparation is often described as dual-purpose: it builds exam-ready recall of formulas and algorithms and, when paired with implementation practice rather than pure theory, functional competence in the tools (Python, SQL, ML libraries) that DS/AI teams actually use day to day.

Admission Procedure

## Admission Procedure Getting from "GATE DA aspirant" to "enrolled in an M.Tech/MS/PhD seat" runs through several stages. First, registration on GOAPS (the GATE Online Application Processing System) during the window set by that year's organising institute — for GATE 2027 (IIT Madras), GOAPS opens 27 August 2026, with the regular deadline on 27 September 2026 and an extended, late-fee window until 5 October 2026. Second, the computer-based exam itself, held across designated February weekends. Third, results and scorecards, released roughly a month later, along with the category-wise qualifying cutoffs for each paper, including DA. From there, the process splits by target institute. For IITs, qualified candidates apply through COAP (Common Offer Acceptance Portal) once individual departments open their DA-based M.Tech/MS admissions and publish their own — usually higher — admission cutoffs or shortlisting thresholds. For NITs, IIITs, and GFTIs, the CCMT (Centralised Counselling for M.Tech/M.Arch/M.Plan) portal handles seat allocation. Some departments layer on an extra stage specific to their programme: IIT Hyderabad's AI department, for instance, shortlists by GATE score and/or academic background and may then call candidates for a written test and/or interview before a final offer; IIT Madras Zanzibar runs its own separate M.Tech screening test rather than relying purely on GATE DA scores. Once shortlisted or allotted a seat, candidates go through document verification (degree certificates, category certificates where applicable, GATE scorecard), fee payment, and registration at the admitting institute. Because DA-specific seats are still relatively few compared to legacy papers, and acceptance rules differ department by department, checking each target institute's current DA-admission notice — rather than assuming one unified GATE admission process — is a necessary step for this particular paper.

Preparation Strategy

## Preparation Strategy GATE DA rewards a sequenced approach because its subjects build on each other: the mathematics sections are prerequisites for machine learning, and machine learning is consistently among the heaviest-weighted subjects in the paper. Step 1 — Foundational mathematics first: Start with linear algebra (vector spaces, eigenvalues, SVD, matrix decompositions) and probability & statistics (distributions, estimation, hypothesis testing) before touching machine learning, since ML topics like PCA, SVM, and neural-network backpropagation are built directly on these. Calculus and optimisation (gradients, convexity, Lagrange multipliers) belongs in this same early phase. Step 2 — Programming and DSA in parallel: Python, data structures, and algorithms don't depend on the math track, so run them alongside Step 1 rather than sequentially — daily, shorter practice sessions here tend to beat occasional long ones. Step 3 — Machine learning, once the math is stable: With foundations in place, move to supervised and unsupervised ML methods and neural-network basics — this is consistently one of the highest-weighted individual subjects in recent DA papers, so it deserves the largest single block of study time. Step 4 — The "quick-convert" subjects: Artificial intelligence (search, logic, probabilistic reasoning), database management and warehousing, and any remaining calculus/optimisation topics are comparatively compact syllabi that convert into marks faster once the heavier subjects are under control — good subjects for the middle-to-late preparation phase. Step 5 — Previous-year questions, subject by subject: After each topic, solve every available GATE DA PYQ on it (2024, 2025, and 2026 papers, since DA doesn't yet have the multi-decade archive that CS or ME candidates draw on) to calibrate what "exam-ready" actually means for that topic, rather than relying on textbook depth alone. Step 6 — Mock tests and General Aptitude, throughout the final stretch: GA is only 15 marks but comparatively low-effort-per-mark, so don't leave it for the last week. Full-length, timed mocks should start once most of the syllabus is covered and continue weekly through to the exam, with each mock followed by focused error review rather than just a score check. Step 7 — Revision: In the final three to four weeks, shift from new content to formula sheets, common-mistake logs from mock analysis, and timed sectional drills on your two or three weakest areas. Indicative time-allocation profiles: Starting from a non-CS science/engineering background (strong math, little programming): roughly 40% of study time on Python/DSA/DBMS to close the programming gap, 35% on ML/AI, and the remaining 25% split across math revision, GA, and mocks, since the math foundation is usually already stronger for this profile. Starting from a CS/engineering background with programming experience but light ML/stats theory: roughly 45% on probability, statistics, and machine learning theory, 25% on linear algebra/calculus as applied to ML, 15% on AI, DBMS, and GA, and the remaining 15% on mocks and revision started early. Repeat attempt or advanced candidate refining an existing score: shift the bulk of remaining time — roughly 60% — into full-length mocks, PYQ-based speed drills, and targeted revision of the two or three topics causing the most lost marks, rather than re-studying the syllabus broadly.

Syllabus

## Complete Syllabus The GATE DA syllabus is structured as eight sections in total: General Aptitude (shared across every GATE paper) plus seven core DA-specific sections — Probability and Statistics; Linear Algebra; Calculus and Optimisation; Programming, Data Structures and Algorithms; Database Management and Warehousing; Machine Learning; and Artificial Intelligence. The full topic-by-topic breakdown within each section is detailed in the subject, unit, and chapter tables on this page — this section explains how the syllabus is shaped and how to sequence studying it, rather than repeating the full topic list. Structurally, the syllabus has a clear dependency chain: Linear Algebra, Probability and Statistics, and Calculus and Optimisation function as mathematical prerequisites that Machine Learning and, to a lesser extent, Artificial Intelligence build directly on top of — concepts like PCA, SVMs, and neural network training only make sense once the underlying linear algebra and probability are solid. Programming, Data Structures and Algorithms sits somewhat independently and can be studied in parallel with the math track. Database Management and Warehousing is the most self-contained core section, requiring the least dependency on other subjects. Based on analysis of the GATE DA papers held so far (2024, 2025, 2026 — this being a newer paper without a multi-decade archive), Machine Learning, Programming/DSA, and Probability & Statistics have consistently carried the largest individual shares of the 85 core marks, though GATE does not publish an officially fixed weightage and the exact distribution moves somewhat year to year, so any specific percentage should be read as an informed estimate rather than a guarantee. For the current official topic list, syllabus PDF, and any year-over-year syllabus revisions (GATE 2027 introduced subject-wise syllabus updates across multiple papers), always cross-check against the syllabus document published on that year's official GATE website, since exam-prep summaries — including this one — can lag an official update.

Course Curriculum

## Course Curriculum GATE DA itself has no "curriculum" beyond its exam syllabus — the curriculum question really applies to the M.Tech, MS, or PhD programme you're admitted into afterward, and its shape varies by institute. Using IIT Madras's M.Tech in Data Science and AI (Wadhwani School of Data Science and AI, WSAI) as an illustrative example, since it is the flagship programme built specifically around the DA paper: it runs as a standard two-year, four-semester M.Tech, with the first two semesters weighted toward core and foundational coursework — mathematical foundations (probability, linear algebra, optimisation), programming, and core machine learning and deep learning — and the later semesters shifting toward electives, domain-application coursework, and a significant thesis or project component carried out with close faculty supervision, often through WSAI's affiliated research centres. Other DA-accepting departments structure things differently. IIT Hyderabad's Department of Artificial Intelligence, for example, runs multiple admission "modes" (GATE-score-based, high-CGPA-based, and project-experience-based) that feed into the same core-plus-elective-plus-thesis M.Tech shape, but with different screening stages before entry. Programmes at NITs and IIITs generally follow the standard AICTE-aligned two-year M.Tech structure — core subjects in the first year, electives and a project/thesis in the second — though elective menus and thesis expectations differ by department. Across these variations, the general sequencing a DA-driven M.Tech candidate should expect is consistent: foundational and core coursework front-loaded in year one, followed by increasing specialisation, lab or research-group attachment, and a thesis or major project in year two, with the core-versus-elective balance and depth of the research component being the main things to compare when shortlisting between DA-accepting institutes. Because WSAI's M.Tech is unusually GATE-DA-exclusive, its published curriculum is the most directly relevant public reference for DA aspirants planning around "what happens after the exam" — programmes at other institutes should be checked individually before assuming the same core-to-elective ratio applies.

Day In Life

## A Day in the Life There's no single "day in the life" for GATE DA as an exam — a working professional revising in the evenings, a final-year B.Tech student preparing alongside coursework, and a full-time repeat-attempt aspirant all look different day to day. What's common on MastersUp is the shape of the routine, since the platform itself is a fully online, AI-adaptive self-study tool rather than a scheduled classroom programme. A typical weekday for a full-time aspirant might start with two to three hours on a single core subject — say, machine learning or linear algebra — worked through structured, verified notes rather than scattered internet sources, followed by a set of topic-wise practice questions. Because the platform tracks progress and flags weak areas as it goes, the questions served next are meant to concentrate on what a student is actually getting wrong, rather than a fixed worksheet everyone gets. Midday is often spent on programming/DSA practice, since coding problems reward daily repetition more than long single sessions. Evenings tend to shift toward review: revisiting flagged weak topics, attempting a chapter-wise or subject-wise mock, or working through previous-year questions for whichever subject was covered that day. Weekly rhythm typically adds one longer session — a full-length, timed mock test under exam-like conditions — followed by a review pass rather than moving straight to new content, since analysing wrong answers is usually where the biggest score gains come from at this stage. As the exam date approaches (registration for GATE 2027 opens 27 August 2026, with the test itself in February 2027), this rhythm compresses: less new content, more timed sectional practice and revision of a personal "mistakes list" built up over the preceding months.

Campus Life

## Campus Life MastersUp is a fully online, AI-powered self-study platform — there is no physical campus, hostel, or classroom attached to GATE DA preparation here, and this page won't claim otherwise about the platform itself. What "campus life" does apply to is the postgraduate programme a candidate joins after clearing the DA paper, and that varies significantly by institute. At IIT Madras's Wadhwani School of Data Science and AI (WSAI) — the department built specifically around this paper — DA-admitted M.Tech students join India's largest dedicated AI department by faculty count (15 full-time faculty at launch in 2024), consolidating existing research infrastructure such as the Robert Bosch Centre for Data Science and AI (RBCDSAI, established 2017), which runs active research in areas including NLP, computer vision, and healthcare AI. Students in this kind of dedicated school typically have access to specialised compute and lab resources, closer faculty-to-student research supervision than in a general department, and a cohort of similarly AI-focused peers across the BTech, MTech, and PhD levels. At other DA-accepting institutes — IIT Hyderabad's AI department, various NITs, and IIITs — campus facilities and the degree of AI-specific specialisation differ, and generic claims about labs, housing, or extracurricular culture at "a GATE DA college" shouldn't be assumed uniformly; these are worth confirming on each institute's own department page once a shortlist exists, since the DA-admission ecosystem is still young and unevenly documented compared to legacy engineering programmes.

Alumni Stories

## Alumni Stories GATE DA is young enough — first held in 2024 — that its own M.Tech-admitted cohort (which joined dedicated programmes like WSAI in mid-2024) is only now reaching the end of a standard two-year programme, so verifiable, named alumni placement stories specific to GATE-DA-admitted postgraduate students are not yet publicly documented in detail. [NEEDS VERIFICATION: specific alumni stories for GATE DA-admitted M.Tech/MS cohorts] What is documented, and useful as context for the broader dedicated data-science-and-AI school model that GATE DA feeds into, is the outcome of a related — though not identical — cohort: IIT Guwahati's Mehta Family School of Data Science and AI (launched 2021) graduated its first BTech batch (admitted via JEE, 2021–2025) in 2025, with an officially reported 91% placement rate and recruiters including Google, Microsoft, and Warner Bros Discovery; one graduate went on to further study at Carnegie Mellon University. This is a JEE-admitted undergraduate cohort, not a GATE-DA-admitted postgraduate one, so it should be read as an indicator of strong industry demand for graduates of dedicated AI/DS schools generally — not as a DA-specific placement statistic. Until GATE-DA-specific outcome data is published by individual departments, the more reliable way to gauge likely trajectories is to look at typical patterns rather than individual stories: DA-admitted M.Tech graduates from dedicated schools like WSAI tend to move into applied ML/data-engineering roles, applied-research or R&D positions at technology companies, continue into PhD programmes at the same or a partner institute, or — as GATE DA's acceptance by PSUs and public-sector recruiters matures — pursue government-sector technical roles that increasingly recognise the DA paper alongside longer-established ones like GATE CS.

Global Exposure

## Global Exposure International exposure tied specifically to GATE DA depends entirely on which postgraduate programme a candidate is admitted into, since the exam itself has no international component. At IIT Madras's Wadhwani School of Data Science and AI — the department most closely tied to this paper — publicly available programme information references DAAD exchange fellowships and other overseas research opportunities available to graduate students, alongside government-funded fellowships for eligible candidates, though exact eligibility, current availability, and application windows for these should be verified directly on WSAI's official site before factoring them into a decision. WSAI's head of department, Prof. Balaraman Ravindran, has international visibility in the AI research community — he has been named among TIME magazine's 100 most influential people in AI and was appointed to the United Nations' Independent International Scientific Panel on AI in 2026 — a reasonable, verifiable signal of the department's research connections, though not itself a guarantee of student-level exchange opportunities. For other DA-accepting institutes, specific international exchange programmes, dual-degree tie-ups, or global faculty collaborations are not consistently documented in public sources at this stage and should be checked department by department. [NEEDS VERIFICATION: global exposure details for GATE DA-accepting institutes beyond WSAI]

Course Comparison

## Course Comparison GATE DA vs GATE CS: Both lead to computer- or data-focused postgraduate seats, but the papers test different things. GATE CS leans on classical computer-science theory — theory of computation, compilers, computer organisation, operating systems — alongside programming and DSA. GATE DA replaces most of that theoretical-CS core with probability, statistics, machine learning, and AI, and has no separate Engineering Mathematics section since the math is embedded directly in its core subjects. Practically, GATE CS is accepted by a far wider, longer-established set of M.Tech CS programmes across almost every IIT/NIT/IIIT, while GATE DA's acceptance is newer and narrower — some flagship programmes like WSAI's M.Tech in Data Science and AI accept only DA and no other paper, while others treat it as one of several eligible options. A candidate aiming specifically at data science, ML, or AI research is better served by DA's directly aligned syllabus; one wanting the widest possible net of eligible M.Tech CS seats is better served by CS's broader acceptance. GATE DA path vs ISI MSQMS / CMI MSDS entrance (both also covered on MastersUp): these are structurally different routes to a similar destination. GATE DA is a single national exam accepted, with varying rules, by many institutes, with no age or attempt limit and a three-year-valid score usable across multiple admission cycles. ISI's MSQMS and CMI's MSDS, by contrast, are admitted through each institute's own dedicated entrance test, leading to a seat at that specific institute only, with a curriculum that tends to be more theoretical-statistics-heavy (ISI in particular) than the applied ML/AI/programming mix GATE DA covers. A candidate wanting flexibility across many institutes and a broader applied AI/ML skill set is usually better served preparing for GATE DA; one set on a specific institute's more mathematically rigorous statistics programme may be better served preparing directly for that institute's own entrance test instead.

Quick Facts

## Quick Facts **Q: What does GATE DA stand for?** A: GATE DA stands for Data Science and Artificial Intelligence, one of 30 subject papers offered under the Graduate Aptitude Test in Engineering (GATE). Introduced in 2024, it tests mathematics, programming, database management, machine learning, and AI, alongside a common General Aptitude section, for admission to postgraduate data-science-and-AI programmes. **Q: Who conducts GATE DA 2027?** A: GATE 2027, including the DA paper, is organised by IIT Madras on behalf of IISc Bangalore and the IITs, under the National Coordination Board–GATE, Ministry of Education. The organising institute rotates yearly; IISc Bangalore ran DA's first edition in 2024, followed by IIT Roorkee (2025) and IIT Guwahati (2026). **Q: Is there an age limit for GATE DA?** A: No. GATE, including the DA paper, has no minimum or maximum age limit, and candidates of any age — students, repeat attempters, or working professionals — can apply as long as they meet the educational eligibility requirement. **Q: How many times can I attempt GATE DA?** A: There is no cap on attempts. Candidates can appear for GATE DA every year they meet the eligibility criteria, and each attempt produces an independent score valid for three years from the result date. **Q: Do I need a computer science background to attempt DA?** A: No. GATE DA is open to candidates from engineering, science, mathematics, statistics, and related disciplines, not only CS/IT. Candidates from other branches still need to build the required programming, statistics, and machine-learning foundations themselves. **Q: What is the GATE DA exam pattern?** A: A 3-hour computer-based test worth 100 marks across 65 questions: 15 marks of General Aptitude and 85 marks of core DA subjects, using MCQ, MSQ, and Numerical Answer Type (NAT) questions, with negative marking applied only to MCQs. **Q: How many sections does the GATE DA syllabus have?** A: Seven core sections — Probability and Statistics, Linear Algebra, Calculus and Optimisation, Programming/Data Structures/Algorithms, Database Management and Warehousing, Machine Learning, and Artificial Intelligence — plus the General Aptitude section common to every GATE paper. **Q: What was the GATE DA qualifying cutoff in recent years?** A: The General-category qualifying cutoff has trended downward as the candidate pool has grown: approximately 37.1 in 2024, 29.0 in 2025, and 26.4 in 2026. OBC-NCL/EWS cutoffs are set at about 90% of the General cutoff, and SC/ST/PwD at roughly two-thirds, per the standard GATE category-relaxation rule. **Q: Is the qualifying cutoff the same as the admission cutoff?** A: No. The qualifying cutoff only secures a valid scorecard; the admission cutoff — the competitive score needed for a seat at a specific institute and programme — is set independently by each department and is typically well above the bare qualifying mark, especially for high-demand, limited-seat programmes. **Q: How many candidates appear for GATE DA?** A: The candidate pool has grown quickly since 2024: around 39,000 candidates in the first year, rising to roughly 75,900 registered and about 57,000 appearing in 2025, making DA one of GATE's fastest-growing papers. [NEEDS VERIFICATION: exact 2026 and 2027 candidate figures] **Q: How long is a GATE score valid?** A: A GATE score, including for the DA paper, is valid for three years from the date results are announced, so a single qualifying attempt can be used across more than one admission cycle if needed. **Q: Which institutes accept GATE DA scores?** A: IIT Madras's Wadhwani School of Data Science and AI requires GATE DA specifically for its M.Tech in Data Science and AI; IIT Hyderabad's AI department accepts DA alongside CS/EC/EE; other IITs, NITs, and IIITs vary in whether and how they accept DA, so checking each target department's current admission notice is essential. **Q: What is the GATE 2027 application fee?** A: As currently stated for GATE 2027, the application fee is ₹2,000 per paper for General/OBC-NCL/EWS candidates and ₹1,000 for Female/SC/ST/PwD candidates during the regular registration window, plus an additional ₹500 late fee in the extended window — confirm the final figure on the official GOAPS portal when applying. **Q: When does GATE 2027 registration open?** A: GOAPS registration for GATE 2027 opens on 27 August 2026, with the regular (no late fee) deadline on 27 September 2026 and an extended, late-fee window until 5 October 2026. The exam is scheduled across six days in February 2027. **Q: Does GATE DA have a separate Engineering Mathematics section?** A: No. Unlike GATE CS or EC, DA does not carry a separate Engineering Mathematics section — probability, linear algebra, and calculus are tested directly as core DA subjects with their own sectional weight. **Q: Can I apply for GATE DA and another paper in the same year?** A: Yes, GATE allows candidates to apply for up to two papers in the same year, provided the specific combination is permitted by that year's organising institute — check the current GATE brochure for allowed paper combinations. **Q: Is there a scholarship for GATE-qualified M.Tech students?** A: Many GATE-qualified, full-time M.Tech students (including DA-admitted candidates) are eligible for the MoE/AICTE PG scholarship or an institute assistantship, commonly cited around ₹12,400 per month, though exact eligibility and continuation conditions vary by institute and should be confirmed with the admitting department. **Q: Is GATE DA used for PSU recruitment?** A: Several established GATE papers are used directly by PSUs for recruitment; because DA is comparatively new, its adoption by individual PSUs for direct recruitment is still developing, and candidates should check individual PSU notifications rather than assume the same pathways as legacy papers apply yet. [NEEDS VERIFICATION: current list of PSUs recruiting via GATE DA specifically]

Cutoff Marks

## Cutoff Marks GATE DA's qualifying cutoff — the minimum score needed for a valid scorecard, not for admission to a specific programme — has fallen each year since the paper launched, as the candidate pool has grown: from roughly 37.1 (General) in 2024 to 29.0 in 2025 and 26.4 in 2026. Category relaxation follows GATE's standard rule: OBC-NCL/EWS candidates qualify at approximately 90% of the General cutoff, and SC/ST/PwD candidates at roughly two-thirds of it — confirmed by the actual 2025 released figures of 29.0 (General), 26.1 (OBC-NCL/EWS), and 19.3 (SC/ST). Because GATE DA 2027 results aren't out yet, any 2027 cutoff figure is necessarily an estimate based on this multi-year trend, not a confirmed number. ## GATE DA 2027 Expected Qualifying Cutoff (Indicative, based on 2024–2026 trend) | Category | Approx. Cutoff (Indicative) | Safe Range | |---|---|---| | General | 25–29 | 32+ | | OBC-NCL / EWS | 23–26 | 29+ | | SC / ST / PwD | 17–19 | 22+ | These indicative figures describe the qualifying threshold only. Competitive, seat-winning scores at high-demand programmes like WSAI's M.Tech in Data Science and AI run well above any of these numbers — see the Cutoff Analysis section for how to think about a genuinely "safe" admission score rather than a bare qualifying one.

Rank Marks

## Rank vs Marks Analysis The rank a given GATE DA score translates to isn't fixed by any published formula, and because DA is still building its multi-year data history (only three prior editions — 2024, 2025, 2026 — exist as of GATE 2027), a precise, DA-specific marks-to-rank table isn't as reliable as it is for decades-old papers like GATE CS or ME; treat any "marks = rank X" claim seen for DA as a rough estimate rather than a fixed conversion. Directionally, the pattern that holds across GATE papers generally also applies here: the 40–65 mark band tends to be the most densely populated and most rank-sensitive range, where even a 2–3 mark difference can shift rank by a meaningful margin, while very high scores (75+) separate out into a much thinner, lower-competition band typically associated with the strongest institutes and, where applicable, PSU shortlists. Because DA's candidate pool is still smaller and growing year to year, rank-at-a-given-mark is also moving from year to year — a rank that corresponded to a given score in 2025 will not directly translate to 2027 as more candidates enter the paper. [NEEDS VERIFICATION: official GATE DA marks-vs-rank data by year]

Eligibility Criteria

## Eligibility Criteria GATE DA's eligibility to sit the exam is broad and, on its own, has no age limit and no cap on attempts — a rule common to all GATE papers. Academically, candidates must be currently in the third year or higher of a government-recognised undergraduate degree, or have already completed one, in Engineering, Technology, Architecture, Science, Commerce, Arts, or Humanities; a four-year B.S./B.Sc. (Research) is also accepted. There is no minimum percentage or CGPA required just to appear for the exam, and candidates with backlogs in their qualifying degree can still apply. Unlike some other GATE papers, DA is not restricted to a specific feeder branch such as CS or IT — candidates from mechanical, electrical, physics, mathematics, statistics, or other eligible backgrounds can choose the DA paper, provided they are prepared to build the required programming, statistics, and ML foundations themselves. Both Indian nationals and candidates from a defined set of eligible foreign countries can apply, subject to nationality rules published in the current GATE brochure. Reserved-category candidates (SC/ST/OBC-NCL/EWS/PwD) need valid category or disability certificates in the prescribed format at the appropriate stage of the process; OBC-NCL/EWS certificates are generally required at the counselling/admission stage rather than at GATE registration itself. It's important to separate exam eligibility from admission eligibility: while GATE itself sets no minimum marks to appear, individual institutes commonly require roughly 55–60% marks (or equivalent CGPA) in the qualifying degree at the point of actual M.Tech/MS admission, on top of a competitive GATE score.

Placement

## Placement Details Roles typically associated with DA-driven M.Tech/MS/PhD graduates from dedicated data-science-and-AI departments include machine learning engineer, data scientist, applied research scientist, AI/ML product roles, and data engineering positions, based on the applied ML, deep learning, statistics, and domain-application curriculum these programmes are built around. Recruiters at comparable dedicated AI/DS schools in the IIT system — drawing on IIT Guwahati's documented first-batch outcomes — have included large technology companies such as Google and Microsoft as well as media/technology firms like Warner Bros Discovery, alongside the broader mix of core-tech and analytics employers that typically recruit from top engineering institutes' placement seasons generally. Company-specific and salary figures tied specifically to GATE-DA-admitted graduates are not yet independently verifiable and are not stated here as confirmed data. [NEEDS VERIFICATION: company-wise and salary-wise placement data for GATE DA-admitted cohorts specifically] Beyond direct campus placement, GATE DA also opens two other outcome paths worth noting: continuation into a PhD at the same or a partner institute, a common trajectory for students in research-heavy dedicated AI schools like WSAI; and, as the DA paper matures and gains wider recognition, potential PSU recruitment routes that currently apply more established to older GATE papers. Candidates weighing placement strength as a factor in choosing between DA-accepting institutes are better served comparing each department's existing (even if not DA-specific) placement track record, faculty research output, and industry partnerships than relying on informal, unsourced salary claims about this specific paper.

Course Outcomes

## Career Outcomes Clearing GATE DA and progressing into a data-science-or-AI-focused M.Tech, MS, or PhD opens several distinct paths rather than one fixed career track. Academically, it's a direct route into research-heavy postgraduate programmes at some of India's newest and most heavily invested-in AI departments — IIT Madras's Wadhwani School of Data Science and AI being the clearest example — with a natural continuation option into PhD study for students who want to stay in research. Industry-facing outcomes, based on the applied curriculum these programmes run (machine learning, deep learning, statistics, and project-based domain applications), typically include roles like machine learning engineer, data scientist, applied research scientist, data engineer, and increasingly generative-AI-focused engineering roles, at technology companies, analytics-driven businesses across sectors, and — as the paper's institutional recognition grows — potentially research labs and PSUs. Because the DA-driven postgraduate ecosystem is still young, longitudinal outcome data — where DA-admitted graduates end up five or ten years out — doesn't exist yet the way it does for older GATE papers. What is reasonably well established instead is the strength of the underlying demand: India's broader dedicated AI/data-science school model (illustrated by IIT Guwahati's 91%-placed first BTech cohort with recruiters like Google and Microsoft) shows real, verified employer appetite for graduates of this kind of specialised programme, even though that specific data point isn't a GATE DA figure. Prospective candidates should read GATE DA's career outcomes as strong and growing, in a genuinely high-demand field, without yet a decade of DA-specific track record to point to.

Syllabus Key Takeaway

## Key Takeaways The GATE DA syllabus is organised into seven core sections — Probability and Statistics, Linear Algebra, Calculus and Optimisation, Programming/Data Structures/Algorithms, Database Management and Warehousing, Machine Learning, and Artificial Intelligence — plus the General Aptitude section shared across all GATE papers. Unlike GATE CS or EC, there's no separate Engineering Mathematics section; the mathematics is embedded directly within the core subjects. Based on analysis of the 2024–2026 papers (GATE does not publish an official fixed weightage, so this is Indicative rather than guaranteed), Machine Learning and Programming/DSA are consistently among the highest-weighted individual subjects, with Probability and Statistics close behind; combined, the three mathematics-adjacent sections (Probability & Statistics, Linear Algebra, Calculus & Optimisation) typically account for roughly 40% of the core marks, underlining how math-heavy this paper is relative to its "data science" branding. Sequencing matters here specifically because Machine Learning depends on Linear Algebra and Probability being solid first, and because Artificial Intelligence, Database Management, and Calculus are comparatively compact, faster-to-complete sections best slotted into the middle-to-late stretch of a study plan rather than left entirely for the end alongside General Aptitude and full-length mocks.

Unit Test Keys

## Test Series Structure A GATE DA-focused test series is typically layered to match the paper's seven-section structure rather than treated as one undifferentiated question bank. The base layer is unit or chapter-wise tests — short, topic-specific sets (for example, a test purely on eigenvalues/SVD within Linear Algebra, or one purely on decision trees within Machine Learning) meant to confirm a single concept is solid before moving on. The next layer is subject-wise or sectional tests, combining every chapter within one of the seven core sections (or General Aptitude) into a timed set, used once a full section is covered to check retention and speed together rather than just conceptual correctness. The top layer is full-length mock exams, replicating the actual 65-question, 100-mark, 3-hour GATE DA structure across General Aptitude and all seven core sections together, used to build exam-day pacing and stamina once most of the syllabus is covered. MastersUp's GATE track currently includes 65+ mock tests and 15,000+ verified practice questions across this kind of layered structure, spanning the exam's General Aptitude and core DA sections.

Question Pattern Analysis

## Question Pattern Analysis GATE DA uses three question formats across its 65 questions: Multiple Choice Questions (MCQ, exactly one correct option), Multiple Select Questions (MSQ, one or more correct options, with no partial credit for partially correct selections), and Numerical Answer Type (NAT, a typed numeric value rather than a choice from options). Both 1-mark and 2-mark questions appear across General Aptitude and the core DA section, with 2-mark questions generally testing multi-step or applied problems rather than single-fact recall. The marking scheme, standard across all GATE papers including DA: a wrong MCQ answer costs 1/3 mark on a 1-mark MCQ and 2/3 mark on a 2-mark MCQ; MSQ and NAT questions carry no negative marking at all, though an unattempted question also earns nothing. This makes MSQ and NAT questions comparatively lower-risk to attempt even with partial confidence, while MCQs reward genuine elimination-based reasoning over guessing. Section-wise, the core DA subjects that have carried the heaviest question counts across the 2024–2026 papers are Programming/DSA, Machine Learning, and Probability & Statistics — consistent with their higher indicative weightage discussed elsewhere on this page — while Database Management/Warehousing and Calculus/Optimisation have tended to appear as a smaller, more concentrated set of questions. As with all weightage discussion for this paper, year-to-year question distribution should be read as a planning guide based on past papers rather than a fixed, officially guaranteed pattern, since GATE does not publish a binding per-topic question count in advance.

Cutoff Analysis

## Cutoff Analysis Two different numbers get called "GATE DA cutoff," and conflating them is the most common mistake aspirants make. The qualifying cutoff (26.4 General in 2026, trending down from 37.1 in 2024) is simply the bar for getting a valid scorecard — it says nothing about whether that score is competitive for any specific seat. The admission cutoff — set independently by each department, often not published as a single clean number the way the qualifying cutoff is — is what actually determines whether a candidate gets an offer, and for high-demand, limited-seat programmes it typically sits well above the bare qualifying mark. What counts as a "safe score" for GATE DA therefore depends entirely on which programme is being targeted. For a flagship, GATE-DA-exclusive programme like WSAI's M.Tech in Data Science and AI at IIT Madras — where DA is the only accepted paper and seats are limited relative to demand — a safe, competitive score is meaningfully higher than the qualifying threshold; treat any specific number quoted for this as Indicative and confirm against the department's own released admission statistics once available, since DA-specific historical admission-cutoff data is still thin. For departments that accept DA alongside other GATE papers (like IIT Hyderabad's AI department) or for NIT/IIIT-level DS/AI programmes, competitive scores tend to be comparatively more attainable, but are still institute- and year-specific. The trend worth planning around: as DA's candidate pool keeps growing year over year, the qualifying cutoff (likely continuing a gradual downward or stabilising trend as normalisation adjusts for a larger pool) and the admission cutoffs at popular programmes (likely rising, as more well-prepared candidates compete for a still-limited number of dedicated DA-linked seats) are moving in different directions — which is exactly why "clearing the exam" and "getting a preferred seat" are two different targets to prepare for.