GATE Data Science and Artificial Intelligence is a young paper. It began in 2024, so a serious GATE DA 2027 plan should use all three completed papers without pretending that a long history already exists. The official figures below show rapid growth: 39,210 candidates appeared in 2024, 57,054 in 2025 and 69,242 in 2026.
How many candidates have appeared for GATE DA?
The number appearing for DA grew by about 77% between 2024 and 2026. Candidate count alone does not make a paper harder, but it does mean that a small top percentile now contains more people.
| Year | DA appeared | Growth | CS appeared |
|---|---|---|---|
| 2024 | 39,210 | First DA paper | 1,23,967 |
| 2025 | 57,054 | 46% | 1,70,825 |
| 2026 | 69,242 | 21% | 2,11,020 |
What marks may be needed for AIR under 100 in GATE DA 2027?
GATE does not publish an official “marks required for AIR 100” cutoff. Our planning model estimates the mark at which each year’s candidate count, qualifying cutoff and AIR-1 mark point toward a rank near 100. It produces targets of about 75 marks for 2024, 78 for 2025 and 74 for 2026. The median is 75 marks.
For GATE DA 2027 preparation, treat 75 as a centre point rather than a promise. A safer mock-test target is 78–80 marks, because paper difficulty, score distribution, normalization and the number of candidates can all move the actual rank boundary.
GATE DA 2027 syllabus: subjects and the topics to prepare
The GATE 2027 information brochure and syllabus are not published yet. Until they are, the right baseline is the latest official DA syllabus. It has seven technical areas, plus General Aptitude. GATE papers are 100 marks overall: General Aptitude is 15 marks and the DA syllabus covers the remaining 85 marks.
| Subject | High-value areas to cover | Planning range* |
|---|---|---|
| General Aptitude | Verbal ability, numerical ability, logical reasoning and data interpretation | 10 questions · 15 marks |
| Probability and Statistics | Counting, conditional probability, Bayes’ theorem, random variables, distributions, expectation, hypothesis tests and confidence intervals | 10–14 questions |
| Linear Algebra | Vector spaces, matrices, rank, determinants, eigenvalues, projections, LU decomposition and SVD | 5–8 questions |
| Calculus and Optimization | Limits, continuity, differentiation, Taylor series, maxima, minima and single-variable optimization | 3–6 questions |
| Programming, Data Structures and Algorithms | Python, stacks, queues, linked lists, trees, hash tables, searching, sorting, divide and conquer, graphs and shortest paths | 8–12 questions |
| Database Management and Warehousing | ER and relational models, relational algebra, SQL, constraints, normalization, indexing, transformation and warehousing models | 4–7 questions |
| Machine Learning | Regression, classification, cross-validation, neural networks, clustering and PCA | 10–15 questions |
| Artificial Intelligence | Informed and uninformed search, adversarial search, logic, uncertainty, variable elimination and sampling | 3–6 questions |
*The ranges are GATE Topper planning estimates from the three completed DA papers and syllabus breadth, not an official GATE blueprint. Individual subjects can move significantly from one year to another; use them to decide revision time, not to skip a subject.
Probability, statistics and linear algebra
These subjects support much of the DA paper. Do not prepare distributions, Bayes’ theorem, matrix operations, eigenvalues or SVD as isolated formulas. Practise converting a question into the right random variable, matrix or equation before calculating. That habit carries directly into machine-learning questions too.
Machine learning and AI
For machine learning, understand what a model is optimizing, what its assumptions are and how validation changes the answer. Revise linear and logistic regression, ridge regression, k-NN, naive Bayes, SVMs, trees, cross-validation, clustering, PCA and feed-forward networks. In AI, practise choosing the right search strategy, representing knowledge in logic and reasoning with uncertain evidence.
Python programming, data structures and algorithms
Python is explicitly part of the DA syllabus, not an optional add-on. Focus on tracing short programs, functions, lists, dictionaries, recursion, time complexity and the behaviour of core data structures. Then connect code to algorithms: binary search, sorting, merge sort, quicksort, graph traversal and shortest paths. Candidates who want a structured coding-practice route can explore Mastejri coding classes; always map any practice plan back to the official GATE DA syllabus.
Databases, warehousing, calculus and optimization
SQL and relational algebra reward precise reading, while normalization and indexing require you to identify the structure behind a scenario. For calculus and optimization, make limits, derivatives, Taylor series and maxima/minima routine enough that they do not consume disproportionate time in a full paper.
How to use this split without overfitting to a prediction
First finish every syllabus area. Then give more weekly revision time to probability and statistics, machine learning, programming and linear algebra because they have broad coverage and connect to other topics. Keep a short error log: concept gap, calculation error, misread wording or time-management mistake. The goal is not to guess GATE 2027 exactly; it is to be prepared when the paper makes a different choice.
Is GATE DA harder than GATE CSE?
There is no single honest yes-or-no answer. DA has fewer candidates than CS, but it is not automatically easier. Its mathematics is deeper in probability, statistics and linear algebra, while CS covers a broader systems and theory syllabus. Your background changes which paper feels harder.
GATE DA
- Strong emphasis on probability, statistics, linear algebra and machine learning
- 69,242 candidates appeared in 2026
- General cutoff moved from 37.1 in 2024 to 26.4 in 2026
- Only three previous papers exist
GATE CS
- Broader coverage of algorithms, theory, architecture, operating systems, networks and databases
- 2,11,020 candidates appeared in 2026
- General cutoff stayed near 28–30 during 2024–2026
- A much larger archive of previous papers exists
The median General-category qualifying cutoff across 2024–2026 was 29.0 for DA and 29.2 for CS. Those similar medians do not mean equal difficulty: qualifying cutoffs describe the lower boundary for qualification, while an AIR under 100 concerns the extreme top of each paper’s distribution.
A practical GATE DA 2027 preparation target
Build first toward consistent scores above the qualifying range, then raise the target in stages: 50, 60, 70 and finally the high 70s. Take full three-hour papers, record whether each lost mark came from a concept gap, calculation error or poor question selection, and reattempt the question before calling it mastered.
Because DA has only three official papers, do not exhaust them casually. Use one as a diagnostic, preserve one for a mid-course checkpoint and take the newest paper under strict exam conditions closer to the exam. Topic practice and selected mathematics, programming and algorithm questions from CS can fill the gaps between full papers.
Sources and methodology
The subject list and topic coverage follow the official GATE 2026 DA syllabus, used as the published baseline until GATE 2027 releases its own syllabus. Candidate counts and General cutoffs come from the official GATE 2024, GATE 2025 and GATE 2026 cutoff tables. The marks-for-rank figures are GATE Topper planning estimates, not official cutoffs or admission guarantees. We will append the next completed exam year when verified official data becomes available.