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GATE Data Science and Artificial Intelligence (DA)

GATE Data Science and AI decides M.Tech/ME/PhD admission and is the recruitment filter for many PSUs. The DA paper was introduced in GATE 2024 for data science and AI programmes and jobs (the official site publishes DA papers from 2024 onwards). Figures below are from the GATE 2027 official site and brochure.

At a glance
Conducted by IISc Bengaluru and seven IITs for NCB-GATE (IIT Madras organises GATE 2027)
Exam dates (announced) 6, 7, 13, 14, 20 and 21 February 2027; paper-wise schedule on the official site
Who can apply Students in the 3rd year or higher of a UG programme, and graduates; no age limit
Score validity 3 years from the date of announcement of results

Official sources

Exam pattern

Section Questions Marks
General Aptitude: 5 × 1 mark + 5 × 2 marks (common to all papers) 10 15
DA subject sections (the 7 syllabus sections below; DA has no separate Engineering Mathematics section) 55 85
Total 65 100
  • Question types: MCQ (one correct), MSQ (one or more correct, no partial credit), NAT (numerical).
  • Negative marking for MCQ only: −1/3 (1-mark) and −2/3 (2-mark). MSQ and NAT have none.
  • 3 hours, computer-based, in English. Only the on-screen virtual scientific calculator is allowed.
  • A candidate may appear in one or up to two papers from the approved two-paper combination matrix.

Syllabus

The 7 sections of the official DA syllabus PDF (the PDF is the authority):

  1. Probability and Statistics: counting, probability axioms, joint/conditional probability, Bayes theorem, random variables and standard distributions, central limit theorem, confidence intervals, z/t/chi-squared tests.
  2. Linear Algebra: vector spaces, matrices and their properties, systems of linear equations, eigenvalues and eigenvectors, rank, projections, LU and singular value decomposition.
  3. Calculus and Optimization: single-variable functions, limits, continuity, differentiability, Taylor series, maxima and minima.
  4. Programming, Data Structures and Algorithms: Python; stacks, queues, linked lists, trees, hash tables; searching, sorting, divide and conquer; basic graph algorithms.
  5. Database Management and Warehousing: ER and relational models, SQL, normal forms, indexing, data transformation; warehouse modelling and multidimensional schemas.
  6. Machine Learning: supervised (linear/logistic regression, k-NN, naive Bayes, SVM, decision trees, bias–variance trade-off, cross-validation, MLPs) and unsupervised (k-means, hierarchical clustering, PCA).
  7. AI: search (informed, uninformed, adversarial), propositional and predicate logic, reasoning under uncertainty (exact inference by variable elimination, approximate inference by sampling).

How to prepare (free, in order)

  1. Probability, statistics and linear algebra carry the paper: master them first.
  2. Solve the official DA papers of 2024–2026 with answer keys from the downloads page above, timed.
  3. Machine learning and programming need hands-on practice: implement every algorithm you study in Python.

Free resources