GATE · Branch-wise preparation

GATE Data Science and AI (DA): syllabus, who should take it and how to prepare

By the PrepMatic team · Updated · 8 min read

On this page
  1. The DA paper at a glance
  2. The GATE 2027 DA syllabus, section by section
  3. Should you take DA, CS or both?
  4. What to prioritise
  5. A six-month order of study
  6. Books and sources
  7. Practising GATE DA with PrepMatic
  8. Frequently asked questions
  9. Official sources

GATE DA (Data Science and Artificial Intelligence) is a 100-mark paper with 15 marks of General Aptitude and 85 marks across seven subject sections: Probability and Statistics, Linear Algebra, Calculus and Optimization, Programming with Data Structures and Algorithms, Database Management and Warehousing, Machine Learning and AI. It has no separate Engineering Mathematics block, because the maths topics are full sections of the paper. DA was introduced in GATE 2024, programming is in Python, and the syllabus was revised for GATE 2027.

The DA paper at a glance

PartMarksNotes
General Aptitude1510 questions, common to every paper
Subject sections857 sections, 55 questions
Total10065 questions in 3 hours

Compared with GATE CS, which has 13 marks of Engineering Mathematics and 72 of core subjects, DA puts the mathematics and the machine-learning content in front. The question types, negative marking and the on-screen calculator work as for every other paper; see the GATE exam pattern. As an illustration of scale, 91,764 candidates registered for DA in GATE 2026, 69,242 appeared and the General-category qualifying mark was 26.4 (official IIT Guwahati table). Qualifying marks change every year and are not predictable.

The GATE 2027 DA syllabus, section by section

Condensed from the official 2027 DA syllabus on the IIT Madras site.

SectionWhat the 2027 syllabus lists
1. Probability and StatisticsCounting (permutations and combinations), probability axioms, conditional, joint and marginal probability, Bayes' theorem, conditional expectation and variance, correlation and covariance; discrete distributions (uniform, Bernoulli, binomial, Poisson) and continuous distributions (uniform, exponential, normal, standard normal, t, chi-squared), CDF, conditional PDF; central limit theorem, confidence intervals, z-test, t-test, chi-squared test
2. Linear AlgebraVector spaces and subspaces, linear independence, matrices (projection, orthogonal, idempotent, partitioned), quadratic forms, linear systems and Gaussian elimination, eigenvalues and eigenvectors, determinant, rank, nullity, projections, LU decomposition, singular value decomposition
3. Calculus and OptimizationFunctions of a single variable, limits, continuity, differentiability, Taylor series, maxima and minima, optimization involving a single variable
4. Programming, Data Structures and AlgorithmsProgramming in Python; stacks, queues, linked lists, trees, hash tables; linear and binary search; selection, bubble and insertion sort; mergesort and quicksort; introduction to graph theory; graph traversals and shortest paths
5. Database Management and WarehousingER model; relational model, relational algebra, tuple calculus, SQL, integrity constraints, normal forms; file organisation, indexing; data types and data transformation (normalisation, discretisation, sampling, compression); data warehouse modelling (multidimensional schemas, concept hierarchies, measures)
6. Machine LearningSupervised: simple, multiple, ridge and logistic regression; k-nearest neighbour, naive Bayes, linear discriminant analysis, support vector machine, decision trees; bias-variance trade-off; leave-one-out and k-fold cross-validation; multi-layer perceptron and feed-forward networks. Unsupervised: k-means and k-medoid, hierarchical clustering, dimensionality reduction, principal component analysis
7. AISearch (informed, uninformed, adversarial); propositional and predicate logic; reasoning under uncertainty: conditional independence, exact inference by variable elimination, approximate inference by sampling

We have not compared the 2027 list with earlier years, so don't assume which topics are new. The official DA syllabus PDF should be your checklist.

Should you take DA, CS or both?

QuestionGATE DAGATE CS
Programming languagePythonC
MathematicsThree full sections: probability and statistics, linear algebra, calculus and optimization13-mark Engineering Mathematics block, including discrete maths
Machine learning and AITwo full sectionsNot in the syllabus
Operating systems, networks, compilers, TOC, computer organization, digital logicNot in the syllabusCore subjects
DatabasesOne section, plus data warehousingOne section

Pick DA if you want M.Tech or MS programmes in data science, machine learning or AI and you are comfortable with probability and linear algebra. Pick CS if you want broad computer-science programmes or roles built on systems subjects. Many aspirants take both. DA's allowed second papers are CS, EC, EE, MA, ME, PH, RA, ST and XE, and both papers go in one application. The CS preparation guide shows what the second paper adds.

Two checks before you commit. First, choosing a paper outside your own discipline is allowed, but each admitting institute decides whether it accepts that paper, so read the admission page of the programmes you want. Second, jobs: among the organisations on the GATE 2026 announcement list, only NIC (the National Informatics Centre, a government body) named DA, alongside CS and EC. Others such as EIL, NHAI, GAIL, NPCIL and POWERGRID named other papers. Hiring changes every year, so check current notices.

What to prioritise

GATE publishes no official weightage for the seven sections, and DA has only three real papers so far (2024 to 2026), so past-paper patterns are thin. Treat every section as examinable: 85 marks are spread over only seven sections, so skipping Calculus and Optimization or AI would be a poor trade. Give the three maths sections and Machine Learning the most time, because the rest of the paper builds on them.

  • Probability and Statistics is the base for Machine Learning and AI. Know each distribution's mean, variance and typical use, and practise hypothesis tests until choosing between z, t and chi-squared is automatic.
  • Linear Algebra matters twice: directly, and inside PCA and SVD. Practise rank, eigenvalues, projections and LU by hand.
  • Calculus and Optimization is a short section. Single-variable only, so it is quick to finish.
  • Programming, Data Structures and Algorithms is tested in Python. Trace code by hand and know the time complexity of each sorting and search method on the list.
  • Database Management and Warehousing mixes SQL and relational algebra with data-warehouse vocabulary that students from a CS background often skip.
  • Machine Learning questions combine concept and small calculations: running k-means for a step, computing a regression fit, reading a bias-variance situation.
  • AI is search, logic and probabilistic inference. It is compact and rewards practice on small worked examples.

A worked probability example

A disease affects 1% of a population. A test is positive for 90% of people who have the disease and for 5% of people who do not. If someone tests positive, what is the probability they have the disease?

P(disease and positive) = 0.01 × 0.90 = 0.009. P(no disease and positive) = 0.99 × 0.05 = 0.0495. Total P(positive) = 0.0585. So P(disease given positive) = 0.009 / 0.0585 = 0.154, about 15.4%. A NAT version of this would ask for the answer to three decimal places.

A six-month order of study

The order front-loads mathematics because Machine Learning and AI depend on it.

MonthFocus
1Probability and Statistics: counting, conditional probability, distributions, CLT, hypothesis tests
2Linear Algebra, then Calculus and Optimization
3Python, data structures and algorithms; then Databases and warehousing
4Machine Learning, with maths revision as needed
5AI; General Aptitude every day (see GATE General Aptitude); topic-wise questions for every section
6Full-length mocks, error-log revision

If you have 12 months, stretch each block and add a second pass; the GATE preparation strategy has the general plans.

Books and sources

GATE recommends no books. These are widely used references.

AreaCommonly used reference
ProbabilityA First Course in Probability, Sheldon Ross
StatisticsProbability and Statistics for Engineering and the Sciences, Jay Devore
Linear algebraIntroduction to Linear Algebra, Gilbert Strang
CalculusThomas' Calculus
PythonThink Python, Allen Downey
AlgorithmsIntroduction to Algorithms (CLRS), Cormen, Leiserson, Rivest and Stein
Databases and warehousingDatabase System Concepts, Silberschatz, Korth and Sudarshan; Data Mining: Concepts and Techniques, Han, Kamber and Pei
Machine learningAn Introduction to Statistical Learning, James, Witten, Hastie and Tibshirani; Mathematics for Machine Learning, Deisenroth, Faisal and Ong
AIArtificial Intelligence: A Modern Approach, Russell and Norvig

For practice, use the official DA papers released since 2024 on the GATE downloads page; the previous year papers guide explains how. CS papers are a useful extra bank for probability, linear algebra, databases, searching, sorting and graphs, but check each question against the DA syllabus because CS code questions use C. The maths itself is covered in the Engineering Mathematics guide, and the whole exam is in the GATE hub.

Practising GATE DA with PrepMatic

PrepMatic's free GATE assessment is about 15 multiple-choice questions, untimed, and shows your weakest and strongest subjects with a projected score out of the paper's maximum. Since the DA paper spans seven sections, that subject-level view helps you decide where the first month goes. You can then follow a study plan built from your exam date and daily hours, and take topic tests and mocks focused on your weak subjects. The questions are AI-generated, not official papers, so keep the official DA papers for your final mock phase.

Frequently asked questions

Which programming language is used in GATE DA?

Python. The Programming, Data Structures and Algorithms section of the 2027 syllabus is written for Python, while GATE CS uses C.

Is there Engineering Mathematics in GATE DA?

There is no separate Engineering Mathematics block. Probability and Statistics, Linear Algebra and Calculus and Optimization are three of the seven subject sections, so the maths is examined as subject content, and Machine Learning and AI build on it.

When was GATE DA introduced?

GATE DA was first held in GATE 2024. The paper therefore has only a few official past papers, and the 2027 syllabus is a revised one.

Can I take GATE DA and CS together?

Yes, DA and CS are on each other's allowed lists, and both are applied for in one application. You sit one assigned session of each paper. DA cannot be combined with papers outside its list of CS, EC, EE, MA, ME, PH, RA, ST and XE.

Can a non-CS graduate take GATE DA?

Yes. Anyone in the third year or above of an eligible degree, or who has completed one, may apply, and choosing a paper outside your discipline is allowed. Each admitting institute decides whether it accepts that paper. See the GATE eligibility page.

Official sources

Check the latest information on the official site before relying on any detail here.

Turn the GATE syllabus into a plan you can finish

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