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    GATE 2027 Data Science and Artificial Intelligence Syllabus Out - Check DA Important Topics

    GATE 2027 Data Science and Artificial Intelligence Syllabus Out - Check DA Important Topics

    Simran KashyapUpdated on 04 Sep 2026, 12:48 PM IST

    GATE 2027 Data Science and Artificial Intelligence syllabus: Candidates looking for the GATE Data Science and Artificial Intelligence syllabus 2027 can refer to this article. The IIT Madras has published the GATE 2027 DS and AI syllabus on the official website, gate2027.iitm.ac.in. Candidates are advised to look at the GATE exam pattern along with the GATE syllabus. Candidates are advised to look at the GATE exam pattern along with the syllabus. The GATE 2027 question paper will be prepared based on the syllabus. The authority will conduct the GATE 2027 exam on February 6, 7, 13, 14, 20 & 21, 2027. Read the article for GATE 2027 DS and AI syllabus.
    Download GATE DA Syllabus PDF

    This Story also Contains

    1. GATE Data Science and Artificial Intelligence Syllabus 2027
    2. GATE 2027 Data Science and Artificial Intelligence Topic-Wise Weightage
    3. GATE Preparation Tips 2027
    GATE 2027 Data Science and Artificial Intelligence Syllabus Out - Check DA Important Topics
    GATE 2027 Data Science and Artificial Intelligence Syllabus

    GATE Data Science and Artificial Intelligence Syllabus 2027

    IIT Madras has published the GATE syllabus 2027 for DS and AI online. The GATE 2027 DA syllabus has been uploaded as PDF. The syllabus includes topics such as Probability, Statistics, Linear Algebra, Algorithms, Programming, Data Structures, Database Management Systems, Data Warehousing, Machine Learning, and Artificial Intelligence. Candidates can check the detailed syllabus for Data Science and Artificial Intelligence syllabus from the table below:

    GATE 2027 Syllabus Data Science and Artificial Intelligence

    Subject

    Topics

    GATE DA syllabus for Probability and Statistics

    Counting (permutation and combinations), probability axioms, Sample space, events, independent events, mutually exclusive events, marginal, conditional and joint probability, Bayes Theorem, conditional expectation and variance, mean, median, mode and standard deviation, correlation, and covariance, random variables, discrete random variables and probability mass functions, uniform, Bernoulli, binomial distribution, Continuous random variables and probability distribution function, uniform, exponential, Poisson, normal, standard normal, distribution, chi-squared distributions, cumulative distribution function, Conditional PDF, Central limit theorem, confidence interval, z-test, t-test, chi-squared test.

    GATE DA syllabus for Linear Algebra

    Vector space, subspaces, linear dependence and independence of vectors, matrices, projection matrix, orthogonal matrix, idempotent matrix, partition matrix and their properties, quadratic forms, systems of linear equations and solutions; Gaussian elimination, eigenvalues and eigenvectors, determinant, rank, nullity, projections, LU decomposition, singular value decomposition.

    GATE DA syllabus for Calculus and Optimisation

    Functions of a single variable, limit, continuity and differentiability, Taylor series, maxima and minima, optimization involving a single variable.

    GATE DA syllabus for Database Management and Warehousing

    ER-model, relational model: relational algebra, tuple calculus, SQL, integrity constraints, normal form, file organization, indexing, data types, data transformation such as normalization, discretization, sampling, compression; data warehouse modelling: schema for multidimensional data models, concept hierarchies, measures: categorization and computations

    Programming, Data Structures and Algorithms

    Programming in Python, basic data structures: stacks, queues, linked lists, trees, hash tables; Search algorithms: linear search and binary search, basic sorting algorithms: selection sort, bubble sort and insertion sort; divide and conquer: mergesort, quicksort; introduction to graph theory; basic graph algorithms: traversals and shortest path.

    Machine Learning

    Supervised Learning: regression and classification problems, simple linear regression, multiple linear regression, ridge regression, logistic regression, k-nearest neighbour, naive Bayes classifier, linear discriminant analysis, support vector machine, decision trees, bias-variance trade-off, cross-validation methods such as leave-one-out (LOO) cross-validation, k-folds cross-validation, multi-layer perceptron, feed-forward neural network; Unsupervised Learning: clustering algorithms, k-means/k-medoid, hierarchical clustering, top-down, bottom-up: single-linkage, multiple-linkage, dimensionality reduction, principal component analysis.

    GATE DA syllabus for AI

    Search: informed, uninformed, adversarial; logic, propositional, predicate; reasoning under uncertainty topics - conditional independence representation, exact inference through variable elimination, and approximate inference through sampling.

    GATE 2027 Data Science and Artificial Intelligence Topic-Wise Weightage

    Candidates can check the GATE DS and AI topic-wise weightage from the table. Candidates should prepare accordingly based on the weightage of their topics. It will help in good preparation of candidates.

    Topic Name

    Number of Questions

    Total Marks

    General Aptitude

    10

    15

    Probability and Statistics

    10

    16

    Linear Algebra

    6

    10

    Calculus and Optimization

    5

    8

    Programming, Data Structures, and Algorithms

    13

    21

    Database Management and Warehousing

    6

    8

    Machine Learning

    8

    11

    Artificial Intelligence (AI)

    7

    11

    Total

    65

    100

    GATE Preparation Tips 2027

    Below are the GATE 2027 preparation tips that candidates should follow while preparing for the exam:

    • Start as early as possible: Candidates need to start as early as possible for better preparation. Staring early will help with having enough time for preparing a schedule, checking the GATE 2027 syllabus, and understanding the topics.

    • Prepare a study schedule: Candidates need to prepare a proper study schedule with a time table. The schedule should be prepared based on the strengths and weaknesses of the candidate.

    • Go for the reference materials: you should go for good reference materials such as undergraduate books, good books, focus on high weightage topics, and others. Start with a basic and wide spectrum of knowledge to have a good knowledge of the base topics.

    • Keep revising: Students should keep revising all the past topics they learned. Revision will help them to keep their minds fresh about the learned topics.

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