GATE Syllabus

GATE 2025 Data Science & Artificial Intelligence syllabus

GATE 2025 Data Science & Artificial Intelligence syllabus details the latest GATE syllabus for the subject released by the official gate organizing institute IIT Roorkee 2025.

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GATE 2025 Data Science & Artificial Intelligence syllabus (DA)

Probability and Statistics Counting (permutations 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 function, uniform, Bernoulli, binomial distribution, Continuous random variables and probability distribution function, uniform, exponential, Poisson, normal, standard normal, t-distribution, chi-squared distributors’, cumulative distribution function, Conditional PDF, Central limit theorem, confidence interval, z-test, t-test, chi-squared test.

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, projectors, LU decomposition, singular value decomposition.

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

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 soaring algorithms: selection sort, bubble sort, and insertion sort; divide and conquer: mergesort, quicksort; introduction to graph theory; basic graph algorithms: traversals and shortest path.

Database Management and Warehousing ER-model, relational model: relational algebra, tuple calculus, SQL, integrity constraints, normal form, file organizing, indexing, data types, data transformation such as normalizing, discretization, sampling, compression; data warehouse modeling: schema for multidimensional data models, concept hierarchies, measures: categorizing and computations.

Machine Learning (i) Supervised Learning: regression and classicising problems, simple linear regression, multiple linear regression, ridge regression, logistic regression, k-nearest neighbour, naïve 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; (ii) Unsupervised Learning: clustering algorithms, k-means/k-medoid, hierarchical clustering, top-down, bottom-up: single-linkage, multiple linkages, dimensionality reduction, principal component analysis.

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.

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