GATE DA syllabus 2026

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, t-distribution, chi-squared distributions, cumulative distribution function, Conditional PDF, Central limit theorem, confidence interval, z-test, t-test, chi-squared test.

• 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.

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

• 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.

• 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 modeling: schema for multidimensional data models, concept hierarchies, measures: categorization and computations.

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

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

tenses, articles, adjectives, prepositions, conjunctions, verb-noun agreement, and other parts of speech

words, idioms, and phrases in context.

data graphs (bar graphs, pie charts, and other graphs representing data), 2- and 3-dimensional plots, maps, and tables

ratios, percentages, powers, exponents and logarithms, permutations and combinations, and series Mensuration and geometry Elementary statistics and probability.

deduction and induction, Analogy, Numerical relations and reasoning.

translation, rotation, scale, mirroring, assembling, and grouping paper folding, cutting, and patterns in 2 and 3 dimensions.