GATE 2025 Exam Details: Registration, DA Paper Syllabus, and Popular Papers

The Indian Institute of Technology (IIT) Roorkee has launched the official GATE 2025 website. However, the GATE 2025 information brochure has yet to be released by authorities. The GATE 2025 Brochure contains all relevant information about the GATE 2025 exam.

GATE 2025 registration will begin soon, likely in the last week of August 2024. Before applying for the GATE 2025 exam, candidates should carefully review the GATE 2025 syllabus. Data Science and Artificial Intelligence papers are becoming increasingly popular among GATE candidates. Even though the DA paper was added for the first time to the GATE examination in 2024, 52493 candidates registered for it.

GATE 2025 DA Syllabus

There are 7 sections of the GATE 2025 DA paper namely

1. Probability and Statistics
2. Linear Algebra
3. Calculus and Optimization
4. Programming, Data Structures and Algorithms
5. Database Management and Warehousing
6. Machine Learning
7. Artificial Intelligence

Check the detailed section-wise GATE 2025 DA syllabus:

1. Probability and statistics: include 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, probability mass functions, uniform, Bernoulli, binomial distribution, continuous random variables, and probability distribution functions, uniform, exponential, Poisson, normal, standard normal, t-distribution, chi-squared distributions, and cumulative distribution function Conditional PDF, Central Limit Theorem, confidence interval, z-test, t-test, and chi-squared test.
   
   
2. 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, linear equation systems and solutions, Gaussian elimination, eigenvalues and eigenvectors, determinant, rank, nullity, projections, LU decomposition, and singular value decomposition.
   
   
3. Calculus and Optimization: Functions of a single variable, limit, continuity, and differentiability, Taylor series, maxima and minima, single variable optimisation.
   
   
4. Programming: Data Structures and Algorithms: Basic Python data structures include stacks, queues, linked lists, trees, and 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, including traversals and shortest path.
   
   
5. Database Management and Warehousing: The ER-model and relational model involve relational algebra, tuple calculus, SQL, integrity constraints, normal form, file organisation, indexing, data types, and data transformations like normalisation, discretization, sampling, and compression. Data warehouse modelling includes schema for multidimensional data models, concept hierarchies, and measures for categorization and computation.
   
   
6. Machine Learning: (i) 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-oneout (LOO) cross-validation, k-folds cross-validation, multi-layer perceptron, feed-forward neural network; (ii) Unsupervised learning: clustering algorithms, k-
   
   
7. Artificial Intelligence: Search: informed, uninformed, adversarial; logic, propositional, predicate; reasoning under uncertainty topics include conditional independence representation, exact inference via variable elimination, and approximate inference via sampling.