GATE DS & AI Syllabus 2027 OUT: Download PDF, Subject-Wise Weightage

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, tdistribution, chi-squared distributions, 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, projections, LU decomposition, singular value decomposition.

Calculus and Optimization

Functions 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 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.

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.

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.

Artificial Intelligence (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.

GKP GATE Data Science and Artificial Intelligence - Guide (solved papers)

GK Publications

Mathematical Statistics and Data Analysis (3rd Edition)

John A. Rice 

Introduction to Probability Models

Sheldon M.Ross

Data Structures and Algorithms in Python

Roberto Tamassia, Micheal H, Goldwasser, Michael T.Goodrich

Database Management Systems

Raghu Ramakrishnan

Data Mining and Data Warehousing

Prateek Bhatia

Artificial Intelligence by Example

Denis Rothman

Artificial Intelligence Basics

Tom Taulli

Machine Learning for Beginners

Chris Sebastian

Fundamentals of Database Systems

Navathe Shamkant