GATE DA Syllabus 2026 PDF (Out) - Check Data Science and AI Important 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, t-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 optimization

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 organisation, indexing, data types, data transformation such as normalisation, discretization, sampling, compression
• Data warehouse modelling: schema for multidimensional data models, concept hierarchies, measures: categorization and computations.

GATE DA syllabus for 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

GATE DA syllabus for 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-one-out (LOO) cross-validation, k-folds cross-validation, mulo-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.

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.

General Aptitude (GA)

15

Subject marks

85

Total

100

Artificial Intelligence: A Modern Approach

Textbook by Peter Norvig and Stuart J. Russell

‘Deep Learning’

by Ian Goodfellow, Yoshua Benjio, Aaron Courville

Introduction to Data Science: Practical Approach with R and Python

B. Uma Maheswari (Author), R. Sujatha (Author)

Data Science for Dummies

Lillian Pierson (Author), Jake Porway (Foreword)

Data Science from Scratch: First Principles with Python

Joel Grus