GATE 2026 Data Science & Artificial Intelligence Syllabus (Out) - Download GATE DA Syllabus PDF, Important Topics/Chapters, Section Wise Weightage

Calculus and Optimization

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

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.

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

Machine Learning

(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.

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

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.

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.

Verbal Aptitude

Vocabulary: Words, Idioms, and Phrases in context Reading and comprehension Narrative sequencing, Basic English grammar: tenses, articles, adjectives, prepositions, conjunctions, verb-noun agreement, and other parts of speech. Basic

Quantitative Aptitude

Data interpretation: data graphs (bar graphs, pie charts, and other graphs representing data), 2- and 3-dimensional plots, maps, and tables. Numerical computation and estimation: ratios, percentages, powers, exponents and logarithms, permutations and combinations, and series. Mensuration and geometry, Elementary statistics and probability. 

Analytical Aptitude

Logic: deduction and induction, Analogy, Numerical relations and reasoning

Spatial Aptitude

Transformation of shapes: translation, rotation, scaling, mirroring, assembling, and grouping Paper folding, cutting, and patterns in 2 and 3 dimensions

Programming, Data Structures and Algorithms

• Search Algorithms
• Programming in Python
• Basic Graph Algorithms
• Basic Sorting Algorithms
• Divide and Conquer

Linear Algebra

• Matrices
• Eigenvalues and Eigenvectors
• Determinant
• Vector space, subspaces
• Linear dependence and independence of vectors
• LU decomposition, singular value decomposition

Probability and Statistics

• Bayes Theorem
• Variance, mean, median, mode and standard deviation, correlation, and covariance
• Bernoulli, Binomial Distribution
• t-distribution, chi-squared distributions
• Permutations and Combinations
• Independent events, mutually exclusive events
• z-test, t-test, chi-squared test

Calculus and Optimization

• Functions of a single variable
• Maxima and Minima
• Limit, continuity, and differentiability
• Taylor Series

Artificial Intelligence

• Logic, propositional, predicate
• Reasoning under uncertainty
• Informed, uninformed, adversarial

Machine Learning

• Regression and classification problems
• Top-down
• Cross-validation Methods
• Clustering Algorithms
• k-medoid
• Bottom-up: single-linkage, multiple linkage

Database Management and Warehousing

• Data Transformation
• Data Warehouse Modelling
• ER-model
• Relational Model

Calculus and Optimization

10-12 marks

Probability and Statistics

08-10 marks

General Aptitude

15 marks

Linear Algebra

10-12 marks

Database Management and Warehousing

10-12 marks

Programming, Data Structures, and Algorithms

12-15 marks

Artificial Intelligence (AI)

15-18 marks

Machine Learning

7-8 marks

Database Management Systems

Raghu Ramakrishnan and Johannes Gehrke

Introduction to Linear Algebra

Gilbert Strang

Introduction to Probability

Dimitri P. Bertsekas & John N. Tsitsiklis

Learning Python

Mark Lutz

Computer Vision: Algorithms and Applications

Richard Szeliski

Machine Learning for Beginners

Chris Sebastian

Artificial Intelligence: A Modern Approach

Stuart Russell and Peter Norvig

Pattern Recognition and Machine Learning

Christopher M. Bishop

Deep Learning

Ian Goodfellow, Yoshua Bengio, and Aaron Courville

Elements of Statistical Learning

Trevor Hastie, Robert Tibshirani, and Jerome Friedman

Speech and Language Processing

Daniel Jurafsky and James H. Martin

Introduction to the Theory of Computation

Michael Sipser

Python Machine Learning

Sebastian Raschka and Vahid Mirjalili

Bayesian Reasoning and Machine Learning

David Barber

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow

Aurélien Géron