GATE 2026 Data Science And Artificial Intelligence Syllabus: Check Dates, Pattern, Books, and More

Probability and Statistics

Counting (permutation and combination) | probability axioms | sample space | Bayes Theorem | Discrete Random Variables and Probability Mass Theorems | Binomial Distribution | Central Limit Theorem | Chi-Squared Test

Linear Algebra

Vector space | Subspaces | Projection Matrix | Quadratic Forms | Orthogonal Matrix | Eigen Values and Eigen Vectors | 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 | Search Algorithms: Linear Search and Binary Search | Basic Sorting Algorithms | Basic Data Structures: Stacks, Queues, Linked Lists, Trees, Hash Tables | 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, File Organization, Indexing, Data Types, and Data Transformation, such as normalisation, discretisation, sampling, compression, etc. | Data Warehouse Modelling: Schema for Multidimensional Data Models, Concept Hierarchies, Measures: Categorisation and Computations

Machine Learning

Supervised Learning: Regression and Classification Problems | Simple Linear Regression | Support Vector Machine | Multi-Layer Perception | Feed-Forward Neural Network | Unsupervised Learning

Artificial Intelligence (AI)

Search: Informed and Uniformed | Adversarial: Logic, Propositional, Predicate | Reasoning under Uncertainty Topics - Conditional Independence Representation, Exact Inference Through Variable Elimination, and Approximate Inference Through Sampling

GKP GATE 2025: 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