GATE DA Syllabus 2027

Last Updated : 21 Aug, 2026

GATE is a national-level examination conducted jointly by IISc and the IITs. For the year 2027, IIT Madras is set to conduct the GATE exam, as confirmed by their official notification. A valid GATE score can be used for admission to various Master's and doctoral programmes in eligible institutes, and some Public Sector Undertakings (PSUs) use GATE scores as part of their recruitment process. However, qualifying GATE does not guarantee admission, scholarship, or employment.

GATE Data Science and Artificial Intelligence Syllabus PDF 2027

IIT Madras has released the official syllabus for the GATE DA 2027 exam, giving candidates a clear idea of the topics and concepts they need to prepare.

Download the latest GATE Data Science and Artificial Intelligence Syllabus PDF here "GATE Data Science and Artificial Intelligence Syllabus"

GATE Data Science and Artificial Intelligence (DA) Subjects

A variety of topics are covered in the GATE Data Science and Artificial Intelligence courses that are crucial for comprehending and succeeding in the discipline. The following are some of the main GATE data science and artificial intelligence topics covered in the curriculum:

Probability and Statistics  

Covers counting methods, probability theory, random variables, key probability distributions, and statistical inference techniques such as hypothesis testing and confidence intervals.

Counting

Covers basic counting principles, permutations and combinations (with and without repetition), and techniques such as the pigeonhole principle and inclusion–exclusion principle.

Basics of Probability

Covers probability axioms, sample space, events and their types, and fundamental rules used to model and analyze uncertain outcomes.

Conditional Probability

Covers conditional probability, the law of total probability, and Bayes’ theorem for analyzing dependent events and updating probabilities based on given information.

Descriptive Statistics

Covers measures of central tendency and dispersion, including mean, median, mode, variance, standard deviation, correlation, and covariance for summarizing data.

Random Variable

Covers discrete and continuous random variables, probability mass and density functions, expectation, variance, and conditional expectation.

Joint Random Variable

Represents two or more random variables together, describing their joint, marginal, and conditional probabilities and relationships.

Probability Distributions

Describe how probabilities are assigned to values of a random variable, including discrete and continuous distributions, and summarize the likelihood of outcomes.

Inferential Statistics

Involves making predictions or generalizations about a population based on a sample, using techniques like estimation, and hypothesis testing.

Refer to Last Minute Notes on Probability and Statistics for quick revision.

Linear Algebra

Covers vector spaces, matrices and determinants, systems of linear equations, eigenvalues and eigenvectors, and special matrices with decompositions.

Basics of Vector Spaces

Focuses on vector spaces, subspaces, linear dependence and independence, spanning sets, and basis.

Matrices and Determinants

Deals with matrix types, operations, inverses, determinants, and their role in solving linear equations.

Systems of Linear Equations

Studies methods to solve homogeneous and non-homogeneous linear equations, including LU decomposition.

Eigenvalues and Eigenvectors

Involves finding scalar values and corresponding vectors that satisfy linear transformations of matrices.

Special Matrices and Decompositions

Covers partition and projection matrices, quadratic forms, and singular value decomposition.

Refer to Last Minute Notes on Linear Algebra for quick revision.

Calculus and Optimization

Studies limits, derivatives, and integrals to analyze functions and their behavior.
Focuses on finding maxima, minima, and optimal solutions in mathematical and real-world problems, applying techniques under constraints.

Introduction to Calculus

Studies change and motion through concepts of limits, derivatives, and integrals.

Continuity and Differentiability

Examines whether functions are smooth and unbroken, and how they change at each point.

Series and Function Behavior

Analyzes sequences, series, and the overall behavior of functions, including convergence and divergence.

Extremes and Optimization

Focuses on finding maximum and minimum values of functions and applying them to solve practical optimization problems.

Refer to Last Minute Notes on Calculus for quick revision.

Python Programming and Data Structures

Covers Python syntax, control structures, functions, and data structures like lists, stacks, queues, and trees for efficient data handling.

Python Fundamentals

Introduces basic Python concepts, including variables, data types, operators, and simple input/output operations.

Python Data Types

Covers different types of data in Python, such as integers, floats, strings, lists, tuples, sets, and dictionaries.

Python Conditional Statements and Loops

Focuses on decision-making using if, elif, else, and repeating tasks using for and while loops.

Python Functions

Introduces defining and using reusable blocks of code, including parameters, return values, and scope.

Python OOPs Concepts

Covers object-oriented programming in Python, including classes, objects, inheritance, encapsulation, and polymorphism.

Python Collections

Introduces built-in data structures in Python, such as lists, tuples, sets, and dictionaries, for efficient data storage and manipulation.

Data Structures

Studies ways to organize, store, and manage data efficiently, including arrays, linked lists, stacks, queues, trees, and graphs.

Algorithms

Covers step-by-step procedures for solving problems efficiently, including searching, sorting, and optimization techniques.

Asymptotic Analysis of Algorithms

Evaluates the efficiency of algorithms by analyzing their growth rates and estimating time and space complexity for large inputs.

Recurrence Relations

Studies equations that define sequences recursively, expressing each term in terms of previous terms.

Divide and Conquer

Solves problems by breaking them into smaller subproblems, solving each recursively, and combining the results.

Greedy Techniques

Solves optimization problems by making the best local choice at each step with the hope of finding a global optimum.

Graph

Studies structures consisting of nodes (vertices) and edges to model relationships and solve problems like traversal, shortest paths, and connectivity.

Dynamic Programming

Solves complex problems by breaking them into overlapping subproblems, storing solutions, and combining them to find the optimal result.

Searching, Sorting, Technique-based Theorem and Hashing

Covers methods to locate and organize data efficiently, including linear and binary search, various sorting algorithms, algorithmic techniques, and hash-based data storage.

Refer to Last Minute Notes on Algorithms for quick revision.

Database Management System

Focuses on storing, organizing, and managing data efficiently, covering concepts like tables, queries, normalization, and transaction management.

Introduction

Provides an overview of the subject, its purpose, key concepts, and basic applications.

ER-Model

Represents data and their relationships using entities, attributes, and relationships to design a database schema.

Relational Model (Relational algebra, Tuple Calculus)

Defines data in tables (relations) and uses operations and queries to manipulate and retrieve information.

Database Design (Integrity Constraints, Normal Forms)

Focuses on structuring databases efficiently while ensuring data accuracy, consistency, and eliminating redundancy.

Structured Query Languages (SQL)

Used to create, manage, and query relational databases, including operations like data retrieval, insertion, updating, and deletion.

Transactions and Concurrency Control

Manages multiple database operations to ensure data consistency, integrity, and isolation in concurrent environments.

File Structures (Sequential files, Indexing, B and B+ trees)

Covers methods for storing and accessing data efficiently using sequential files, indexes, and tree-based structures like B and B+ trees.

Refer to Last Minute Notes on DBMS for quick revision.

Data Warehousing

Involves collecting, storing, and managing large volumes of historical data for analysis, reporting, and decision-making.

Data Warehousing Basics

Introduces fundamental concepts of data warehousing, including data integration, storage, and support for analytical processing and decision-making.

OLAP Technology

Focuses on online analytical processing techniques for multidimensional data analysis, enabling fast querying, aggregation, and reporting.

Data Transformation

Deals with modifying, cleaning, and restructuring data to make it suitable for analysis and further processing.

Data Warehousing Concepts and Models

Explains core warehousing concepts and data models such as star and snowflake schemas used for efficient analytical processing.

Schema for Multidimensional Data Models

Defines the structure of multidimensional databases, including fact and dimension tables, to support efficient analysis and reporting.

Concept Hierarchies and Measures

Describes levels of data abstraction and numerical metrics used for aggregation and analysis in multidimensional models.

Refer to Last Minute Notes on Data Warehousing for quick revision.

Machine Learning

Studies algorithms that enable systems to learn from data, identify patterns, and make predictions or decisions without explicit programming.

Machine Learning Basics

Introduces fundamental concepts of learning from data, including supervised and unsupervised learning, features, and model evaluation.

Supervised Learning

Involves training models on labeled data to learn relationships and make predictions or classifications.

Unsupervised Learning

Focuses on discovering patterns, structures, and relationships in unlabeled data.

Refer to Last Minute Notes on Machine Learning for quick revision.

Artificial Intelligence

Studies the creation of intelligent systems that can perform tasks such as reasoning, learning, problem-solving, and decision-making.

Search in Artificial Intelligence

Deals with techniques to explore problem spaces systematically in order to find solutions or optimal paths.

Uninformed Search (Blind Search)

Informed Search (Heuristic Search)

Adversarial Search

Logic

Studies formal rules of reasoning and inference used to represent knowledge and derive valid conclusions.

Reasoning Under Uncertainty

Focuses on making decisions and inferences when information is incomplete or probabilistic.

Refer to Last Minute Notes on Artificial Intelligence for quick revision.

General Aptitude

Assesses logical reasoning, quantitative ability, and verbal skills required for problem-solving and decision-making.

Verbal Aptitude

Measures proficiency in understanding, interpreting, and using language effectively, including grammar, vocabulary, and comprehension.

Quantitative Aptitude

Evaluates numerical and mathematical ability, including arithmetic, algebra, geometry, and data interpretation skills.

Analytical Aptitude

Tests logical reasoning, problem-solving, and the ability to analyze and interpret information effectively.

Spatial Aptitude

Assesses the ability to visualize, manipulate, and reason about objects and their relationships in space.

As you prepare for the GATE DA exam, mastering these core topics and strategies will be crucial for success. Stay consistent with your study plan and refer to the Last Minute Notes for a quick revision closer to the exam date.

Comment

Explore