GATE DS & AI Syllabus 2027: Subject-Wise Topics, Exam Pattern & PDF

The GATE DS & AI Syllabus has been released by IIT Madras for GATE 2027. If you are preparing for the Data Science and Artificial Intelligence (DA) paper, you should know every topic before starting your preparation. The syllabus covers mathematics, programming, databases, machine learning, and artificial intelligence. It also includes statistical concepts that are useful for data analysis.

The GATE DS & AI Syllabus is divided into seven major sections. Along with the syllabus, you should also understand the exam pattern and marks distribution. A clear understanding of the syllabus helps you prepare in the right direction and avoid missing important topics.

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GATE DS & AI Syllabus 2027 Highlights

The table below provides an overview of the GATE DA Syllabus 2027. It includes important details such as the conducting institute, paper code, exam mode, marks distribution, and the number of sections covered in the syllabus. 

GATE DS & AI Syllabus 2027 Subject-Wise Topics

The GATE DS & AI Syllabus covers seven sections. Each section tests your understanding of mathematics, programming, databases, machine learning, and artificial intelligence.

Probability and Statistics

This section builds the foundation for data science concepts.

Topics include:

• Permutations and combinations

• Probability axioms

• Sample space

• Events

• Independent and mutually exclusive events

• Marginal, conditional, and joint probability

• Bayes Theorem

• Conditional expectation

• Variance

• Mean

• Median

• Mode

• Standard deviation

• Correlation

• Covariance

• Random variables

• Discrete random variables

• Probability mass function

• Bernoulli distribution

• Binomial distribution

• Uniform distribution

• Exponential distribution

• Poisson distribution

• Normal distribution

• Standard normal distribution

• t-distribution

• Chi-squared distribution

• Cumulative distribution function

• Conditional probability density function

• Central Limit Theorem

• Confidence interval

• z-test

• t-test

• Chi-squared test

Linear Algebra

This section focuses on vectors, matrices, and matrix operations.

Topics include:

• Vector space

• Subspaces

• Linear dependence

• Linear independence

• Matrices

• Projection matrix

• Orthogonal matrix

• Idempotent matrix

• Partition matrix

• Matrix properties

• Quadratic forms

• Systems of linear equations

• Gaussian elimination

• Eigenvalues

• Eigenvectors

• Determinant

• Rank

• Nullity

• Projections

• LU decomposition

• Singular Value Decomposition (SVD)

Calculus and Optimization

This section tests basic calculus and optimization concepts.

Topics include:

• Functions of a single variable

• Limits

• Continuity

• Differentiability

• Taylor series

• Maxima

• Minima

• Optimization involving a single variable

Programming, Data Structures and Algorithms

Programming and problem-solving form an important part of the syllabus.

Topics include:

• Python programming

• Stacks

• Queues

• Linked lists

• Trees

• Hash tables

• Linear search

• Binary search

• Selection sort

• Bubble sort

• Insertion sort

• Merge sort

• Quick sort

• Introduction to graph theory

• Graph traversal

• Shortest path algorithms

Database Management and Warehousing

This section covers database concepts and data storage methods.

Topics include:

• ER model

• Relational model

• Relational algebra

• Tuple calculus

• SQL

• Integrity constraints

• Normal forms

• File organization

• Indexing

• Data types

• Data normalization

• Discretization

• Sampling

• Compression

• Data warehouse modelling

• Multidimensional schema

• Concept hierarchies

• Measures

• Categorization

• Computation

Machine Learning

The GATE DS & AI Syllabus includes both supervised and unsupervised learning methods.

Supervised Learning

• Regression

• Classification

• 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

• Leave-One-Out (LOO) cross-validation

• k-fold cross-validation

• Multi-layer perceptron

• Feed-forward neural network

Unsupervised Learning

• Clustering

• k-means

• k-medoid

• Hierarchical clustering

• Top-down clustering

• Bottom-up clustering

• Single-linkage

• Multiple-linkage

• Dimensionality reduction

• Principal Component Analysis (PCA)

Artificial Intelligence

This section introduces the basic concepts of AI.

Topics include:

• Informed search

• Uninformed search

• Adversarial search

• Propositional logic

• Predicate logic

• Reasoning under uncertainty

• Conditional independence representation

• Exact inference through variable elimination

• Approximate inference through sampling

GATE DS & AI Syllabus 2027 PDF

The GATE DS & AI Syllabus 2027 PDF helps you access the complete syllabus in one place. You can download the official PDF released by IIT Madras and use it while planning your preparation. The PDF includes all seven sections of the Data Science and Artificial Intelligence (DA) paper along with the prescribed topics. Keeping the syllabus PDF with you makes it easier to track completed topics and revise important concepts before the examination.

Download GATE DS and AI Syllabus PDF

GATE DS & AI Syllabus 2027 Exam Pattern

The GATE DS & AI Syllabus should be prepared along with the exam pattern. This helps you understand the mark distribution.

Preparation Tips for GATE DS & AI Syllabus 2027

The GATE DS & AI Syllabus is broad. A planned preparation strategy can help you cover every section.

• Read the complete syllabus before starting your preparation.

• Give equal attention to mathematics, programming, and machine learning.

• Practice Python programming regularly.

• Revise probability and linear algebra concepts frequently.

• Solve previous years' GATE questions.

• Attempt mock tests to improve speed and accuracy.

• Revise important formulas and algorithms every week.

The GATE DS & AI Syllabus for 2027 covers seven major sections that are important for the Data Science and Artificial Intelligence paper. You should complete every topic and revise regularly. A proper study plan, regular practice, and mock tests can help you prepare well for the examination.

Related Resources:

• GATE 2027 Subjects

• GATE 2027 Syllabus

• GATE 2027 Exam Pattern

• GATE 2027 Eligibility Criteria

• GATE 2027 Important Dates

• GATE 2027 Official Website

• GATE 2027 Exam Date