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

Updated By Lipi on 11 Aug, 2025 23:55

Registration Starts On September 01, 2025

The GATE 2026 syllabus is released separately for all 30 papers by IIT Guwahati. The GATE syllabus 2026 includes subjects like General Aptitude, Engineering Mathematics, and Core Engineering specialization.

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GATE 2026 Syllabus for DA

GATE Data Science and Artificial Intelligence syllabus 2026 has been released by IIT Guwahati. The GATE syllabus for DA includes chapters such as Calculus and Optimization, Linear Algebra, Probability and Statistics, Database Management and Warehousing, Data Structures and Algorithms, etc. The paper on Data Science and Artificial Intelligence was first introduced in 2024. The syllabus for AI and DS includes 3 types of questions on the DA paper, ie, MSQs, MCQs, and NATs. This page includes a detailed description of the GATE syllabus 2026 for DA.

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GATE DA Syllabus 2026 Section Wise Topics

The GATE DA syllabus 2026 is yet to be released by IIT Guwahati. However, going by the previous year trends, the syllabus will include a total of 7 sections. Note that there is expected to be no change in the syllabus for 2026, therefore, you can freely refer to the previous year’s syllabus. You can check the detailed GATE Data Science and Artificial Intelligence syllabus for 2026 below:

Chapter

Topics

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.

GATE DA Syllabus 2026 for General Aptitude

A part of the GATE 2026 syllabus for DA is General Aptitude. The GATE General Aptitude section is common for all the GATE papers. You can check the GATE General Aptitude syllabus 2026 below:-

Sections

Sub-Topics

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

GATE 2026 DA Syllabus PDF

IIT Guwahati has relleased the GATE 2026 DA syllabus pdf on its website. Download the GATE 2026 DA syllabus pdf from the link mentioned below:-

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GATE DA Syllabus 2026 Important Topics

While studying for GATE exam, you must make sure to pay special heed to the important topics. Note that these topics have been deemed important as questions related to them are asked repeatedly in the exam. In the table below, we have mentioned the important topics for GATE DA syllabus 2026 in detail:-

Important Topics

Sub Topics

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

GATE DA Syllabus 2026 Topic-Wise Weightage (Expected)

Topic wise wieghtage is a crucial aspect of the whole syllabus as through this, you will get to know about how much time you need to dedicate to all the topics. We suggest you to dedicate more time to the highly weighted topics and less time to the less weighted topics. Find the expected weightage of the GATE DA 2026 topics in the table below:-

Subject

Weightage of Marks

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

How to Prepare for GATE DA Syllabus 2026

GATE is one of the most difficult competitive tests, requiring extensive practice and preparation to pass. There are several strategies to prepare for the GATE exam. Check out some of the GATE preparation strategies 2026 below:-

  • Analyze Syllabus and Pattern: First, analyze the GATE syllabus 2026 and GATE exam pattern 2026 to know what topics need to be studied, the marking scheme, section-wise weightage, etc.
  • Make Appropriate Study Plans: It is usually a good idea to plan ahead of time when studying for an exam. Create weekly, and monthly GATE study plans.
  • Recognize Your Strengths and Shortcomings: Before digging deeper into anything, you must first understand your own strengths and shortcomings. Analyze the syllabus and make a list of topics that you have to study from fresh.
  • Revise Thoroughly: Whether it’s for the GATE or any other exam, you must thoroughly revise it to pass it.
  • Solve Previous Year Papers: Attempting GATE previous year question papers and mock tests will help you in improving your exam preparation by working on your mistakes. It also helps in getting familiar with the exam pattern.
  • Improve Time Management Skills: Time accuracy is important for GATE exam preparation, as the paper is long. You should work on your time management skills to complete the paper on time. Solving mock tests can help you improve your time accuracy.
  • Make Notes: While studying the GATE syllabus 2026 make notes of important topics and formulas. These notes will come in handy in revision.
  • Avoid Stress: Preparing for the national level exam can be challenging. But you should stay positive. Take enough sleep and have a balanced meal.

Best Books for GATE DA Syllabus 2026

To study for the GATE DA exam, you should refer to the best books only. The GATE 2026 DA best books are chosen by the exam experts. Refer to the following GATE best books 2026 for exam preparation:-

Name of the Book

Author

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

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