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SYLLABUS GATE Data Science and Artificial Intelligence Syllabus 2027: DA PDF and Exam Pattern
GATE DA syllabus 2027 with the official PDF, 100-mark exam pattern and complete section-wise topic tables for Data Science and Artificial Intelligence.
Updated:
GATE DA syllabus 2027Data Science and Artificial IntelligenceGATE 2027 PDFIIT Madras
Use the available GATE DA Syllabus 2027 download resources and review the detailed syllabus, unit-wise topics, exam pattern information, preparation guidance below.
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Key Highlights
- DA is the official GATE code for Data Science and Artificial Intelligence.
- The official syllabus contains 7 sections: Probability and Statistics; Linear Algebra; Calculus and Optimization; Programming, Data Structures and Algorithms; Database Management and Warehousing; Machine Learning; AI.
- The paper is a 3-hour Computer-Based Test for 100 marks, including 15 marks of General Aptitude.
- Allowed second-paper codes when DA is primary: CS, EC, EE, MA, ME, PH, RA, ST, XE.
- The official IIT Madras PDF is available in the download section.
GATE DA Syllabus 2027 Overview
The official GATE DA syllabus 2027 for Data Science and Artificial Intelligence is organized into 7 sections, covering Probability and Statistics, Linear Algebra, Calculus and Optimization, Programming, Data Structures and Algorithms and the remaining paper-specific areas listed below. This page follows the IIT Madras syllabus order, provides the correct 100-mark exam pattern, and links the official PDF so aspirants can prepare from a complete, verified checklist.
How to Prepare from the GATE DA Syllabus
- Create one checklist for every official section and retain the same sequence used in the PDF.
- Start with a diagnostic test, then allocate more study time to weak high-coverage sections instead of dividing time equally.
- Solve previous-year GATE questions immediately after completing each topic and record errors by concept, calculation and time pressure.
- Revise formulas, definitions and frequently confused conditions in short weekly cycles, followed by mixed-section tests.
- Use the official PDF as the final scope document; coaching notes should expand a listed topic, not introduce an unrelated syllabus.
GATE DA Official Source and Internal Links
The syllabus tables were checked against the IIT Madras GATE 2027 DA PDF. Use the download section for the database-hosted copy, the GATE syllabus hub to switch papers, and the notification page for registration dates and policy updates.
Exam Pattern
GATE DA Exam Pattern 2027
| Section | Marks | How it applies |
|---|---|---|
| General Aptitude | 15 | Common to all GATE papers |
| Core subject questions | 85 | Selected test-paper syllabus |
| Total | 100 | 3-hour CBT |
General Aptitude
Marks
15
How it applies
Common to all GATE papers
Core subject questions
Marks
85
How it applies
Selected test-paper syllabus
Total
Marks
100
How it applies
3-hour CBT
GATE DA Question and Marking Rules
| Question type | Possible marks | Negative marking |
|---|---|---|
| MCQ | 1 or 2 | Yes: 1/3 for a wrong 1-mark MCQ; 2/3 for a wrong 2-mark MCQ |
| MSQ | 1 or 2 | No negative marking and no partial marking |
| NAT | 1 or 2 | No negative marking |
MCQ
Possible marks
1 or 2
Negative marking
Yes: 1/3 for a wrong 1-mark MCQ; 2/3 for a wrong 2-mark MCQ
MSQ
Possible marks
1 or 2
Negative marking
No negative marking and no partial marking
NAT
Possible marks
1 or 2
Negative marking
No negative marking
Syllabus Breakdown
GATE DA Syllabus 2027 - Official Section-wise Topics
The tables below preserve the section order and complete topic coverage published by IIT Madras for the GATE 2027 DA paper. Use each table as a study and revision checklist, and verify any later corrigendum against the official PDF.
Section 1: Probability and Statistics
| Topic area | Official syllabus coverage |
|---|---|
| Official coverage | 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, binomial distribution, Continuous random variables and probability distribution function, uniform, exponential, Pois son, normal, standard normal, t - distribution, chi-squared distributions, cumulative distribution function, Conditional PDF, Central limit theorem, confidence interval, z-test, t-test, chi-squared test. |
| Topic area | Official syllabus coverage |
|---|---|
| Official coverage | 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, binomial distribution, Continuous random variables and probability distribution function, uniform, exponential, Pois son, normal, standard normal, t - distribution, chi-squared distributions, cumulative distribution function, Conditional PDF, Central limit theorem, confidence interval, z-test, t-test, chi-squared test. |
Section 2: Linear Algebra
| Topic area | Official syllabus coverage |
|---|---|
| Official coverage | 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 eliminati on, eigenvalues and eigenvectors, determinant, rank, nullity, projections, LU decomposition, singular value decomposition. |
| Topic area | Official syllabus coverage |
|---|---|
| Official coverage | 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 eliminati on, eigenvalues and eigenvectors, determinant, rank, nullity, projections, LU decomposition, singular value decomposition. |
Section 3: Calculus and Optimization
| Topic area | Official syllabus coverage |
|---|---|
| Official coverage | Functions of a single variable, limit, continuity and differentiability, Taylor series, maxima and minima, optimization involving a single variable. |
| Topic area | Official syllabus coverage |
|---|---|
| Official coverage | Functions of a single variable, limit, continuity and differentiability, Taylor series, maxima and minima, optimization involving a single variable. |
Section 4: Programming, Data Structures and Algorithms
| Topic area | Official syllabus coverage |
|---|---|
| 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. |
| Topic area | Official syllabus coverage |
|---|---|
| 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. |
Section 5: Database Management and Warehousing
| Topic area | Official syllabus coverage |
|---|---|
| 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 modelling: schema for multidimensional data models, concept hierarchies, measures: categorization and computations. |
| Topic area | Official syllabus coverage |
|---|---|
| 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 modelling: schema for multidimensional data models, concept hierarchies, measures: categorization and computations. |
Section 6: Machine Learning
| Topic area | Official syllabus coverage |
|---|---|
| Supervised Learning | regression and classification problems, 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, cross-validation methods such as leave -one-out (LOO) cross-validation, k-folds cross-validation, multi-layer perceptron, feed-forward neural network; |
| Unsupervised Learning | clustering algorithms, k-means/k-medoid, hierarchical clustering, top-down, bottom-up: single-linkage, multiple-linkage, dimensionality reduction, principal component analysis. |
| Topic area | Official syllabus coverage |
|---|---|
| Supervised Learning | regression and classification problems, 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, cross-validation methods such as leave -one-out (LOO) cross-validation, k-folds cross-validation, multi-layer perceptron, feed-forward neural network; |
| Unsupervised Learning | clustering algorithms, k-means/k-medoid, hierarchical clustering, top-down, bottom-up: single-linkage, multiple-linkage, dimensionality reduction, principal component analysis. |
Section 7: AI
| Topic area | Official syllabus coverage |
|---|---|
| Search | informed, uninformed, adversarial; logic, propositional, predicate; reasoning under uncertainty topics — conditional independence representation, exact inference through variable elimination, and approximate inference through sampling. |
| Topic area | Official syllabus coverage |
|---|---|
| Search | informed, uninformed, adversarial; logic, propositional, predicate; reasoning under uncertainty topics — conditional independence representation, exact inference through variable elimination, and approximate inference through sampling. |
Download Official PDFs & Question Papers
Official GATE DA Syllabus 2027 PDF
Prepare for Graduate Aptitude Test in Engineering
Frequently Asked Questions
Use the featured PDF button on this page. It opens the database-hosted copy of the official IIT Madras DA syllabus.
Yes. General Aptitude is compulsory in every GATE 2027 test paper and carries 15 marks.
The paper uses Multiple Choice Questions, Multiple Select Questions and Numerical Answer Type questions carrying one or two marks.
Negative marking applies only to incorrect MCQs. MSQ and NAT questions have no negative marking, and MSQs have no partial marking.
Follow the official section order, complete topic-level concepts and examples, solve previous-year questions after each unit, and use full-length mock tests only after completing the major sections.