GATE Exams

GATE Data Science & Artificial Intelligence Vacancy 2026: Apply Online, Selection Process

📅 Updated On 11 October 2026✨ Update By Jyoti Sharma🌐 Hindi & English

GATE Data Science & Artificial Intelligence Free Mock Test

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GATE Data Science & Artificial Intelligence Syllabus 2027, Exam Pattern

GATE Data Science & Artificial Intelligence Syllabus 2027, Exam Pattern, Exam Date 2027

Detail Information about GATE has published notification 2027 for the Notification of Data Science & Artifical Intelligence Entrance Exam. Those Candidates who are Interested to the following Entrance exam and completed all Eligibility Criteria can read the Notification & Apply Online. In this page we provide the Complete Syllabus of this Exam with Latest Update Exam Pattern and the Exam Date also.

GATE Exam Highlights 2027

Exam	Graduate Aptitude Test in Engineering (GATE)
Organization	Indian Institute of Technology (IIT) Madras
Application Mode	Online
Frequency of Examination	Annual
Exam Date 2027	06th, 07th, 13th, 14th, 20th & 21st February 2027
Mode of Examination	Computer-Based Mode
Category	Syllabus and Exam Pattern
Purpose of Examination	Qualifying Examination for M.E./ M.Tech/ Ph.D admissions and PSU
Official Website	gate2027.iitm.ac.in

GATE Data Science & Artificial Intelligence Syllabus 2027

Verbal Aptitude :

1. Basic English grammar
2. Tenses
3. Articles
4. Adjectives
5. Prepositions
6. Conjunctions
7. Verb-noun agreement and other parts of speech
8. Basic vocabulary
9. Words
10. Idioms
11. Phrases in context
12. Reading and comprehension
13. Narrative sequencing

Quantitative Aptitude :

1. Data interpretation
2. Data graphs (bar graphs, pie charts, and other graphs representing data)
3. 2- and 3-dimensional plots
4. Maps
5. Tables
6. Numerical computation and estimation
7. Ratios
8. Percentages
9. Powers
10. Exponents and logarithms
11. Permutations and combinations
12. Series
13. Mensuration and geometry
14. Elementary statistics
15. Probability

Analytical Aptitude :

1. Logic: deduction and induction
2. Analogy
3. Numerical relations and reasoning

Spatial Aptitude :

1. Transformation of shapes
3. Translation
4. Rotation
5. Scaling
6. Mirroring
7. Assembling
8. Grouping
9. Paper folding
10. Cutting
11. Patterns in 2 and 3 dimensions

Data Science & Artificial Intelligence :

Probability and Staisics : Couning (permutaion and combinaions), probability axioms, Sample space, events, independent events, mutually exclusive events, marginal, condiional and joint probability, Bayes Theorem, condiional expectaion and variance, mean, median, mode and standard deviaion, correlaion, and covariance, random variables, discrete random variables and probability mass funcions, uniform, Bernoulli, binomial distribuion, Coninuous random variables and probability distribuion funcion, uniform, exponenial, Poisson, normal, standard normal, t-distribuion, chi-squared distribuions, cumulaive distribuion funcion, Condiional PDF, Central limit theorem, confidence interval, z-test, t-test, chi-squared test.

Linear Algebra : Vector space, subspaces, linear dependence and independence of vectors, matrices, projecion matrix, orthogonal matrix, idempotent matrix, pariion matrix and their properies, quadraic forms, systems of linear equaions and soluions; Gaussian eliminaion, eigenvalues and eigenvectors, determinant, rank, nullity, projecions, LU decomposiion, singular value decomposiion.

Calculus and Opimizaion : Funcions of a single variable, limit, coninuity and differeniability, Taylor series, maxima and minima, opimizaion involving a single variable.

Programming, Data Structures and Algorithms :

1. Programming in Python
2. Basic data structures: stacks, queues, linked lists, trees, hash tables
3. Search algorithms: linear search and binary search
4. Basic soring algorithms: selecion sort, bubble sort and inserion sort
5. Divide and conquer: mergesort, quicksort; introducion to graph theory
6. Basic graph algorithms: traversals and shortest path.

Database Management and Warehousing :

ER-model, relaional model: relaional algebra, tuple calculus, SQL, integrity constraints, normal form, file organizaion, indexing, data types, data transformaion such as normalizaion, discreizaion, sampling, compression;

data warehouse modelling: schema for mulidimensional data models, concept hierarchies, measures: categorizaion and computaions.

Machine Learning :

(i) Supervised Learning : regression and classificaion problems, simple linear regression, muliple linear regression, ridge regression, logisic regression, k-nearest neighbour, naive Bayes classifier, linear discriminant analysis, support vector machine, decision trees, bias-variance
trade-off, cross-validaion methods such as leave-one-out (LOO) cross-validaion, k-folds crossvalidaion, muli-layer perceptron, feed-forward neural network;

(ii) Unsupervised Learning : clustering algorithms, k-means/k-medoid, hierarchical clustering, top-down, boƩom-up: single-linkage, muliplelinkage, dimensionality reducion, principal component analysis.

Artificial Intelligence : Search: informed, uninformed, adversarial; logic, proposiional, predicate; reasoning under uncertainty topics - condiional independence representaion, exact inference through variable eliminaion, and approximate inference through sampling.

GATE Data Science & Artificial Intelligence Exam Pattern 2027

Examination Mode	Computer Based Test
Duration	180 Minutes
Number of Subjects (Papers)	29
Sections	General Aptitude (GA) + Candidate’s Selected Subject
Types of Questions	(a) Multiple Choice Questions (MCQ) (b) Multiple Select Questions (MSQ) (c) Numerical Answer Type (NAT)
Questions for testing these abilities	(a) Recall (b) Comprehension (c) Application (d) Analysis and Synthesis
Number of Questions	10 (GA) + 55 (Subject) = 65 Questions
Total Marks	100
Marking Scheme	Questions carry 1 mark and 2 marks
Negative Marking	For a wrong answer chosen in an MCQ, there will be a negative marking. For 1-mark MCQ, 1/3 mark will be deducted for a wrong answer. Likewise, for 2-mark MCQ, a 2/3 mark will be deducted for a wrong answer. There is no negative marking for wrong answer(s) to MSQ and NAT questions.

GATE Data Science & Artifical Intelligence :

S.No	Subject	No.of Question	Marks
1	General Aptitude	5 (5 x 1)	5
	General Aptitude	5 (5 x 2)	10
2	Data Science & Artifical Intelligence	55	85
	Total	65	100

GATE Exam Date 2026 : 06th, 07th, 13th, 14th, 20th & 21st February 2027

Starting Date of Application Form : 02nd September 2026

Last Date of Application Form : 27th September 2026

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