GATE Data Science & AI
- 1
- Year
- 1
- Paper
- 65
- Questions
Introduced in 2024. Probability and statistics, linear algebra, calculus and optimisation, Python programming with data structures and algorithms, databases and warehousing, machine learning, and AI (search, logic, reasoning under uncertainty).
Exam pattern
- 65 questions, 100 marks, 3 hours: General Aptitude (Q1–10, 15 marks) + subject (Q11–65, 85 marks)
- MCQ (one correct option), MSQ (one or more correct, all-or-nothing) and NAT (type a number)
- Wrong MCQ answers cost 1/3 (1-mark) or 2/3 (2-mark); no negative marking for MSQ or NAT
- On-screen virtual scientific calculator only
The most recent paper solved here, GATE 2026 Data Science and Artificial Intelligence (DA), ran to 65 questions: 10 in Section GA and 55 in Section DA.
Conducted by IISc and the IITs (organising institute rotates). Patterns change from year to year, so confirm the current one at gate2027.iitm.ac.in.
Topic analysis
How many questions each topic was worth, year by year, across the 1 solved GATE DA paper (65 questions). Sittings of the same year are counted together.
| Subject · topic | 2026 | Total |
|---|---|---|
| Probability | 12 | 12 |
| Random Variables | 4 | 4 |
| Standard Distributions | 4 | 4 |
| Bayes Theorem | 1 | 1 |
| Combinatorics | 1 | 1 |
| Descriptive Statistics | 1 | 1 |
| Discrete Probability | 1 | 1 |
| Databases & Warehousing | 11 | 11 |
| Relational Model | 4 | 4 |
| Data Warehouse Modelling | 2 | 2 |
| Indexing | 2 | 2 |
| SQL | 2 | 2 |
| ER Model | 1 | 1 |
| General Aptitude | 10 | 10 |
| Analytical Aptitude | 3 | 3 |
| Verbal Aptitude | 3 | 3 |
| Quantitative Aptitude | 2 | 2 |
| Spatial Aptitude | 2 | 2 |
| Machine Learning | 9 | 9 |
| Supervised Learning | 2 | 2 |
| Unsupervised Learning | 2 | 2 |
| Algorithms for ML Tasks | 1 | 1 |
| Model Evaluation | 1 | 1 |
| Model Selection | 1 | 1 |
| Neural Networks | 1 | 1 |
| Optimisation | 1 | 1 |
| Programming, Data Structures & Algorithms | 9 | 9 |
| Basic Sorting Algorithms | 2 | 2 |
| Programming in Python | 2 | 2 |
| Divide and Conquer | 1 | 1 |
| Graph Algorithms | 1 | 1 |
| Recursion | 1 | 1 |
| Search Algorithms | 1 | 1 |
| Trees | 1 | 1 |
| Artificial Intelligence | 6 | 6 |
| Logic | 4 | 4 |
| Adversarial Search | 1 | 1 |
| Search | 1 | 1 |
| Linear Algebra | 5 | 5 |
| Matrix Theory | 3 | 3 |
| Quadratic Forms | 1 | 1 |
| Subspaces | 1 | 1 |
| Analysis | 3 | 3 |
| Combinatorics | 1 | 1 |
| Functions of a Single Variable | 1 | 1 |
| Series | 1 | 1 |
| All topics | 65 | 65 |
Syllabus
From the IISc and the IITs (organising institute rotates) GATE 2027 DA syllabus. Always check it against the current notification.
Section 1
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, binomial distribution, Continuous random variables and probability distribution function, uniform, exponential, Poisson, normal, standard normal, tdistribution, chi-squared distributions, cumulative distribution function, Conditional PDF, Central limit theorem, confidence interval, z-test, t-test, chi-squared test.
Section 2
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.
Section 3
Calculus and Optimization
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
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
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
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
Search: informed, uninformed, adversarial; logic, propositional, predicate; reasoning under uncertainty topics — conditional independence representation, exact inference through variable elimination, and approximate inference through sampling.