DA covers probability and statistics, linear algebra, calculus and optimisation, Python programming and algorithms, databases, machine learning and artificial intelligence. Its mathematics is inside the subject syllabus, not a separate 13-mark block.

Data Science and Artificial Intelligence is tested within a 100-mark paper. The paper also includes General Aptitude. The sections below show the complete subject syllabus.

GATE DA Syllabus and Marks Distribution

There is no optional subject block in this paper. All listed sections are part of the syllabus.

Data Science and Artificial Intelligence Unit-wise Syllabus

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, 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.

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 eliminati on, eigenvalues and eigenvectors, determinant, rank, nullity, projections, LU decomposition, singular value decomposition.

Calculus and Optimization

• Functions of a single variable, limit, continuity and differentiability, Taylor series, maxima and minima, optimization involving a single variable.

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.

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.

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.

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.

General Aptitude Syllabus

General Aptitude contributes 15 marks within the 100-mark paper. It is not an additional paper that can be left out when choosing the technical sections.

Chapter-wise Weightage and Important Topics

Only the section-wise marks are fixed. IIT Madras has not assigned a guaranteed number of marks to each chapter of Data Science and Artificial Intelligence. A topic may appear in one year and receive fewer questions in another; a past-paper count is not an official 2027 weightage.

For a chapter-weightage comparison, record the year, paper code, session, question number and marks of each matching question. Keep a combined question under one main topic or label the overlap explicitly; counting it twice inflates the total. GATE previous-year question papers can be used for this topic-by-topic practice.

Question Types and Negative Marking

The paper uses one-mark and two-mark questions. The number of MCQ, MSQ and NAT questions is not a fixed chapter-wise quota. In an MSQ, selecting one correct option is not enough when the correct answer contains two options.