GATE Data Science & Artificial Intelligence Syllabus 2026, Check GATE DA Important Topics, Download PDF

GATE DA Syllabus 2026: The candidates who are preparing for the GATE 2026 Data Science & Artificial Intelligence (DA) exam must go through the GATE Data Science & Artificial Intelligence syllabus. IIT Guwahati has released the comprehensive syllabus for GATE 2026 Data Science & Artificial Intelligence, along with the official notification. This syllabus PDF outlines all the important topics that can be covered in the upcoming GATE Data Science & Artificial Intelligence Paper. GATE Data Science & Artificial Intelligence exam aspirants can check the detailed GATE DA syllabus with weightage here. We have also attached the GATE DA syllabus PDF in this article.

GATE Data Science & Artificial Intelligence (DA) Syllabus 2026

The GATE syllabus for Data Science & Artificial Intelligence (DA) 2026 covers topics like  Probability and Statistics, Linear Algebra, Calculus and Optimisation, Programming, Data Structures and Algorithms, Database Management and Warehousing, Machine Learning, and Artificial Intelligence. It is essential for all the candidates who are going to appear in the GATE Data Science & Artificial Intelligence exam that they must be well-versed with the GATE Data Science & Artificial Intelligence syllabus before starting their preparation. Check the important topics for the GATE Data Science & Artificial Intelligence syllabus.

GATE Data Science & Artificial Intelligence Section-Wise Syllabus 2026 

The GATE Data Science & Artificial Intelligence (DA) exam contains two parts, i.e. General Aptitude and core Data Science & Artificial Intelligence subjects. The weightage of General Aptitude and core Data Science & Artificial Intelligence is 15% and 85% respectively. The detailed list of topics of the GATE Data Science & Artificial Intelligence syllabus is provided below.

Data Science & Artificial Intelligence

• 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, 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 elimination, eigenvalues and eigenvectors, determinant, rank, nullity, projections, LU decomposition, singular value decomposition.

• Calculus and Optimisation: Functions of a single variable, limit, continuity and differentiability, Taylor series, maxima and minima, optimisation 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 organisation, indexing, data types, data transformation such as normalisation, discretisation, sampling, compression; data warehouse modelling: schema for multidimensional data models, concept hierarchies, measures: categorisation and computations.

• Machine Learning: (i) 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; (ii) Unsupervised Learning: clustering algorithms, k-means/k-medoid, hierarchical clustering, top-down, bottom-up: single-linkage, multiple-linkage, dimensionality reduction, principal component analysis.

• Artificial Intelligence: 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 Data Science & Artificial Intelligence (DA) Syllabus 2026: Official PDF

The official GATE Data Science & Artificial Intelligence syllabus PDF has been released along with the notification on the official website of GATE 2026. Here, we provide you with the direct link to download the GATE Data Science & Artificial Intelligence 2026 syllabus.

How to Prepare the GATE Data Science & Artificial Intelligence (DA) Syllabus 2026?

As Data Science & Artificial Intelligence (DA) is a newly added subject in the GATE 2026 exam, all interested aspirants need to follow a well-planned approach to excel in the GATE Data Science & Artificial Intelligence (DA) exam. Here, we are giving you some tips for GATE preparation for the Data Science & Artificial Intelligence (DA) paper.

• Understand the Syllabus: First of all, the aspirants must thoroughly review the complete GATE Data Science & Artificial Intelligence syllabus. Note the important GATE Data Science & Artificial Intelligence topics, giving priority to those needing more attention. Make a study plan around these priorities.

• Create a Study Schedule: Once you go through the entire syllabus, create a complete study plan that covers all the topics given in the GATE Data Science & Artificial Intelligence syllabus. Allocate ample time to each subject/topic as per your convenience.

• Focus on Fundamental Understanding: Always focus on understanding the core principles of each topic. Only memorising things will not be enough for this exam. 

• Create Revision Notes: Develop a habit of making short revision notes with important formulas, concepts, and important points for quick last-minute review.

• Take Mock Tests: The candidates must take enough mock tests to get familiar with the real exam environment. After each mock test, you should analyse your performance and work on improving it. This practice will also help to improve time management abilities.

Best Books to Prepare for the GATE Data Science & Artificial Intelligence (DA) Syllabus 2026

The selection of the right study material plays a vital role in preparing for the GATE Data Science & Artificial Intelligence exam. Below is a list of some highly recommended books for the GATE Data Science & Artificial Intelligence syllabus.

GATE Data Science & Artificial Intelligence (DA) Exam Pattern 

The GATE Data Science & Artificial Intelligence paper includes questions based on General Aptitude and Data Science & Artificial Intelligence. The GATE Data Science & Artificial Intelligence paper comprises 65 questions with a total score of 100 marks. Candidates have 3 hours to complete the online exam. The question types include multiple-choice questions, multiple-select questions, and numerical-answer-type questions. Refer to the table below for more information on the GATE Data Science & Artificial Intelligence exam pattern.

Also check: The candidates can also check the detailed syllabus of the following subjects.