GATE 2026 DA Exam Timeline
For the 2026 cycle, the Data Science and Artificial Intelligence (DA) paper is officially scheduled for the final afternoon session of the examination window.
Event
Official Date / Time
Admit Card Release
January 13, 2026
DA Exam Date
February 15, 2026 (Afternoon)
Result Declaration
March 19, 2026
Exam Pattern & Question Types
The GATE DA paper consists of 65 questions for a total of 100 marks. Unlike many other papers, the DA paper does not have a separate "Engineering Mathematics" section; instead, math is integrated into the core subjects.
• MCQ (Multiple Choice): 1 or 2 marks. Negative marking applies (1/3 or 2/3).
• MSQ (Multiple Select): One or more options can be correct. No negative marking.
• NAT (Numerical Answer): Precise numerical calculation required. No negative marking.
Major Sectional Weightage
The 100 marks are divided into General Aptitude and the core DA discipline. Probability and Statistics often carry the highest weightage among technical subjects.
Subject Area
Approx. Marks
Weightage %
General Aptitude
15 Marks
15%
Probability and Statistics
16-18 Marks
~17%
Machine Learning
14-16 Marks
~15%
Prog, Data Structures & Algorithms
12-14 Marks
~13%
Mathematics: Linear Algebra & Calculus
Success in Machine Learning is impossible without a strong grasp of these mathematical foundations.
1. Linear Algebra (10-12 Marks): Vector spaces, Eigenvalues/Eigenvectors, LU Decomposition, and SVD.
2. Calculus & Optimization (8-10 Marks): Maxima/Minima, Taylor Series, and Gradient Descent.
Artificial Intelligence & Machine Learning
These sections form the technical heart of the paper. Focus on both conceptual theory and numerical implementation.
Machine Learning: Focus on Supervised Learning (Regression, SVM, Decision Trees), Unsupervised Learning (Clustering, PCA), and Neural Networks.
Artificial Intelligence: Focus on Search Techniques (Informed, Uninformed, Adversarial), Propositional Logic, and Reasoning under Uncertainty.
Success Roadmap for DA 2026
1. Master Python logic: The programming section focuses heavily on Python-based data structures and algorithms. Practice dry-running Python code snippets.
2. Leverage NAT Accuracy: Since Probability and Optimization are full of NATs, practice using the virtual calculator to avoid precision errors.
3. Statistics is King: Nearly 20% of the technical paper revolves around Statistics. Ensure you are comfortable with Hypothesis Testing (z-test, t-test) and Bayes' Theorem.
4. Cross-Disciplinary Prep: Use resources from both Computer Science (for DBMS/Algorithms) and Statistics backgrounds.
Official Links & Reference Portals
Refer to the Organizing Institute for the most authentic syllabus and exam pattern links:
