GATE DA - Data Science and Artificial Intelligence - is one of the six GATE papers Myndaq prepares candidates for, and this guide is its dedicated map: the official revised 2027 syllabus section by section, the paper frame, and the strategy decisions that are specific to this paper rather than to GATE in general.
The GATE 2027 cycle you are preparing for
IIT Madras is the organizing institute for GATE 2027, and the cycle is live right now: the application portal opened on 2 September 2026, regular registration closes on 27 September 2026 (without late fee) and extended registration closes on 5 October 2026 with a late fee. Registration through DigiLocker is mandatory for Indian nationals. The examinations run on 6-7, 13-14 and 20-21 February 2027, with results on 19 March 2027 and city allotment notified on 4 January 2027. The syllabi for the GATE 2027 test papers have been revised, so prepare from the official 2027 PDF linked below rather than an older year's copy. All dates are as published on the official site and liable to change - recheck before acting.
The DA paper in the GATE frame
The paper itself follows the standard GATE frame: 65 questions, 100 marks, 3 hours, computer-based, with General Aptitude contributing 10 questions for 15 marks and the subject component 55 questions for 85 marks across MCQ, MSQ and NAT formats. The exam pattern and negative-marking maths are decoded in their own guide, and because high-volume papers run multiple sessions, your marks pass through normalization before they become a GATE score. The branch-agnostic preparation system - the 6-month plan, the mock cadence, the error ledger - lives in the GATE 2027 preparation guide; this page adds what is specific to this paper.
The official 2027 syllabus, section by section
The summaries below follow the official GATE 2027 DA syllabus PDF (linked at the end). They are orientation, not substitutes - download the PDF and use it as your coverage checklist.
Probability and Statistics
Counting, probability axioms and conditioning, random variables and standard distributions, expectation and variance, central limit theorem, and hypothesis-testing foundations. This is the heart of the DA paper - statistics behaves like a core subject, not a maths formality.
Linear Algebra
Vector spaces and subspaces, linear dependence, matrix operations, rank, eigenvalues and eigenvectors, and decompositions - the machinery beneath every ML section topic.
Calculus and Optimization
Single-variable calculus, limits and continuity, and optimisation - including the convexity and gradient-based ideas that ML questions quietly assume.
Programming, Data Structures and Algorithms
Programming in Python - not C - plus the standard data-structure and algorithm canon: stacks, queues, trees, hashing, searching, sorting and graph basics.
Database Management and Warehousing
ER-model, relational model and SQL, integrity constraints, normal forms, and warehousing concepts alongside data transformation ideas.
Machine Learning
Supervised learning - regression and classification with the standard model families, bias-variance and validation - and unsupervised learning with clustering and dimensionality reduction.
AI
Search, logic and reasoning under uncertainty - the classical AI toolkit that complements the ML section.
Strategy that is specific to DA
DA is the newest mainstream GATE paper, first offered in 2024, and it behaves differently from CS in three ways worth planning around. The programming language is Python, so code-reading drills in C are wasted effort here. The statistics-linear-algebra-optimization block carries the paper: candidates from CS or engineering backgrounds usually underestimate how much probability fluency the ML and statistics sections demand, while maths-background candidates underestimate SQL and data structures. Diagnose which of the two you are early, because the fix is different.
The PYQ pool is still shallow - only a few official papers exist - so supplement with the DA syllabus PDF used as a checklist: every listed sub-topic has appeared or will appear, and there is less history to reveal weightage patterns than in CS or ME. That makes syllabus-complete coverage more valuable in DA than in older papers. If you are choosing between this paper and CS, the CS vs DA comparison is the decision framework.
Quick answers
Which programming language does GATE DA use? Python. The official DA syllabus lists programming in Python alongside the data-structures and algorithms canon.
Is GATE DA easier than GATE CS? They are different rather than easier or harder: DA is statistics- and ML-centred with Python, CS is systems- and theory-centred with C. Choose by target programme and background - the comparison guide linked above walks through it.
