GATE (Graduate Aptitude Test in Engineering) 2026 is being conducted by IIT Guwahati. Among its 30 test papers, DA (Data Science & Artificial Intelligence) is a relatively new domain exam that integrates mathematics, computing, probability, machine learning, data engineering, and AI topics.
The DA paper consists of 100 marks, divided as General Aptitude (15 marks) + Core DA portion (85 marks).
Below is the official / widely accepted syllabus breakdown and which topics carry more weight (“important topics”) to focus on.
Table of Contents
GATE DA 2026: Syllabus Breakdown
The syllabus is broadly grouped into two sections:
General Aptitude (GA)
Core DA Subjects
Let’s look at the topic-wise breakdown.
1. General Aptitude (15 marks)
This section is common to all GATE papers.
Topics include:
Verbal / English aptitude
Basic grammar (tenses, articles, prepositions, conjunctions, subject-verb agreement), vocabulary (idioms, phrases), reading comprehension, sentence sequencingQuantitative aptitude / Numeric skills
Data interpretation (graphs, tables, charts), estimation, ratios, percentages, exponents and logarithms, permutation & combination, series, basic geometry & mensuration, elementary statistics & probabilityAnalytical / Logical & Spatial reasoning
Deduction / induction, analogies, numerical reasoning; spatial skills like transformations (rotation, mirroring), folding, patterns in 2D / 3D
Because GA is only 15% of the total, a consistent but lighter effort is enough to score decently here.
2. Core DA Subjects (85 marks)
This is where the vast majority of the “action” is. It covers around 7 major domains.
Below is a topic-wise snapshot:
| Domain | Key Topics / Subtopics |
|---|---|
| Probability & Statistics | Permutations & combinations; probability axioms; sample space, events; conditional, marginal, joint probability; Bayes’ theorem; expectation, variance; correlation & covariance; random variables (discrete & continuous); common distributions (uniform, Bernoulli, binomial, exponential, Poisson, normal, chi-square, t-distribution); cumulative distribution; conditional PDF; central limit theorem; confidence intervals; hypothesis testing (z-test, t-test, chi-square) |
| Linear Algebra | Vector spaces and subspaces; linear dependence / independence; matrices (orthogonal, projection, idempotent); determinants; rank & nullity; systems of linear equations, Gaussian elimination; eigenvalues & eigenvectors; quadratic forms; LU decomposition; singular value decomposition (SVD) |
| Calculus & Optimization | Limits, continuity, differentiation; Taylor series; maxima / minima; optimization of functions (especially of single variable)—finding critical points, analyzing convexity / concavity |
| Programming, Data Structures & Algorithms (DSA) | Programming (Python is often preferred) Data structures like stacks, queues, linked lists, trees, hash tables; basic sorting / searching (bubble, insertion, selection, quicksort, mergesort); graph theory basics; traversals (DFS / BFS), shortest path algorithms |
| Database Management & Warehousing | ER model; relational model (relational algebra, tuple calculus); SQL / queries; integrity constraints; normalization (1NF, 2NF, 3NF, BCNF); file organization, indexing; data transformation techniques (sampling, discretization, compression); data warehouse modeling (schemas, concept hierarchies) |
| Machine Learning (ML) | Supervised learning: linear regression (simple/multiple), logistic regression, ridge regression, SVM, decision trees; bias-variance tradeoff; cross-validation (k-fold, LOOCV); neural networks / multi-layer perceptron; unsupervised learning: clustering (k-means, hierarchical), dimensionality reduction (PCA) |
| Artificial Intelligence (AI) | Search (uninformed and informed: DFS, BFS, A*, etc.); adversarial search (minimax, alpha-beta pruning); logic (propositional, predicate); reasoning under uncertainty (conditional independence, inference, variable elimination, approximate inference, sampling) |
Additionally, some sources list topic-wise weightage estimates (based on mock exams / past patterns) such as:
ML: ~ 25–30% of subject marks
Probability & Statistics: ~ 20–25%
Programming / DSA: ~ 15–20%
Also, one syllabus source presents a weightage table:
GA: 15 | Probability & Statistics: 16 | Linear Algebra: 10 | Calculus & Optimization: 8 | Programming / Data Structures & Algorithms: 21 | DB & Warehousing: 8 | Machine Learning: 11 | AI: 11 (marks)
The total question count is generally 65 questions for 100 marks.
Also, negative marking applies only to MCQs:
For 1-mark MCQ: –1/3 mark for wrong answer
For 2-mark MCQ: –2/3 mark for wrong answer
No negative marking for Numerical Answer Type (NAT) questions
Important Topics / Focus Areas (High-Yield)
Given limited time, you should prioritize certain topics that typically carry higher weight or are conceptually dense. Here’s a refined “priority list”:
Machine Learning algorithms (regression, classification, decision trees, SVMs, neural networks)
Probability & Statistics (especially distributions, hypothesis testing, expectations, central limit theorem)
Programming + Data Structures & Algorithms (graph traversals, sorting, trees)
Linear Algebra / Matrix operations / Eigen decomposition / SVD
Database & SQL / Normalization / Data warehousing
Search techniques and logical reasoning in AI
Calculus + Optimization basics
If you master these, you can secure a strong base. Other topics (e.g. advanced warehouse modeling, approximate inference techniques) can be attempted if time permits.
Also, GA must not be neglected — 15 marks can swing overall ranking.
Suggested Study Strategy
Start early & plan phases: Begin with foundational mathematical topics (probability, linear algebra) → move to programming and DSA → then ML and AI topics → finally, databases & warehousing.
Integrate concepts: ML / AI heavily rely on math + programming, so make sure you connect concepts rather than treat them in silos.
Practice coding / DSA problems: Implement basic algorithms; write Python snippets to internalize data structures.
Mock tests + previous year papers: Expose yourself to DA mock exams and GATE DA papers to get pattern familiarity.
Revision & formula sheets: Maintain short summary sheets for formulas, distributions, inference rules etc.
Time management & accuracy: Especially with negative marking, be cautious on MCQs you are unsure of.
Conclusion
The GATE 2026 DA (Data Science & AI) syllabus is broad but well-structured, combining mathematics, programming, machine learning, AI, and data management. If you prepare smartly—focusing first on high-yield topics and then expanding—you stand a great chance of scoring well.
