Quick Summary: The GATE DA (Data Science and Artificial Intelligence) paper tests 7 core subjects worth 85 marks, plus General Aptitude worth 15 marks. GATE 2027 is conducted by IIT Madras, with exams on February 6, 7, 13, 14, 20 and 21, 2027. This guide breaks down every section of the GATE DA syllabus 2027, the weightage each subject typically carries, and the smartest order to study them in.
If you are starting your preparation, pair this guide with our complete GATE DA course 2027, which covers every subject below with lectures, notes, PYQs and a full test series.
GATE DA 2027 at a Glance
| Feature | Details |
|---|---|
| Paper code | DA (Data Science and Artificial Intelligence) |
| Conducting institute (2027) | IIT Madras |
| Exam dates | February 6, 7, 13, 14, 20, 21 — 2027 |
| Total marks | 100 (General Aptitude 15 + Core 85) |
| Questions | 65 (MCQ, MSQ and NAT) |
| Negative marking | Only for MCQs: −1/3 for 1-mark, −2/3 for 2-mark questions |
| Score validity | 3 years |
Section-Wise GATE DA Syllabus 2027
1. Probability and Statistics
Counting (permutations and combinations), probability axioms, sample space and events, independent and 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 PMFs (uniform, Bernoulli, binomial); continuous random variables and PDFs (uniform, exponential, Poisson, normal, standard normal, t-distribution, chi-squared); cumulative distribution functions; Central Limit Theorem; confidence intervals; z-test, t-test and chi-squared test.
This is consistently among the highest-weightage core subjects. Build it early with the Probability & Statistics for GATE DA course.
2. Linear Algebra
Vector spaces, subspaces, linear dependence and independence; matrices (projection, orthogonal, idempotent, partition); quadratic forms; systems of linear equations; Gaussian elimination; eigenvalues and eigenvectors; determinant, rank, nullity; projections; LU decomposition; singular value decomposition (SVD).
Linear algebra questions in GATE DA are concept-driven rather than lengthy — see the Linear Algebra for GATE DA course for a PYQ-first approach.
3. Calculus and Optimization
Functions of a single variable, limit, continuity and differentiability; Taylor series; maxima and minima; optimization involving a single variable.
The shortest section of the syllabus and one of the fastest to complete — the Calculus & Optimization course covers it end to end, and this GATE DA Calculus guide explains the strategy.
4. Programming, Data Structures and Algorithms
Programming in Python; stacks, queues, linked lists, trees, hash tables; linear and binary search; selection sort, bubble sort, insertion sort, divide and conquer with mergesort and quicksort; introduction to graph theory; graph traversals and shortest path.
Python-based questions make this section very scoring for practice-driven students — prepare with the Data Structures & Algorithms for GATE DA course.
5. Database Management and Warehousing
ER-model, relational model — relational algebra and tuple calculus; SQL; integrity constraints; normal forms; file organization and indexing; data transformation — normalization, discretization, sampling, compression; data warehouse modelling — multidimensional schemas, concept hierarchies, measures.
Read the detailed DBMS & Warehousing for GATE DA guide, and prepare with the DBMS and Data Warehousing course.
6. Machine Learning
Supervised learning: regression and classification, simple and multiple linear regression, ridge regression, logistic regression, k-nearest neighbours, naive Bayes, linear discriminant analysis, support vector machines, decision trees, bias-variance trade-off, cross-validation (leave-one-out, k-fold), multi-layer perceptrons and feed-forward neural networks. Unsupervised learning: clustering, k-means and k-medoids, hierarchical clustering (single-linkage and multiple-linkage), dimensionality reduction and PCA.
Machine Learning is the single largest core subject by weightage — start with the Machine Learning for GATE DA course.
7. Artificial Intelligence
Search — informed, uninformed, adversarial; logic — propositional and predicate; reasoning under uncertainty — conditional independence representation, exact inference through variable elimination, approximate inference through sampling.
Compact but conceptual — covered fully in the Artificial Intelligence for GATE DA course.
General Aptitude (15 Marks)
Verbal aptitude, quantitative aptitude, analytical aptitude and spatial aptitude. These 15 marks are the cheapest marks in the paper — do not leave them for the last week.
Subject-Wise Weightage Trend in GATE DA
| Subject | Approximate Weightage |
|---|---|
| Machine Learning | ~18–22 marks |
| Probability & Statistics | ~12–16 marks |
| Programming, DS & Algorithms | ~10–14 marks |
| Linear Algebra | ~8–10 marks |
| Artificial Intelligence | ~8–10 marks |
| Database Management & Warehousing | ~6–8 marks |
| Calculus & Optimization | ~4–6 marks |
| General Aptitude | 15 marks (fixed) |
Weightage varies year to year; treat this as a planning guide, not a guarantee.
What Order Should You Study the GATE DA Syllabus In?
- Linear Algebra + Probability & Statistics first — they are prerequisites for Machine Learning.
- Python, Data Structures & Algorithms in parallel — daily practice beats marathon sessions.
- Machine Learning next — the highest-weightage subject, once its math foundations are ready.
- AI, DBMS & Warehousing, Calculus — compact subjects that convert quickly into marks.
- General Aptitude + full-length mocks — throughout the final 3 months.
For the week-by-week plan, read the GATE DA preparation strategy for 2027, and see the best books for GATE DA for subject-wise references.
FAQs on GATE DA Syllabus 2027
Is Engineering Mathematics a separate section in GATE DA?
No. Unlike papers such as CS or EC, the DA paper has no separate Engineering Mathematics section — the math you need (probability, statistics, linear algebra, calculus) is built into the core syllabus itself.
Has the GATE DA syllabus changed for 2027?
IIT Madras announced a syllabus revision across GATE 2027 papers — the first in five years. Check the official GATE 2027 syllabus PDF for the final DA topic list, and revisit this page as we track changes.
Which subject should I start with?
Start with Linear Algebra and Probability & Statistics. They unlock Machine Learning, which carries the most marks.
Is the GATE DA syllabus easier than GATE CS?
It is shorter, but not “easy” — DA trades breadth (CS has 10+ subjects) for depth in math and ML. Strong fundamentals in probability and ML decide ranks in DA.
Free YouTube Lectures: Subject-Wise GATE DA Playlists
Want to start learning right now, for free? Piyush Wairale’s YouTube channel has subject-wise playlists mapped to this syllabus:
- Machine Learning for GATE DA — full playlist
- Artificial Intelligence for GATE DA — full playlist
- Calculus & Optimization for GATE DA — full playlist
- All subject playlists — Linear Algebra, Probability & Statistics, DBMS, Python/DSA and more
Subscribe to Piyush Wairale IITM on YouTube for new GATE DA lectures, PYQ solutions and strategy sessions.
Ready to Start Your GATE DA 2027 Preparation?
Structured lectures, notes, topic-wise PYQs and a full test series for every subject in this syllabus — by Piyush Wairale (IIT Madras).
Explore the Complete GATE DA Course 2027Recent Post
Probability and Statistics carries ~12–16 marks in GATE DA and underpins Machine Learning. This complete guide covers the full official syllabus — counting, Bayes' theorem, expectation, every named distribution, CLT, confidence intervals and the z/t/chi-squared tests — with diagrams, a worked Bayes example, GATE patterns, a study plan and FAQs, by IIT Madras instructor Piyush Wairale.
Machine Learning is the highest-weightage subject in GATE DA (~18–22 marks). This complete guide covers the full official syllabus — regression, classifiers, SVM, decision trees, bias-variance, cross-validation, neural networks, clustering and PCA — with diagrams, GATE question patterns, a 6-week plan and FAQs, by IIT Madras instructor Piyush Wairale.
Master reasoning under uncertainty for GATE DA 2027 — Bayesian networks, conditional independence, d-separation, variable elimination with a fully worked alarm-network example, and sampling-based approximate inference. With diagrams, GATE question patterns, a 10-day plan and FAQs, by IIT Madras instructor Piyush Wairale.
The best books for GATE DA 2027, subject by subject — probability, linear algebra, calculus, Python & DSA, DBMS, machine learning and AI — plus how to actually use them without drowning in references. By IIT Madras alumnus Piyush Wairale.
A practical GATE DA preparation strategy for 2027 — month-by-month study plan from August 2026 to exam day, subject order, PYQ practice, mock test schedule and revision system for the GATE Data Science and AI paper, by IIT Madras alumnus Piyush Wairale.
GATE DA 2027 will be conducted by IIT Madras on February 6, 7, 13, 14, 20 and 21, 2027, with registration expected to open in mid-August 2026. Full details on the notification, eligibility, exam pattern, application fee and preparation timeline for the GATE Data Science and AI paper.
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