GATE DA Syllabus 2027 — Subject-Wise Breakdown & Weightage
The complete GATE Data Science & Artificial Intelligence syllabus — every subject, every topic, with weightage estimates and the preparation order our toppers actually followed.
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Source note: The official syllabus and exam details should always be verified with the latest GATE notification — see the official GATE syllabus page (IIT Guwahati). Weightage and priority shown here are based on The ML Hub's analysis of recent GATE DA papers and mentor experience.
In short
The GATE DA 2027 syllabus covers 7 core technical subjects — Probability & Statistics, Linear Algebra, Calculus & Optimization, Programming/Data Structures & Algorithms, DBMS & Warehousing, Machine Learning and Artificial Intelligence — plus General Aptitude (15 marks). The paper rewards strong fundamentals in probability, statistics and machine learning, with Python (not C) as the programming language. It is a different paper from GATE CS and should be prepared from the DA syllabus directly.
GATE DA 2027 Syllabus at a Glance
All 7 technical subjects plus General Aptitude, with weightage estimates and our priority rating.
| Subject | Est. Weightage | Priority |
|---|---|---|
| Probability & Statistics | 16–18% | High |
| Linear Algebra | 10–12% | High |
| Calculus & Optimization | 8–10% | Medium |
| Programming, Data Structures & Algorithms | 13–15% | High |
| Database Management & Warehousing | 8–10% | Medium |
| Machine Learning | 16–18% | High |
| Artificial Intelligence | 8–10% | Medium |
| General Aptitude | 15% (15 marks) | Medium |
Weightage figures are planning estimates from The ML Hub's analysis of GATE DA 2024–2026 papers, not official figures.
GATE DA Subject-Wise Weightage (Estimated Marks)
Approximate marks out of 100 per subject, based on our analysis of GATE DA 2024–2026 papers. Use it to prioritise effort, not as official figures.
General Aptitude is a fixed 15 marks; the remaining 85 marks are distributed across the 7 technical subjects. Figures are estimates and vary year to year.
Subject-Wise Syllabus & Topics
The official topic list for each subject. Tap a subject's study guide for a deeper walkthrough.
Probability & Statistics
Est. weightage 16–18% · High priority
Counting (permutations & combinations); probability axioms, sample space, events; independent and mutually exclusive events; marginal, conditional and joint probability; Bayes theorem; conditional expectation and variance; mean, median, mode, standard deviation; correlation and covariance; random variables; discrete distributions (Bernoulli, binomial, Poisson); continuous distributions (uniform, exponential, normal, standard normal, t, chi-squared); CDF; conditional PDF; central limit theorem; confidence intervals; z-test, t-test, chi-squared test.
Probability & Statistics study guide for GATE DALinear Algebra
Est. weightage 10–12% · High priority
Vector spaces and subspaces; linear dependence and independence; matrices; projection, orthogonal and idempotent matrices; partition matrices and properties; quadratic forms; systems of linear equations and Gaussian elimination; eigenvalues and eigenvectors; determinant, rank and nullity; projections; LU decomposition; singular value decomposition (SVD).
Linear Algebra study guide for GATE DACalculus & Optimization
Est. weightage 8–10% · Medium priority
Functions of a single variable; limit, continuity and differentiability; Taylor series; maxima and minima; optimization involving a single variable.
Calculus & Optimization study guide for GATE DAProgramming, Data Structures & Algorithms
Est. weightage 13–15% · High priority
Programming in Python; basic data structures — stacks, queues, linked lists, trees, hash tables; search algorithms — linear and binary search; sorting — selection, bubble, insertion, merge sort, quicksort; divide and conquer; introduction to graph theory; basic graph algorithms — traversals and shortest path.
Programming, Data Structures & Algorithms study guide for GATE DADatabase Management & Warehousing
Est. weightage 8–10% · Medium priority
ER model; relational model — relational algebra, tuple calculus, SQL; integrity constraints; normal forms; file organization and indexing; data types; data transformation — normalization, discretization, sampling, compression; data warehouse modelling — schemas for multidimensional models, concept hierarchies, measures and computations.
Database Management & Warehousing study guide for GATE DAMachine Learning
Est. weightage 16–18% · High priority
Supervised learning — simple and multiple linear regression, ridge regression, logistic regression, k-nearest neighbour, naive Bayes, linear discriminant analysis, SVM, decision trees, bias–variance trade-off, cross-validation (LOO, k-fold), multi-layer perceptron, feed-forward neural networks. Unsupervised learning — clustering (k-means, k-medoid), hierarchical clustering (single- and multiple-linkage), dimensionality reduction, principal component analysis (PCA).
Machine Learning study guide for GATE DAArtificial Intelligence
Est. weightage 8–10% · Medium priority
Search — informed, uninformed and adversarial; logic — propositional and predicate; reasoning under uncertainty — conditional independence representation, exact inference via variable elimination, approximate inference through sampling.
Artificial Intelligence study guide for GATE DAGeneral Aptitude
Est. weightage 15% (15 marks) · Medium priority
Verbal aptitude; quantitative aptitude; analytical aptitude; spatial aptitude. Common to all GATE papers and carries a fixed 15 marks.
High vs Medium Priority Topics
Every topic in the syllabus can be tested, but your time is finite. If you are planning effort, weight it like this:
High priority — most marks, most depth
- Probability & Statistics
- Machine Learning
- Programming, Data Structures & Algorithms
- Linear Algebra
Medium priority — score well, but smaller
- Calculus & Optimization
- DBMS & Warehousing
- Artificial Intelligence
- General Aptitude (fixed 15 marks)
Recommended Preparation Order
The sequence we suggest at The ML Hub — built so each subject supports the next.
Linear Algebra + Probability & Statistics
These are the mathematical foundation of Machine Learning. Build them first so ML concepts make sense instead of feeling like formulas to memorise.
Programming in Python + Data Structures
Most students underestimate this. Code along — don't just watch. NAT questions on output and complexity are very scoring.
Machine Learning
The highest-return subject once your maths is solid. Focus on understanding why an algorithm works, not just its steps.
Calculus & Optimization
Compact syllabus, predictable questions. Tie it back to ML (gradients, minima) so it reinforces earlier topics.
DBMS & Warehousing + Artificial Intelligence
Smaller, well-defined topics. SQL and normal forms are scoring; for AI, prioritise search and probabilistic inference.
General Aptitude (throughout)
Don't leave it for the end. 30–40 minutes a week is enough to keep 15 marks safe.
Want this turned into a week-by-week plan? Read our how to study the GATE DA syllabus guide, or follow the structured GATE DA course.
Common Mistakes When Reading the Syllabus
Treating the GATE CS syllabus as a substitute — DA drops Theory of Computation, Compiler Design, Networks and OS, and adds Machine Learning and AI. The papers are not interchangeable.
Reading 'Machine Learning' as one line and underestimating it — it is one of the heaviest subjects and needs the most practice.
Skipping the statistics depth — hypothesis testing (z, t, chi-squared) and distributions appear far more in DA than students expect.
Ignoring Python coding practice because it 'looks easy' — output-prediction and complexity NATs reward hands-on coding, not reading.
Chasing every topic equally instead of weighting effort toward Probability & Statistics, Machine Learning and Programming.
Ready to start preparing?
Cover this entire syllabus with topper-led lectures, then test yourself across 61 tests and 1,685 problems built for the GATE DA pattern.
Frequently Asked Questions
What is the syllabus for GATE DA 2027?+
GATE DA 2027 has 7 core technical subjects plus General Aptitude: Probability & Statistics, Linear Algebra, Calculus & Optimization, Programming/Data Structures & Algorithms, Database Management & Warehousing, Machine Learning, and Artificial Intelligence. General Aptitude carries 15 marks.
Which subjects have the highest weightage in GATE DA?+
Based on our analysis of the 2024–2026 papers, Probability & Statistics and Machine Learning carry the highest weightage (roughly 16–18% each), followed by Programming/DSA and Linear Algebra. Exact weightage shifts year to year — treat these as planning estimates.
Is the GATE DA syllabus the same as GATE CS?+
No. GATE DA tests Python (not C), emphasises probability, statistics and machine learning, and includes AI as a core subject. It excludes several GATE CS topics like Theory of Computation, Compiler Design, Computer Networks and Operating Systems.
How should I study the GATE DA syllabus in order?+
Start with the maths (Linear Algebra, Probability & Statistics), then Programming & DSA and Machine Learning, followed by Calculus, DBMS and AI. Keep General Aptitude as light weekly revision. For a detailed plan, see our preparation guide linked on this page.
Can I download the GATE DA syllabus as a PDF?+
Yes — click 'Save as PDF / Print Syllabus' and choose 'Save as PDF' in your browser's print dialog. The full syllabus stays on this page so you can also bookmark it for quick reference.