GATE DA Syllabus 2027 PDF — Official Topics & Marks Distribution
A downloadable, printable GATE Data Science & Artificial Intelligence syllabus reference — all seven technical subjects, official section marks and preparation guidance. Save it as a PDF for offline revision.
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Source note: Topics follow the official GATE DA 2027 syllabus PDF (IIT Madras). The official exam pattern fixes 15 marks for General Aptitude and 85 for all technical subjects combined. Preparation priorities are teaching guidance, not predicted subject marks.
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. Preparation focus reflects learning dependencies, not predicted marks.
| Subject | Official Marks | Preparation Focus |
|---|---|---|
| Probability & Statistics | Within 85 technical marks; no fixed subject allocation | Foundation |
| Linear Algebra | Within 85 technical marks; no fixed subject allocation | Foundation |
| Calculus & Optimization | Within 85 technical marks; no fixed subject allocation | Foundation |
| Programming, Data Structures & Algorithms | Within 85 technical marks; no fixed subject allocation | Foundation |
| Database Management & Warehousing | Within 85 technical marks; no fixed subject allocation | Core coverage |
| Machine Learning | Within 85 technical marks; no fixed subject allocation | Core coverage |
| Artificial Intelligence | Within 85 technical marks; no fixed subject allocation | Core coverage |
| General Aptitude | 15 marks | Throughout |
General Aptitude carries 15 marks. All seven technical subjects share the remaining 85 marks; no fixed marks are prescribed for any individual technical subject.
GATE DA Weightage: Official Marks Distribution
The official GATE 2027 pattern specifies two allocations out of 100 marks: 15 for General Aptitude and 85 for the seven DA technical subjects combined. It does not specify subject-wise technical marks.
Source: official GATE 2027 question paper pattern (IIT Madras). Individual technical-subject weightages can vary each year; no per-subject prediction is presented here.
Subject-Wise Syllabus & Topics
The official topic list for each subject. Tap a subject's study guide for a deeper walkthrough.
Probability & Statistics
Within 85 technical marks; no fixed subject allocation · Preparation focus: Foundation
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 random variables and probability mass functions (uniform, Bernoulli, binomial, Poisson); continuous random variables and probability density functions (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
Within 85 technical marks; no fixed subject allocation · Preparation focus: Foundation
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
Within 85 technical marks; no fixed subject allocation · Preparation focus: Foundation
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
Within 85 technical marks; no fixed subject allocation · Preparation focus: Foundation
Programming in Python; basic data structures — stacks, queues, linked lists, trees, hash tables; search algorithms — linear and binary search; basic sorting — selection, bubble and insertion sort; divide and conquer — merge sort and quicksort; introduction to graph theory; basic graph algorithms — traversals and shortest path.
Programming, Data Structures & Algorithms study guide for GATE DADatabase Management & Warehousing
Within 85 technical marks; no fixed subject allocation · Preparation focus: Core coverage
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, categorization and computation of measures.
Database Management & Warehousing study guide for GATE DAMachine Learning
Within 85 technical marks; no fixed subject allocation · Preparation focus: Core coverage
Supervised learning — regression and classification problems, 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 (top-down and bottom-up; single- and multiple-linkage), dimensionality reduction, principal component analysis (PCA).
Machine Learning study guide for GATE DAArtificial Intelligence
Within 85 technical marks; no fixed subject allocation · Preparation focus: Core coverage
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
15 marks · Preparation focus: Throughout
Verbal aptitude; quantitative aptitude; analytical aptitude; spatial aptitude. Common to all GATE papers and carries a fixed 15 marks.
General Aptitude study guide for GATE DAPreparation Priorities, Not Marks Predictions
Every topic in the syllabus can be tested. Build the prerequisite skills, cover every technical subject, and allocate extra practice to weaknesses revealed by your tests. These groups describe learning dependencies, not expected weightage.
Build foundations
- Probability & Statistics
- Calculus & Optimization
- Linear Algebra
- Programming, Data Structures & Algorithms
Complete coverage and ongoing practice
- Machine Learning
- DBMS & Warehousing
- Artificial Intelligence
- General Aptitude throughout (fixed 15 marks)
Recommended Preparation Order
This is the exact order we teach in the GATE DA course — built so each subject supports the next. See the full week-by-week timeline on our course schedule.
Probability & Statistics
We start here because it provides a foundation for Machine Learning and probabilistic inference. Getting distributions, expectation and hypothesis testing solid early supports later subjects.
Calculus & Optimization
A compact subject taught right after, while your maths momentum is high. Limits, Taylor series and single-variable optimization feed directly into ML gradients and minima.
Linear Algebra
Vectors, matrices, eigenvalues and SVD — the language of data and ML. With probability and calculus done, the maths foundation is now complete.
Artificial Intelligence
A largely self-contained block of search, logic and reasoning under uncertainty. It sits here because its probabilistic inference builds naturally on the statistics you just covered.
Programming, Data Structures & Algorithms
Code along — don't just watch. Practise Python and the named data structures, searching, sorting and graph algorithms before moving on to ML.
Machine Learning
Study regression, classification, clustering and dimensionality reduction once your maths and programming support them. Focus on understanding why each algorithm works, not just its steps.
DBMS & Warehousing
Cover relational models, tuple calculus, SQL, normalization, indexing, data transformation and warehousing. Reserve dedicated practice time rather than treating this as an optional subject.
General Aptitude (throughout)
Don't leave it for the end. Practise all four aptitude areas regularly and adjust your study time to your diagnostic results; the section carries a fixed 15 marks.
Want this turned into a week-by-week plan? Read our 8-month GATE DA preparation plan or the syllabus study 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 overlooking its named models — cover regression, classification, clustering, neural networks and PCA.
Skipping the statistics depth — distributions, confidence intervals and hypothesis tests (z, t, chi-squared) are explicitly included.
Ignoring Python coding practice because it 'looks easy' — practise tracing programs and the algorithms named in the syllabus.
Treating a subject-wise marks prediction as guaranteed — only General Aptitude's 15 marks and the combined 85 technical marks are fixed. Cover every subject and use your weak areas to allocate practice time.
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?+
The official GATE DA pattern fixes 15 marks for General Aptitude and 85 marks for all seven technical subjects combined. It does not assign fixed marks to individual technical subjects or guarantee a highest-weightage subject. Cover the entire syllabus and use prerequisites and your practice results to decide your preparation priorities.
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?+
Our course sequence is Probability & Statistics, Calculus & Optimization, Linear Algebra, Artificial Intelligence, Programming/DSA, Machine Learning, then DBMS & Warehousing, with General Aptitude practised throughout. This is a teaching sequence, not a ranking of subject marks. Adjust revision time to your weak areas.
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.