GATE DA Syllabus 2027: Official Topics, Weightage & Prep Plan

The GATE DA (Data Science and Artificial Intelligence) is becoming increasingly important as DA continues to be one of GATE's fastest-growing papers — growing from 52,493 registrations in GATE DA 2024 to 91,764 registrations in GATE DA 2026, with 69,242 candidates appearing in DA 2026. Whether you're searching for the GATE DA 2027 syllabus, the official subject-wise breakdown, or a reliable preparation plan, this guide covers everything you need.

Source: GATE 2026 Official Cut-off & Statistics — IIT Guwahati

Below you'll find the complete GATE Data Science and Artificial Intelligence syllabus including the official topics for each section, subject-wise weightage guidance, the GATE DA machine learning syllabus, the GATE DA Python syllabus, and a structured prep strategy used by GATE toppers at The ML Hub. If you're new to the exam, start with our complete GATE DA guide covering the full form, eligibility, exam pattern, and career scope. For GATE DA eligibility details including age limit, documents, and degree requirements, see our dedicated eligibility guide.

GATE DA 2027 Exam Pattern

Before diving into the syllabus, understanding the exam pattern helps you plan time allocation and attempt strategy.

The exam tests recall, comprehension, and application of concepts — so rote memorization alone won't cut it. A strong conceptual foundation is essential.

GATE DA 2027 Official Syllabus

The GATE DA official syllabus covers seven core technical areas plus General Aptitude. Here's the complete GATE DA subject-wise syllabus at a glance:

General Aptitude (15 Marks)

Common across all GATE papers. Divided into four sub-sections:

1. Verbal Aptitude: English grammar, vocabulary, reading comprehension, sentence completion.
2. Quantitative Aptitude: Data interpretation, mensuration, basic arithmetic, percentages.
3. Analytical Aptitude: Logic, induction, deduction, pattern recognition.
4. Spatial Aptitude: Transformation of shapes, paper folding, 2D/3D visualization.

GA carries 15 marks. Practise previous-year questions consistently and review errors across all four areas rather than assuming an easy or guaranteed score.

Subject-Wise GATE DA Syllabus

Probability & Statistics

This section provides mathematical foundations for ML and AI.

• 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, standard deviation; correlation and covariance; random variables
• Discrete random variables and probability mass functions: uniform, Bernoulli, binomial and Poisson distributions
• Continuous random variables and probability density functions: uniform, exponential, normal, standard normal, t and chi-squared distributions
• Cumulative distribution functions and conditional probability density functions
• Central Limit Theorem, confidence intervals, z-test, t-test and chi-squared test

Linear Algebra

These concepts support PCA, regression and neural networks.

• Vector spaces and subspaces; linear dependence and independence of vectors
• Matrices: projection, orthogonal, idempotent and partition matrices, and their properties; quadratic forms
• Systems of linear equations and solutions; Gaussian elimination
• Eigenvalues and eigenvectors; determinant, rank and nullity; projections
• LU decomposition and Singular Value Decomposition (SVD)

Calculus & Optimization

The official section is restricted to single-variable functions and optimization.

• Functions of a single variable; limit, continuity and differentiability
• Taylor series; maxima and minima
• Optimization involving a single variable

Partial derivatives, gradients and the chain rule can be useful supporting mathematics for ML derivations. They are not a separate multivariable-calculus or constrained-optimization syllabus block.

GATE DA Machine Learning Syllabus

The GATE DA machine learning syllabus builds on probability, linear algebra and calculus:

• Supervised Learning: Regression and classification; simple and multiple linear regression, ridge regression, logistic regression, k-nearest neighbors, Naive Bayes, linear discriminant analysis, support vector machines and decision trees
• Model Selection: Bias-variance tradeoff; leave-one-out and k-fold cross-validation
• Neural Networks: Multi-layer perceptron and feed-forward neural networks
• Unsupervised Learning: Clustering algorithms including k-means and k-medoid; hierarchical clustering, top-down and bottom-up approaches, single and multiple linkage
• Dimensionality Reduction: Principal component analysis (PCA)

Backpropagation and activation functions support understanding the listed neural networks; generic ensemble methods, random forests, boosting, CNNs and RNNs are not named official DA syllabus topics.

The ML Hub's GATE DA online course covers every ML topic with live lectures and hands-on problem solving — structured specifically around this syllabus.

GATE DA Python Syllabus

The GATE DA Python syllabus falls under Programming, Data Structures & Algorithms. Python is the programming language specified for DA (unlike GATE CS which focuses on C).

• Programming in Python
• Basic data structures: stacks, queues, linked lists, trees and hash tables
• Search algorithms: linear search and binary search
• Basic sorting: selection sort, bubble sort and insertion sort
• Divide and conquer: mergesort and quicksort
• Introduction to graph theory; graph traversals and shortest paths

Python syntax, built-in collections, functions, recursion and complexity analysis are useful supporting skills for these topics. Generic dynamic programming and greedy-algorithm units are not listed in the official DA syllabus.

Database Management & Warehousing

Prepare both database fundamentals and the explicitly listed data transformation and warehousing topics.

• ER-model, relational model, relational algebra, tuple calculus and SQL
• Integrity constraints, normal forms, file organization and indexing
• Data types; data transformation including normalization, discretization, sampling and compression
• Data warehouse modelling: multidimensional schemas, concept hierarchies, measure categorization and computations

Artificial Intelligence

Probability provides a useful foundation for reasoning under uncertainty.

• Search: informed, uninformed and adversarial
• Logic: propositional and predicate logic
• Reasoning under uncertainty: conditional independence representation; exact inference through variable elimination; approximate inference through sampling

GATE DA Official Marks Distribution

The official question paper pattern specifies only the following marks split. It does not publish fixed marks or question counts for any of the seven individual DA subjects.

Preparation guidance: Study mathematics before the ML topics that use it, build Python and algorithm skills through regular practice, and cover all seven DA subjects. Past papers are practice resources, not a guarantee of future subject-wise coverage.

GATE DA vs GATE CS

Many students are confused between GATE CS and GATE DA. Here's a clear comparison:

Bottom line: Choose DA if you're interested in data analysis, machine learning, and AI roles. Choose CS if you prefer systems programming, networking, and OS-level work.

How to Prepare for GATE DA 2027

A structured preparation strategy is critical. Based on guidance from The ML Hub mentors (including AIR 2 and AIR 6 rankers), here's a proven approach:

Phase 1: Build Foundations (Months 1–3)

1. Start with Mathematics: Probability, Linear Algebra, and Calculus are prerequisites for ML and AI. Spend 40% of early prep time here.
2. Master Python: Get comfortable with Python syntax, data structures, and basic algorithms. Practice coding daily.
3. Learn DBMS basics: ER models, SQL queries, and normalization are scoring and predictable.

Phase 2: Core Subjects (Months 4–7)

1. Machine Learning & AI: Build on your math foundation. Understand algorithms conceptually, not just formulae.
2. Algorithms: Selection, bubble and insertion sort; divide-and-conquer mergesort and quicksort; graph traversals and shortest paths.
3. Weekly practice: Use topic tests and review sessions to identify weak areas, and take a subject-level test when that subject is complete. The series includes 36 topic-wise and 8 subject-level tests.

Phase 3: Revision & Mock Tests (Months 8–10)

1. Full-length mocks: Simulate real exam conditions. Analyze every mistake. The ML Hub's GATE DA Test Series includes 10 full-length grand mock tests with 61 tests and 1,685 problems total.
2. Previous year papers: Solve all available official GATE DA papers (2024 onward). GATE DA started in 2024, so there is no five-year backlog.
3. Revision: Focus on syllabus gaps and weak areas identified through practice.

Subject-Wise Preparation Guidance

Sequence topics by prerequisites and adjust time using your diagnostic results, not predicted subject marks.

Best Resources for GATE DA Preparation

Since the DA paper is relatively new, choosing the right resources matters significantly:

• Textbooks: "Introduction to Linear Algebra" by Gilbert Strang, "Pattern Recognition and Machine Learning" by Christopher Bishop, "Machine Learning" by Tom Mitchell.
• Online Platform: The ML Hub GATE DA Course — live classes, DPPs, weekly tests, and 1:1 mentorship from IIT alumni.
• Practice: NPTEL video lectures, previous year GATE papers, The ML Hub GATE DA Test Series (61 tests, 1,685 problems), and The ML Hub free demo course.
• Community: Join The ML Hub's active Discord community for doubt-solving and peer discussion.

Our GATE DA toppers consistently credit structured preparation and daily practice as the key differentiators in their success.

FAQs

What is the GATE DA 2027 syllabus?

The GATE DA 2027 syllabus covers seven core areas: Probability & Statistics, Linear Algebra, Calculus & Optimization, Programming & DSA (Python), Machine Learning, Artificial Intelligence, and Database Management & Warehousing. Additionally, there is a General Aptitude section common to all GATE papers. The total paper is 100 marks with 65 questions.

Is the GATE DA syllabus official?

Yes, the GATE DA syllabus is officially published by the GATE organizing institute (an IIT) each year. For GATE 2027, the organizing institute is IIT Madras. The syllabus listed in this guide matches the officially released GATE DA 2027 syllabus. Always cross-check with the official GATE 2027 website for the most current version.

Does GATE DA include machine learning?

Yes. Machine Learning is one of the seven DA subjects. It covers supervised learning, clustering, dimensionality reduction, multi-layer perceptrons, feed-forward networks, bias-variance tradeoff and leave-one-out/k-fold cross-validation. No fixed marks are assigned to ML or ML and AI combined.

Is Python required for GATE DA?

Yes, Python is the only programming language tested in GATE DA. The syllabus explicitly mentions Python for the Programming, Data Structures & Algorithms section. You need to be proficient in Python syntax, data structures, and algorithm implementation.

What is the weightage of ML in GATE DA?

No fixed Machine Learning weightage is published. The official pattern assigns 15 marks to General Aptitude and 85 marks to all seven DA subjects combined, for 100 marks total. Do not assume a guaranteed marks share for ML or any other individual DA subject.

How should I prepare for GATE DA 2027?

Start with building strong mathematical foundations (Probability, Linear Algebra, Calculus) — these are prerequisites for ML and AI. Then master Python programming and DSA. Next, tackle ML and AI conceptually. Finally, focus on mock tests and previous year papers — the GATE DA Test Series with 61 tests is designed for exactly this phase. A 10-month preparation timeline with phased study is ideal. Consider joining a complete GATE DA coaching program for guided preparation.

Can I download the GATE DA syllabus PDF?

Yes. GATE 2027 is organized by IIT Madras, and the official GATE DA 2027 syllabus PDF is already available. You can download it directly from the GATE 2027 IIT Madras website or browse all papers on the Test Papers & Syllabus page.

What is the difference between GATE CS and GATE DA?

GATE CS focuses on computer systems (OS, networking, compilers, hardware) with C programming, while GATE DA focuses on data science (ML, AI, statistics, databases) with Python. DA excludes hardware and systems topics but includes advanced statistics and ML. Both share General Aptitude and some programming concepts.

Are there negative marks in GATE DA?

Yes, MCQ questions have negative marking — ⅓ mark deducted for wrong 1-mark MCQs and ⅔ mark for wrong 2-mark MCQs. However, MSQ (Multiple Select Questions) and NAT (Numerical Answer Type) questions have no negative marking.

What are the best books for GATE DA Machine Learning?

"Machine Learning" by Tom Mitchell, "Pattern Recognition and Machine Learning" by Bishop, and "Hands-On Machine Learning with Scikit-Learn" by Aurélien Géron are excellent choices. These cover the theoretical and practical aspects needed for the GATE DA ML syllabus.

Conclusion

The GATE DA syllabus 2027 is well-defined and manageable with the right strategy. Build strong foundations in mathematics and Python, cover all seven DA subjects, and practice consistently with mock tests. The combination of Python proficiency, mathematical rigor, and ML understanding will help you maximize your score.

Remember: structured preparation beats random studying. Use a proven study schedule, track your progress with weekly tests, and don't hesitate to seek guidance from GATE DA course mentors and IIT toppers who've cracked the exam themselves. Check out our GATE DA toppers' journeys for inspiration and realistic benchmarks. For a complete month-by-month study plan, see How to Prepare for GATE DA in 8 Months. To understand how your marks translate to a GATE score, read GATE DA Marks vs Score. Browse all our GATE DA articles for more preparation resources.