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

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 based on previous-year analysis, 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 is a scoring booster — consistent practice with previous year questions can help you secure all 15 marks.

Subject-Wise GATE DA Syllabus

Probability & Statistics

This section carries approximately 16 marks and forms the mathematical backbone for ML and AI.

• Counting, probability axioms, conditional probability, Bayes' theorem
• Random variables — discrete and continuous distributions (Binomial, Poisson, Normal, Exponential)
• Mean, median, mode, standard deviation, moments
• Joint distributions, covariance, correlation
• Sampling distributions, Central Limit Theorem
• Hypothesis testing, confidence intervals, maximum likelihood estimation

Linear Algebra

Carries approximately 10 marks. Essential for understanding ML algorithms like PCA, SVD, and neural networks.

• Vector spaces, subspaces, linear independence, basis, dimension
• Matrices — rank, inverse, determinant, trace
• Systems of linear equations
• Eigenvalues, eigenvectors, diagonalization
• LU decomposition, Singular Value Decomposition (SVD)
• Orthogonality, projections, inner product spaces

Calculus & Optimization

Carries approximately 8 marks. Needed for gradient descent, backpropagation, and optimization problems.

• Functions of single and multiple variables, limits, continuity
• Differentiation — partial derivatives, chain rule, Taylor series
• Maxima, minima, saddle points
• Constrained optimization — Lagrange multipliers

GATE DA Machine Learning Syllabus

The GATE DA machine learning syllabus carries approximately 11 marks and is one of the most important sections for aspirants targeting AI/ML roles after GATE. Topics include:

• Supervised Learning: Linear regression, logistic regression, k-nearest neighbors, Naive Bayes, decision trees, Support Vector Machines
• Unsupervised Learning: K-means clustering, hierarchical clustering, principal component analysis (PCA)
• Neural Networks: Perceptrons, multi-layer perceptrons, backpropagation, activation functions
• Model Evaluation: Bias-variance tradeoff, cross-validation, confusion matrix, precision, recall, F1-score, ROC-AUC
• Ensemble Methods: Bagging, boosting (basic concepts)

Combined with the AI section (search, logic, probabilistic reasoning — ~11 marks), ML and AI together account for 22 marks of the paper.

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 — the highest-weighted core section at approximately 21 marks. Python is the only programming language tested in GATE DA (unlike GATE CS which focuses on C).

• Python Fundamentals: Variables, data types, operators, control flow (if/else, loops)
• Data Structures in Python: Lists, tuples, dictionaries, sets, strings, list comprehensions
• Functions: Recursion, scope, built-in functions
• Abstract Data Structures: Stacks, queues, linked lists, trees, graphs, hash tables
• Algorithms: Searching (linear, binary), sorting (merge sort, quicksort), time & space complexity (Big-O notation)
• Algorithm Design: Greedy algorithms, dynamic programming, divide and conquer

Since Python is also the industry standard for Data Science and AI, mastering this section provides direct career benefits beyond GATE.

Database Management & Warehousing

Carries approximately 8 marks. A scoring and predictable section.

• ER-model, relational model, relational algebra
• SQL — queries, joins, aggregate functions, nested queries
• Normalization — 1NF, 2NF, 3NF, BCNF
• Data warehousing concepts — OLAP, star schema, ETL basics

Artificial Intelligence

Carries approximately 11 marks. Overlaps conceptually with ML.

• Search — BFS, DFS, A*, heuristic search
• Logic — propositional logic, first-order logic, inference
• Reasoning under uncertainty — Bayesian networks, Markov models
• Planning and constraint satisfaction (basics)

GATE DA Subject-Wise Weightage

Based on GATE DA 2025 paper analysis and previous-year trends.

Key insight: Mathematics (Probability + Linear Algebra + Calculus) combined accounts for 34 marks (~40% of core section). Programming & DSA is the single highest-weighted core subject at 21 marks.

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. Advanced DSA: Dynamic programming, greedy algorithms, graph algorithms.
3. Subject-wise tests: Take weekly tests to identify weak areas early.

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 at least 5 years of GATE DA papers.
3. Revision: Focus on high-weightage topics and your weak areas.

Subject-Wise Preparation Priority

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. The syllabus listed in this guide is based on the latest officially released version. Always cross-check with the official GATE website for the most current version, as minor updates may occur annually.

Does GATE DA include machine learning?

Yes, Machine Learning is a core subject in the GATE DA syllabus, carrying approximately 11 marks. It covers supervised learning (regression, classification), unsupervised learning (clustering, PCA), neural networks, and model evaluation metrics. Combined with AI (another 11 marks), ML-related topics account for 22% of the paper.

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 (the highest-weighted core section at ~21 marks). You need to be proficient in Python syntax, data structures, and algorithm implementation.

What is the weightage of ML in GATE DA?

Machine Learning carries approximately 11 marks (11% weightage) based on previous-year paper analysis. However, if you include Artificial Intelligence (which overlaps conceptually with ML), the combined weightage is about 22 marks (22%). The actual distribution may vary slightly each year.

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?

The official GATE DA syllabus PDF is available on the GATE organizing institute's website each year. For GATE 2026, it was hosted on the IIT Guwahati GATE portal. The GATE 2027 syllabus PDF will be released when the new organizing IIT publishes the official notification (typically mid-year).

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. Focus on high-weightage subjects (Programming & DSA, Probability, Mathematics), build strong conceptual foundations, 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. Browse all our GATE DA articles for more preparation resources.