GATE DA Syllabus 2027: Important Topics, Weightage & Making Scheme

GATE DA Syllabus 2027: The Graduate Aptitude Test in Engineering (GATE) for Data Science and Artificial Intelligence (DA) is a national-level examination conducted for admission to postgraduate programmes (M.Tech/MS) in Data Science, AI, and related fields at IITs, IISc, and other institutes, as well as for recruitment in certain public sector undertakings. GATE 2027 is being organised by IIT Madras. The DA paper was introduced in 2024 and has maintained a stable syllabus structure.

The GATE DA 2027 syllabus is identical to the 2026 syllabus. It focuses on foundational mathematics, programming, core data science concepts, machine learning, and artificial intelligence. The paper tests both conceptual understanding and application skills through a mix of theoretical and problem-solving questions.

GATE DA 2027 Exam Pattern and Marking Scheme

The GATE DA exam is a computer-based test of three hours duration. It consists of 65 questions carrying a total of 100 marks. The paper has two main parts: General Aptitude (compulsory for all papers) and the core DA subjects. Questions include Multiple Choice Questions (MCQs), Multiple Select Questions (MSQs), and Numerical Answer Type (NAT) questions.

General Aptitude carries 15 marks, while the remaining 85 marks come from the seven core sections of the DA syllabus. Negative marking applies only to MCQs. There is no negative marking for MSQs or NAT questions. Candidates must manage time carefully across the diverse topics, as the paper balances mathematics, programming, and advanced AI/ML concepts.

GATE DA Syllabus 2026: Overview and Important Topics

The core syllabus is divided into seven technical sections plus General Aptitude. Mathematics (Probability & Statistics, Linear Algebra, and Calculus & Optimization) forms a strong foundation and typically accounts for a significant portion of the marks. Programming and Data Structures, Machine Learning, and Artificial Intelligence also carry high weightage in recent papers.

General Aptitude (15 Marks)

General Aptitude is common across all GATE papers and tests basic verbal, numerical, and analytical skills. It includes English grammar, vocabulary, reading comprehension, sentence completion, and verbal analogies. Quantitative aptitude covers percentages, ratios, time-work, averages, and data interpretation.

Analytical reasoning involves logical deduction, sets, and pattern recognition, while spatial aptitude includes shape transformations and 2D/3D visualisation. Strong performance here provides a reliable scoring base and helps boost the overall score significantly. Regular practice of previous years’ aptitude questions is highly recommended.

Probability and Statistics

This section forms the mathematical backbone of data science. It covers counting principles (permutations and combinations), probability axioms, sample space, independent and mutually exclusive events, marginal, conditional and joint probabilities, and Bayes’ theorem. Random variables (discrete and continuous), probability mass/density functions, and major distributions (Uniform, Bernoulli, Binomial, Poisson, Exponential, Normal, t-distribution, chi-squared) are essential.

Candidates must also master mean, median, mode, standard deviation, correlation, covariance, Central Limit Theorem, confidence intervals, and hypothesis testing (z-test, t-test, chi-squared test). Questions often combine theoretical concepts with numerical calculations.

Linear Algebra

Linear Algebra is critical for understanding machine learning algorithms and data transformations. Key topics include vector spaces, subspaces, linear dependence and independence, basis, and dimension. Matrix algebra covers projection, orthogonal, idempotent, and partition matrices along with their properties, quadratic forms, rank, nullity, determinant, and inverse.

Systems of linear equations, Gaussian elimination, eigenvalues and eigenvectors, LU decomposition, and Singular Value Decomposition (SVD) are frequently tested. A clear understanding of projections and matrix factorisations helps in solving both theoretical and application-based questions.

Calculus and Optimization

This section focuses on functions of a single variable. Topics include limits, continuity, differentiability, Taylor series, maxima and minima, and single-variable optimisation techniques. Candidates should be comfortable with finding critical points, testing for local/global extrema, and applying calculus concepts to optimisation problems.

Although relatively smaller in weightage compared to Probability or Linear Algebra, questions from this area are often straightforward and scoring if the fundamentals are strong. Practice with standard calculus problems related to unconstrained optimisation is useful.

Programming, Data Structures and Algorithms

Programming is tested primarily in Python. Candidates must know basic syntax, data types, control structures, functions, recursion, and common libraries relevant to data science. Data structures include stacks, queues, linked lists, trees, and hash tables.

Search algorithms (linear and binary search), basic sorting algorithms (selection, bubble, insertion sort), and divide-and-conquer methods (merge sort, quicksort) are important. Graph theory basics, traversals (BFS, DFS), and shortest-path algorithms also appear. Understanding time and space complexity (Big-O notation) is essential for algorithm-related questions.

Database Management and Warehousing

This section covers both traditional database concepts and data warehousing. Topics include the ER model, relational model, relational algebra, tuple calculus, SQL queries, integrity constraints, and normal forms (1NF to BCNF). File organisation, indexing, and basic transaction concepts are also included.

Data transformation techniques such as normalisation, discretisation, sampling, and compression are tested. Data warehouse modelling covers multidimensional schemas, concept hierarchies, measures, and basic OLAP operations. Practical knowledge of writing efficient SQL queries is highly beneficial.

Machine Learning

Machine Learning is one of the high-weightage and conceptually rich sections. Supervised learning covers regression (simple, multiple, ridge) and classification (logistic regression, k-NN, Naive Bayes, LDA, SVM, decision trees). Bias-variance trade-off, cross-validation (LOO and k-fold), multi-layer perceptrons, and feed-forward neural networks are important.

Unsupervised learning includes clustering (k-means/k-medoids, hierarchical clustering with single and complete linkage) and dimensionality reduction via Principal Component Analysis (PCA). Evaluation metrics and the practical understanding of model selection are frequently examined.

Artificial Intelligence

The AI section focuses on classical AI techniques. Search algorithms include uninformed (BFS, DFS), informed (A*, greedy), and adversarial search (minimax, alpha-beta pruning). Logic covers propositional and predicate logic along with basic inference.

Reasoning under uncertainty includes representation of conditional independence, exact inference through variable elimination, and approximate inference via sampling methods. These topics test both theoretical understanding and the ability to apply algorithms to problem scenarios.

GATE DA Syllabus 2027 PDF Download

Download Free PDF of GATE DA Syllabus 2027, Click on this link: DA_GATE 2027_Syllabus PDF

Preparation Strategy and Key Takeaways

A balanced approach is essential. Strengthen the mathematical foundations (Probability, Linear Algebra, and Calculus) first, as they underpin Machine Learning and AI concepts. Parallel practice of Python programming and data structures is necessary. Regularly solve previous years’ GATE DA papers (2024–2026) to understand the question pattern and difficulty level.

Focus on high-weightage areas such as Programming & DSA, Machine Learning, Probability & Statistics, and Artificial Intelligence while ensuring adequate coverage of Database and Calculus topics. Consistent revision, mock tests under timed conditions, and analysis of mistakes will significantly improve performance. Since the syllabus remains unchanged from 2026, candidates who prepared earlier can continue with the same material without major adjustments.