GATE DS & AI Syllabus 2027 OUT: Download PDF, Subject-Wise Weightage
GATE 2027 DS & AI Syllabus 2027: IIT Madras has released GATE DS & AI Syllabus 2027 on its official website: gate2027.iitm.ac.in. GATE 2027 Data Science…
GATE 2027 DS & AI Syllabus 2027: IIT Madras has released GATE DS & AI Syllabus 2027 on its official website: gate2027.iitm.ac.in. GATE 2027 Data Science and Artificial Intelligence paper includes a total of sevent sections: probability and statistics, linear algebra, calculus and optimisation, programming, data structures, and algorithms, database management and warehousing, machine learning, and artificial intelligence.
GATE Data Science and Artificial Intelligence Syllabus 2027: General Aptitude
The General Aptitude (GA) in GATE 2027 is common for all 30 subjects. The entire section carries 15 marks out of 100. Candidates can check a brief overview of Syllabus below:
|
Topics |
Syllabus |
|---|---|
|
English |
English Grammar, Vocabulary, Sentence completion, Verbal analogies, word groups, instructions, critical reasoning, and verbal deduction |
|
Reading comprehension |
Reading comprehension and interpretation of passages |
|
Quantitative aptitude |
Numerical computation, estimation, ratios, percentages, powers, exponents, logarithms, permutations and combinations, probability, statistics, mensuration, geometry, and data interpretation using tables, charts, and graphs |
|
Analytical and spatial aptitude |
Logical and analytical reasoning, pattern recognition, spatial visualisation, transformation, paper folding, and assembling of shapes |
GATE 2027 DA Exam Pattern
The GATE DA exam will be conducted as a three-hour computer-based test. Three types of questions will be asked in the examination: multiple-choice questions (MCQ), multiple-select questions (MSQ), and numerical answer-type questions (NAT). According to the GATE Pattern, the DA question paper will comprise 65 questions of 100 marks. It is divided into general aptitude questions of 15 marks and subject-specific questions of 85 marks.
GATE Data Science and Artificial Intelligence Books for Preparation
Candidates can refer to the books below for GATE Preparation for Data Science
| Book Name | Author |
|---|---|
| GKP GATE Data Science and Artificial Intelligence – Guide (solved papers) | GK Publications |
| Mathematical Statistics and Data Analysis (3rd Edition) | John A. Rice |
| Introduction to Probability Models | Sheldon M.Ross |
| Data Structures and Algorithms in Python | Roberto Tamassia, Micheal H, Goldwasser, Michael T.Goodrich |
| Database Management Systems | Raghu Ramakrishnan |
| Data Mining and Data Warehousing | Prateek Bhatia |
| Artificial Intelligence by Example | Denis Rothman |
| Artificial Intelligence Basics | Tom Taulli |
| Machine Learning for Beginners | Chris Sebastian |
| Fundamentals of Database Systems | Navathe Shamkant |
GATE Data Science Important Topics and Weightage
Students can check GATE DA important topics and chapter-wise weightage (as per experts)
| Subjects | Approximate Weightage/Marks |
|---|---|
| Probability and Statistics | 16 marks |
| Linear Algebra | 10 marks |
| Calculus and Optimisation | 8 marks |
| Programming, Data Structures, and Algorithms | 21 marks |
| Database Management and Warehousing | 8 marks |
| Machine Learning | 11 marks |
| Artificial Intelligence | 11 marks |
GATE 2027 DA Syllabus: Check Detailed Syllabus
Candidates can download the GATE Data Science and Artificial Intelligence Syllabus in PDF format from the official website or check the detailed syllabus below –
| Topics (Section) | Sub-Topics |
|---|---|
| Probability and Statistics | Counting (permutation and combinations), probability axioms, Sample space, events, independent events, mutually exclusive events, marginal, conditional and joint probability, Bayes Theorem, conditional expectation and variance, mean, median, mode and standard deviation, correlation, and covariance, random variables, discrete random variables and probability mass functions, uniform, Bernoulli, binomial distribution, Continuous random variables and probability distribution function, uniform, exponential, Poisson, normal, standard normal, tdistribution, chi-squared distributions, cumulative distribution function, Conditional PDF, Central limit theorem, confidence interval, z-test, t-test, chi-squared test. |
| Linear Algebra | Vector space, subspaces, linear dependence and independence of vectors, matrices, projection matrix, orthogonal matrix, idempotent matrix, partition matrix and their properties, quadratic forms, systems of linear equations and solutions; Gaussian elimination, eigenvalues and eigenvectors, determinant, rank, nullity, projections, LU decomposition, singular value decomposition. |
| Calculus and Optimization | Functions of a single variable, limit, continuity and differentiability, Taylor series, maxima and minima, optimization involving a single variable |
| Programming, Data Structures, and Algorithms | Programming in Python, basic data structures: stacks, queues, linked lists, trees, hash tables; Search algorithms: linear search and binary search, basic sorting algorithms: selection sort, bubble sort and insertion sort; divide and conquer: mergesort, quicksort; introduction to graph theory; basic graph algorithms: traversals and shortest path. |
| Database Management and Warehousing | ER-model, relational model: relational algebra, tuple calculus, SQL, integrity constraints, normal form, file organization, indexing, data types, data transformation such as normalization, discretization, sampling, compression; data warehouse modelling: schema for multidimensional data models, concept hierarchies, measures: categorization and computations. |
| Machine Learning | Supervised Learning: regression and classification problems, simple linear regression, multiple linear regression, ridge regression, logistic regression, k-nearest neighbour, naive Bayes classifier, linear discriminant analysis, support vector machine, decision trees, bias-variance trade-off, cross-validation methods such as leave-one-out (LOO) cross-validation, k-folds cross-validation, multi-layer perceptron, feed-forward neural network; Unsupervised Learning: clustering algorithms, k-means/k-medoid, hierarchical clustering, top-down, bottom-up: single-linkage, multiple-linkage, dimensionality reduction, principal component analysis. |
| Artificial Intelligence (AI) | Search: informed, uninformed, adversarial; logic, propositional, predicate; reasoning under uncertainty topics: conditional independence representation, exact inference through variable elimination, and approximate inference through sampling. |