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GATE 2027 Data Science and Artificial Intelligence syllabus: Candidates looking for the GATE Data Science and Artificial Intelligence syllabus 2027 can refer to this article. The IIT Madras has published the GATE 2027 DS and AI syllabus on the official website, gate2027.iitm.ac.in. Candidates are advised to look at the GATE exam pattern along with the GATE syllabus. Candidates are advised to look at the GATE exam pattern along with the syllabus. The GATE 2027 question paper will be prepared based on the syllabus. The authority will conduct the GATE 2027 exam on February 6, 7, 13, 14, 20 & 21, 2027. Read the article for GATE 2027 DS and AI syllabus.
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IIT Madras has published the GATE syllabus 2027 for DS and AI online. The GATE 2027 DA syllabus has been uploaded as PDF. The syllabus includes topics such as Probability, Statistics, Linear Algebra, Algorithms, Programming, Data Structures, Database Management Systems, Data Warehousing, Machine Learning, and Artificial Intelligence. Candidates can check the detailed syllabus for Data Science and Artificial Intelligence syllabus from the table below:
Subject | Topics |
GATE DA syllabus for 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, distribution, chi-squared distributions, cumulative distribution function, Conditional PDF, Central limit theorem, confidence interval, z-test, t-test, chi-squared test. |
GATE DA syllabus for 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. |
GATE DA syllabus for Calculus and Optimisation | Functions of a single variable, limit, continuity and differentiability, Taylor series, maxima and minima, optimization involving a single variable. |
GATE DA syllabus for 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 |
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. |
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. |
GATE DA syllabus for 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. |
Candidates can check the GATE DS and AI topic-wise weightage from the table. Candidates should prepare accordingly based on the weightage of their topics. It will help in good preparation of candidates.
Topic Name | Number of Questions | Total Marks |
General Aptitude | 10 | 15 |
Probability and Statistics | 10 | 16 |
Linear Algebra | 6 | 10 |
Calculus and Optimization | 5 | 8 |
Programming, Data Structures, and Algorithms | 13 | 21 |
Database Management and Warehousing | 6 | 8 |
Machine Learning | 8 | 11 |
Artificial Intelligence (AI) | 7 | 11 |
Total | 65 | 100 |
Below are the GATE 2027 preparation tips that candidates should follow while preparing for the exam:
Start as early as possible: Candidates need to start as early as possible for better preparation. Staring early will help with having enough time for preparing a schedule, checking the GATE 2027 syllabus, and understanding the topics.
Prepare a study schedule: Candidates need to prepare a proper study schedule with a time table. The schedule should be prepared based on the strengths and weaknesses of the candidate.
Go for the reference materials: you should go for good reference materials such as undergraduate books, good books, focus on high weightage topics, and others. Start with a basic and wide spectrum of knowledge to have a good knowledge of the base topics.
Keep revising: Students should keep revising all the past topics they learned. Revision will help them to keep their minds fresh about the learned topics.
On Question asked by student community
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