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GATE 2027 Data Science and AI Syllabus: IIT Madras has uploaded the GATE Data Science and AI Syllabus 2027 online. The GATE 2027 syllabus for Data Science and Artificial Intelligence PDF is available on the official website, gate2027.iitm.ac.in. The Data Science and AI syllabus comprises all seven major topics that will be tested in the GATE exam. The topics are Probability, Statistics, Linear Algebra, Calculus and Optimisation, Programming, Data Structures, ETC. The authority has released the GATE 2027 exam pattern online along with the syllabus. The GATE 2027 exam will be held on February 6, 7, 13, 14, 20 & 21, 2027.
Direct link to download the GATE 2027 Data Science and AI Syllabus
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The GATE 2027 question paper will be prepared based on the syllabus. Candidates must refer to the GATE 2027 syllabus to ensure that they study all the relevant topics. Understanding the syllabus of DA for GATE 2027 is the first and foremost step while preparing for the GATE exam. Candidates can also check the GATE Data Science and AI question paper for a better understanding of the exam pattern and frequently asked topics in the exam.
IIT Madras has released the GATE 2027 Data Science and AI Syllabus on its official website, gate2027.iitm.ac.in. Candidates can check the updated GATE General Aptitude syllabus 2027 from the table below.
| Chapters | Topics |
|---|---|
Verbal Aptitude |
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Quantitative Aptitude |
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Analytical Aptitude |
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Spatial Aptitude |
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IIT Madras has uploaded GATE Syllabus for Artificial Intelligence and Data Science online as a pdf. The syllabus topics include Probability, Statistics, Linear Algebra, Algorithms, Programming, Data Structures, Database Management Systems, Data Warehousing, Machine Learning, and Artificial Intelligence. For detailed information, refer to the GATE syllabus for Data Science and AI 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, tdistribution, 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 |
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| 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. |
Related links:
| Topics 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 |
The GATE exam consists of two sections, General Aptitude and subject-oriented. Check the marking scheme of the GATE 2027 DA exam here. Candidates must note that for an incorrect answer in an MCQ, there will be negative marking. For a 1-mark question, 1/3 mark will be deducted and for a 2-mark question, 2/3 mark will be deducted.
| Subject | Marks Allotted |
|---|---|
General Aptitude (GA) | 15 |
Subject marks | 85 |
Total | 100 |
Related links:
Students can refer to the following table for the list of books for GATE preparation. A useful tip to aspirants is to make GATE Data Science and AI notes in a short and precise manner for better revision. Moreover, aspirants must check their level of preparation regularly with GATE Data Science and AI sample papers and mock tests.
| Book | Author |
|---|---|
Artificial Intelligence: A Modern Approach | Textbook by Peter Norvig and Stuart J. Russell |
‘Deep Learning’ | by Ian Goodfellow, Yoshua Benjio, Aaron Courville |
Introduction to Data Science: Practical Approach with R and Python | B. Uma Maheswari (Author), R. Sujatha (Author) |
Data Science for Dummies | Lillian Pierson (Author), Jake Porway (Foreword) |
Data Science from Scratch: First Principles with Python | Joel Grus |
Frequently Asked Questions (FAQs)
As per the exam pattern for GATE Data Science and Artificial Intelligence, the exam will have 15 marks worth GA section and the remaining 85 for the core subject. The question paper will comprise a mix of MCQs, MSQs and NATs.
A score of 90+ is considered the best for GATE 2027, since it increases the chances of admission drastically.
GATE DA 2027 will have three types of questions, multiple-choice (MCQ) type, multiple-select (MSQ) type, and numerical answer type (NAT).
Yes, there is a negative marking in the GATE Data Science and AI 2027.
It depends on the individual calibre of the aspirant. But yes, with proper planning, and good study material, 6 months is enough for GATE 2027 preparation. Provided that the students must have previous knowledge of the basics.
The GATE Data Science and AI syllabus 2027 includes such as Probability, statistics, linear algebra, algorithms, Programming, Data Structures, database management systems, warehousing, machine learning, and Artificial intelligence.
The weightage of the two sections in the exam is 15% and 85%,
Candidates must possess a degree in computer science, electronics and communication, electrical engineering, mathematics, statistics, and physics.
On Question asked by student community
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https://engineering.careers360.com/articles/gate-question-papers
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