In this article, you will get GATE 2024 Syllabus for Data Science & Artificial Intelligence (DA). Given Below is the official GATE syllabus for Data Science & Artificial Intelligence 2024 that has been released by IISC Bangalore. GATE syllabus for Data Science & Artificial Intelligence (DS & AI) is a newly introduced paper that evaluates candidates’ proficiency in the fields of data science and artificial intelligence.

It covers various sections such as Probability and Statistics, Linear Algebra, Calculus and Optimization, Machine Learning, AI, Programming, Data Structures, Algorithms, Database Management, and Warehousing.

GATE Syllabus for Data Science and Artificial Intelligence Download

This curriculum tests candidates’ understanding of theoretical concepts and practical applications in DS & AI, preparing them for advanced studies and careers in these sectors. The exam pattern consists of 65 questions, with 10 from General Aptitude and 55 from the core discipline, emphasizing the importance of both aptitude and specialized knowledge in the field.

GATE Syllabus for Data Science & Artificial Intelligence 2024-25

SECTIONS	Topics and 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, t-distribution, 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, par on matrix and their proper es, 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:	(i) 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; (ii) Unsupervised Learning: clustering algorithms, k-means/k-medoid, hierarchical clustering, top-down, bottom-up: single-linkage, multiple linkage, dimensionality reduction, principal component analysis.
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.

Data Science & Artificial Intelligence 2024 Paper Pattern and Marking

Mode of Examination: Online
Duration of Exam: 3 hours
Types of Questions: MCQs, MSQs and NAT
Sections: 2 sections – General Aptitude and Data Science & Artificial Intelligence
Total Marks: 100 marks
Total Questions: 65 questions
- General Aptitude – 15 Marks of MCQs Questions
  - 5 Questions – 1 Marks
  - 5 Questions – 2 Marks
- Data Science & Artificial Intelligence – 85 Marks of MCQs, MSQs and NATs Questions
  - 25 Questions – 1 Marks
  - 30 Questions – 2 Marks
Negative Marking
- For MCQs
  - ⅓ for 1 mark questions
  - ⅔ for 2 marks questions
- NATs – No Negative Marking

Frequently Asked Questions

What are the main topics covered in the GATE DS & AI syllabus?
- The syllabus includes Probability and Statistics, Linear Algebra, Calculus and Optimization, Machine Learning, AI, Programming, Data Structures, Algorithms, Database Management, Warehousing, Machine Learning and AI.
Is there a General Aptitude section in the GATE DS & AI paper?
- Yes, like all GATE papers, there is a General Aptitude section that carries 15% of the total marks.
Can I choose a second paper along with DS & AI?
- Yes, candidates can select a second paper from a pre-defined list of combinations provided by the GATE authorities.
- What is the weightage of the core discipline in the GATE DS & AI exam? The core discipline carries 85% of the total marks, which includes the subjects mentioned in the first FAQ.
Where can I find the detailed GATE syllabus for DS & AI?
- The detailed syllabus can be downloaded from our website or visit the official GATE website – https://gate2024.iisc.ac.in/.
Are there any changes in the syllabus from the previous year?
- The syllabus can be updated from time to time. Candidates should refer to the official GATE notification for the latest syllabus updates.
What type of questions are asked in the GATE DS & AI paper?
- The paper includes multiple-choice questions (MCQs), multiple-select questions (MSQs), and numerical answer type (NAT) questions based on the topics in the syllabus.
When will the GATE DS & AI exam be conducted?
- The GATE 2024 exam for Data Science and AI is scheduled to be conducted on February 3, 2024.