GATE 2024 New Paper DS & AI Syllabus is Out Now: Download Now
The Graduate Aptitude Test in Engineering (GATE) is a highly competitive examination that serves as a gateway to prestigious postgraduate programs and research opportunities in India's top institutions. Every year, thousands of aspirants from across the country prepare rigorously for this examination, with hopes of securing a bright future in their respective fields. For the year 2024, aspiring candidates in the domains of Data Science and Artificial Intelligence (AI) have reason to celebrate, The new GATE Paper is added to the GATE 2024 as the total papers in GATE 2024 raised to 30 Now and The syllabus for these cutting-edge fields has been officially released.
The Significance of GATE 2024 for Data Science and AI
Data Science and Artificial Intelligence have emerged as two of the most transformative and sought-after domains in recent years. As these fields continue to drive innovation and reshape industries worldwide, the importance of having a standardized evaluation process like GATE cannot be overstated. GATE ensures that candidates possess the necessary knowledge and skills to excel in these competitive fields, which are critical for the future of technology and data-driven decision-making
In the academic year 2024-25, the Indian Institute of Science (IISc) Bangalore has introduced a new paper, GATE 2024 Data Science and Artificial Intelligence (DA). To easily obtain the GATE 2024 Data Science and AI Syllabus PDF, please refer to the link provided in this article.
For candidates who have selected GATE 2024 DA as their primary choice, there is an opportunity to choose a second choice among various other papers, including CS (Computer Science and Information Technology), EC (Electronics and Communication Engineering), EE (Electrical Engineering), MA (Mathematics), ME (Mechanical Engineering), ST (Statistics), and XE (Engineering Sciences) in the GATE two-paper combination.
GATE 2024 Data Science and AI Syllabus
Subject | 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, 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 | (i) Supervised Learning: regression and classification problems, simple linear regression, multiple linear regression, ridge regression, logisic regression, k-nearest neighbor, 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, boƩom-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. |
Important Points for GATE 2024 DS & AI Paper
- The GATE 2024 DA Syllabus is divided into different parts. About 40% of it covers Mathematics, 25% deals with Programming and Databases from Computer Science, and the rest includes AI and Machine Learning.
- If you're taking exams like CS, EE, EC, or ME, you might find the GATE 2024 DA papers a bit easier because they share some topics with Mathematics, CS, EE, and ECE.
- Experts also think that the GATE 2024 DA paper from IISc Bangalore might be set up in a way that makes the Mathematics part challenging, while the AI and machine learning parts could be a bit easier. This is because it's the first year they're introducing this paper.
- Subjects like Probability and Statistics and Machine Learning are expected to be more important in the GATE 2024 DA Syllabus compared to other topics because they have a lot of content to cover.
The release of the GATE 2024 syllabus for Data Science and Artificial Intelligence marks a significant milestone for aspiring candidates in these fields. With the right preparation and dedication, they can embark on a rewarding journey toward postgraduate studies and research opportunities in India's top engineering institutions, ultimately contributing to the growth and innovation of these transformative domains.