GATE Syllabus 2024 for DA Paper: The Indian Institute of Science (gate.iisc.ac.in), Bangalore, the organising institute officially released complete syllabus of Data Science & Artificial Intelligence (DA) Paper for GATE 2024 exams.
The syllabus of paper DA (Data Science & Artificial Intelligence) has been divided into seven sections. The corresponding sections of the question paper contain different topics.
You may download GATE admit card and GATE exam schedule for paper Data Science & Artificial Intelligence for appearing in the Graduate Aptitude Test in Engineering for the year 2024.
GATE 2024 Paper Pattern for Data Science & Artificial Intelligence:
| Paper | Data Science & Artificial Intelligence |
|---|---|
| Paper Code | DA |
| Examination Mode | Computer Based Test (CBT) |
| Duration | 3 Hours (180 Minutes) |
| Type of Questions | (a) Multiple Choice Questions (MCQ) (b) Multiple Select Questions (MSQ) and/or Numerical Answer Type (NAT) Questions |
| Marking Scheme | Questions carry 1 mark and 2 marks |
| Negative Marking | For a wrong answer chosen in a MCQ, there will be negative marking. For 1-mark MCQ: 1/3 mark will be deducted for a wrong answer. For 2-mark MCQ: 2/3 mark will be deducted for a wrong answer. NO negative marking for MSQ & NAT. |
| Number of Questions | 10 (GA) + 55 (subject) = 65 Questions |
| General Aptitude (GA) Marks | 15 Marks |
| Engineering Mathematics | 13 Marks |
| Subject Questions | 72 Marks |
| Total Marks | 100 Marks |
GATE New Test Paper on (DA) Data Science and Artificial Intelligence
GATE 2024 Syllabus for Data Science and Artificial Intelligence
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 organisation, indexing, data types, data transformation such as normalisation, 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, mulo-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.
Artificial Intelligence
Search: informed, uninformed, adversarial; logic, propositional, predicate; reasoning under uncertainty topics – conditional independence representation, exact inference through variable elimination, and approximate inference through sampling.
You may download complete GATE syllabus 2024 for DA Data Science & Artificial Intelligence in PDF. For more detail, please visit official website GATE2024.IISc.ac.in.