GATE Data Science and Artificial Intelligence Syllabus 2024

The GATE Data Science and Artificial Intelligence will be a 3-hour long computer-based test with 100 questions, of which 15 will be general aptitude questions and 85 will be subject questions. The subject questions will be divided into Seven sections: Probability and Statistics, Linear Algebra, Calculus and Optimization, Programming Data Structures and Algorithms, Database Management and Warehousing, Machine Learning, and Artificial Intelligence(AI). Detail syllabus of Data Science and Artificial Intelligence is given below:

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-modeI, 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 modeling: 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 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, bottom-up: single-linkage, multiple- linkage, dimensionality reduction, principal component analysis.

Artificial Intelligence (AI): Search: informed, uninformed, adversarial; logic, propositional, predicate; reasoning under - conditional independence representation, exact inference through variable uncertainty topics elimination, and approximate inference through sampling.

Eligibility for GATE Data Science and AI Exam

Bachelor’s degree in Engineering / Technology

(4 years after 10+2 or 3 years after B.Sc. / Diploma in Engineering / Technology)

Age Limit for GATE Data Science and AI Exam

No age limit to attempt the GATE exam.

Application Fees For GATE Data Science and AI Exam

Female candidates SC/ST/PwD:₹ 900

All other candidates including foreign nationals: ₹ 1200