Sample Question Paper of Data Science & AI GATE 2024

Hey Guys Welcome to CSE Stud 247 , Today I am provide you Sample Question Paper of Data Science & AI GATE 2024 that is very useful for upcoming GATE Exam 2024 for Data Science and AI Students.

Data Science is a multidisciplinary field that combines techniques from statistics, computer science, and domain knowledge to extract insights and knowledge from data. It involves the collection, cleaning, analysis, and interpretation of data to solve complex problems and make data-driven decisions. Data scientists use a variety of tools and techniques to uncover patterns, trends, and meaningful information from data.

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think, learn, and problem-solve like humans. AI systems can perform tasks that typically require human intelligence, such as understanding natural language, recognizing patterns, making decisions, and adapting to changing circumstances. AI technologies are at the forefront of technological advancements and have a wide range of applications across various industries

GATE Data Science and Artificial Intelligence Syllabus 2024

Below the given syllabus in the written format and PDF Format

  • Probability and Statistics
  • Linear Algebra
  • Calculus and Optimization
  • Programming, Data Structures, and Algorithms
  • Database Management and Warehousing
  • Machine Learning
  • AI (Artificial Intelligence)
Linear AlgebraVector 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.
Probability and StatisticsCounting (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.
Calculus and OptimizationFunctions of a single variable, limit, continuity and differentiability, Taylor series, maxima and minima, optimisation involving a single variable.
Programming, Data Structures and AlgorithmsProgramming 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: merge sort, quicksort; introduction to graph theory; basic graph algorithms: traversals and shortest path.
Database Management and WarehousingDatabase Management and WarehousingER-model, relational model: relational algebra, tuple calculus, SQL, integrity constraints, normal form, file organisation, indexing, data types, data transformation such as normalisation, discretisation, sampling, compression; data warehouse modelling: schema for multidimensional data models, concept hierarchies, measures: categorisation and computations.
Machine Learning(i) Supervised Learning: regression and classificaƟon problems, simple linear regression, mulƟple linear regression, ridge regression, logisƟc regression, k-nearest neighbour, naive Bayes classifier, linear discriminant analysis, support vector machine, decision trees, bias variance trade-off, cross-validaƟon methods such as leave-one-out (LOO) cross-validaƟon, k-folds crossvalidaƟon, mulƟ-layer perceptron, feed-forward neural network;

(ii) Unsupervised Learning: clustering
algorithms, k-means/k-medoid, hierarchical clustering, top-down, boƩom-up: single-linkage, mulƟplelinkage, dimensionality reducƟon, principal component analysis.
AISearch: informed, uninformed, adversarial; logic, propositional, predicate; reasoning under uncertainty topics – conditional independence representation, exact inference through variable elimination, and approximate inference through sampling

GATE 2024 DA & AI Exam Pattern

Paper NameGATE Data Science and Artificial Intelligence Paper
Paper CodeDA
Type of QuestionsMCQs, MSQs, and NATS
Section General Aptitude + Data Science and AI
GATE DA Paper Marks DistributionGeneral Aptitude: 15 Marks

Data Science and AI Subject Questions: 85 Mark
Negative MarkingFor a 1-mark MCQ, an incorrect answer will result in a deduction of 1/3 mark.

For a 2-mark MCQ, an incorrect answer will lead to a deduction of 2/3 marks.

There is no penalty for incorrect responses to MSQ or NAT questions.

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