GATE 2025 Syllabus Released; check Data Science and Artificial Intelligence (DA) Syllabus

IIT Roorkee has recently launched a new website for the GATE 2025 exam. The GATE 2025 syllabus, Exam pattern, and other important information have been released on the GATE 2025 website. Candidates can check GATE 2025 DATA SCIENCE AND ARTIFICIAL INTELLIGENCE syllabus here:

Indian Institute of Technology (IIT) Roorkee has launched the official GATE 2025 website. However, the GATE 2025 information brochure is yet to be released by authorities, GATE 2025 Brochure contains all important information related to the GATE 2025 exam.

GATE 2025 registration will start soon, most likely from the last week of August 2024. Candidates should read thoroughly the GATE 2025 syllabus before filling the application form for GATE 2025 exam. There is a growing demand for Data Science and Artificial Intelligence paper among the GATE candidates. In GATE 2024, 52493 Candidates registered for the GATE 2024 DA paper even though the DA paper was added in the GATE examination for the first time.

The GATE 2025 DA syllabus has been released on the official GATE 2025 website. Candidates can check the GATE DA syllabus 2025 here;

GATE 2025 DA Syllabus

There are 7 sections of the GATE 2025 DA paper namely

1. Probability and Statistics
2. Linear Algebra
3. Calculus and Optimization
4. Programming, Data Structures and Algorithms
5. Database Management and Warehousing
6. Machine Learning
7. Artificial Intelligence

Check detailed section-wise GATE 2025 DA syllabus;

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

For more details visit official GATE 2025 website: Click here

Read more: 

IIT Roorkee launched GATE 2025 website; Check GATE 2025 eligibility criteria and more

GATE 2025 Website launched, Application likely to begin in August; Check Exam Pattern

IIT Roorkee Launches Official GATE 2025 Website; visit gate2025.iitr.ac.in