GATE Data Science and Artificial Intelligence

Syllabus

GATE Syllabus

CAT Syllabus

GATE vs CAT Syllabus

Module: Probability and Statistics

1. Counting (permutation and combinations),
2. Probability axioms,
3. Sample space,
4. Events, independent events, mutually exclusive events,
5. Marginal, conditional and joint probability,
6. Bayes Theorem,
7. Conditional expectation and variance, mean, median, mode and standard deviation,
8. Correlation, and covariance,
9. Random variables, discrete random variables and probability mass functions,
10. Uniform, Bernoulli, binomial distribution,
11. Continuous random variables
12. Probability distribution function,
    • uniform,
    • exponential,
    • Poisson,
    • Normal,
    • Standard normal,
    • t-distribution,
    • chi-squared distributions,
    • cumulative distribution function,
    • Conditional PDF,
13. Central limit theorem, confidence interval, z-test, t-test, chi-squared test.

Module: Linear Algebra

1. Vector space, subspaces,
2. Linear dependence and independence of vectors,
3. Matrices,
   • projection matrix,
   • orthogonal matrix,
   • idempotent matrix,
   • partition matrix and their properties,
4. quadratic forms,
5. Systems of linear equations and solutions; Gaussian elimination,
6. eigenvalues and eigenvectors,
7. determinant,
8. rank,
9. nullity,
10. projections,
11. LU decomposition,
12. singular value decomposition

Module: Calculus and Optimization

1. Functions of a single variable,
2. limit,
3. continuity and differentiability,
4. Taylor series,
5. maxima and minima,
6. optimization involving a single variable

Module : Programming, Data Structures and Algorithms:

1. Programming in Python,
2. Basic data structures:
   • stacks,
   • queues,
   • linked lists,
   • trees,
   • hash tables;
3. Search algorithms:
   • linear search
   • binary search,
4. Basic sorting algorithms:
   • selection sort,
   • bubble sort and insertion sort;
5. Divide and conquer:
   • mergesort,
   • quicksort;
6. Introduction to graph theory;
   • basic graph algorithms: traversals and shortest path.

Module: Database Management and Warehousing

1. ER-model,
2. Relational model: relational algebra,
3. Tuple calculus,
4. SQL,
5. Integrity constraints,
6. Normal form,
7. File organization, indexing,
8. Data types,
9. Data transformation
   • Normalization,
   • Discretization,
   • Sampling,
   • Compression;
10. Data warehouse modelling:
    • Schema for multidimensional data models,
    • Concept hierarchies,
    • Measures: categorization and computations

Module: Artificial Intelligence

1. Introduction
2. Search:
   • Informed,
   • Uninformed,
   • Adversarial.
3. Logic,
   • Propositional,
   • Predicate.
4. Reasoning under uncertainty
   • Conditional independence representation,
   • Exact inference through variable elimination,
   • Approximate inference through sampling

Module: Machine Learning

1. Introduction
2. 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,
   • biasvariance trade-off,
   • cross-validation
     • Leave-one-out (LOO) cross-validation,
     • k-folds cross-validation,
   • Feed-forward neural network;
3. Unsupervised Learning:
   • Multi-layer perceptron,
   • Clustering algorithms,
   • k-means/k-medoid,
   • Hierarchical clustering,
   • Top-down,
   • Bottom-up:
   • Single linkage,
   • Multiple-linkage,
   • Dimensionality reduction,
   • Principal component analysis

Reference Web Resources

1. GitHub – DS-AI-GATE/dsai-gate: A Repository consisting resources primarily of the Gate DA and AI
2. Your Ultimate Study Guide for GATE 2025 in DS & AI
3. Machine Learning Tutorial – GeeksforGeeks
4. 100 Days of GATE Data Science and AI – A Complete Guide For Beginners – GeeksforGeeks
5. GATE DA 2025 Online Test Series [FREE]
6. How to prepare for GATE Data Science and Artificial Intelligence?
7. For all GATE Data Science & Artificial Intelligence preparing students, hope this PPT will be useful. Contents taken from various sources.
8. GATE-2025 : Data Science and Artificial Intelligence eBook : Kumar, Dr. Rajesh: Amazon.in: Kindle Store
9. GATE Data Science And Artificial Intelligence Syllabus 2025 PDF

Video Resources

1. Best Book For GATE Data Science And AI | GATE Wallah

Previous GATE Question Papers

1. DSAI_GATE_Sample_Question_Paper
2. GATE 2024 Question paper
3. GATE 2024 Answer Key
4. GATE 2025 Question Paper
5. GATE 2025 Answer Key
6. GATE CSE Question Papers