GATE Data Science & Artificial Intelligence (DA)

Complete guide to GATE DA syllabus, topics, and preparation strategy.

Overview

GATE DA (Data Science and Artificial Intelligence) is one of the newer GATE papers, introduced in 2024. It is designed for students and professionals in data science, machine learning, and AI fields. A strong score opens doors to M.Tech and research programs in AI/ML at IITs and IISc, as well as data roles in select PSUs.

The paper consists of 65 questions worth 100 marks: 10 questions (15 marks) from General Aptitude and 55 questions (85 marks) from the DA syllabus. The exam is 3 hours long.

Syllabus by Section

Below is the complete GATE Data Science & Artificial Intelligence syllabus with approximate marks weightage based on historical papers.

Probability and Statistics

~20 marks
  • Counting (permutation and combinations), probability axioms, sample space, independent and mutually exclusive events
  • Marginal, conditional, and joint probability; Bayes Theorem
  • Mean, median, mode, standard deviation, correlation, covariance
  • Discrete distributions: uniform, Bernoulli, binomial; continuous distributions: uniform, exponential, Poisson, normal, t-distribution, chi-squared
  • Cumulative distribution function, conditional PDF, Central Limit Theorem
  • Confidence intervals, z-test, t-test, chi-squared test

Linear Algebra

~11 marks
  • Vector spaces, subspaces, linear dependence and independence
  • Matrix algebra, projection matrix, orthogonal matrix, idempotent matrix
  • Systems of linear equations, Gaussian elimination
  • Eigenvalues and eigenvectors, determinant, rank, nullity
  • LU decomposition, singular value decomposition (SVD)

Calculus and Optimization

~7 marks
  • Functions of a single variable, limits, continuity, differentiability
  • Taylor series, maxima and minima
  • Optimization involving a single variable

Programming, Data Structures and Algorithms

~12 marks
  • Programming in Python
  • Basic data structures: stacks, queues, linked lists, trees, hash tables
  • Search algorithms: linear search, binary search
  • Sorting algorithms: selection sort, bubble sort, insertion sort
  • Divide and conquer: mergesort, quicksort
  • Introduction to graph theory; BFS and DFS traversals, shortest path algorithms

Database Management and Warehousing

~9 marks
  • ER model, relational model, relational algebra, tuple calculus, SQL
  • Integrity constraints, normal forms, file organization, indexing
  • Data transformation: normalization, discretization, sampling, compression
  • Data warehouse modelling: multidimensional schemas, concept hierarchies, measures

Machine Learning

~23 marks
  • Supervised learning: simple and 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 (LOO and k-folds)
  • Multi-layer perceptron, feed-forward neural network
  • Unsupervised learning: k-means, k-medoid, hierarchical clustering
  • Dimensionality reduction: principal component analysis (PCA)

Artificial Intelligence

~8 marks
  • Search: informed (A*, heuristics), uninformed (BFS, DFS), adversarial (minimax)
  • Logic: propositional logic, predicate logic
  • Reasoning under uncertainty: conditional independence, exact inference via variable elimination, approximate inference via sampling

Available Practice Papers

All papers below are available on Deep Prep with the full exam simulator.

GATE Data 2025

Single session

1 set available

GATE Data 2024

Single session

1 set available

Recommended Books

Machine Learning

Pattern Recognition and Machine Learning — Christopher M. Bishop

Linear Algebra

Introduction to Linear Algebra — Gilbert Strang

Probability & Statistics

A First Course in Probability — Sheldon Ross

Algorithms & DSA

Introduction to Algorithms — Cormen, Leiserson, Rivest, Stein (CLRS)

Database Systems

Fundamentals of Database Systems — Elmasri & Navathe

Artificial Intelligence

Artificial Intelligence: A Modern Approach — Russell & Norvig

Tips for GATE Data Science & Artificial Intelligence

  1. Prioritize Machine Learning and Probability — These two sections together account for roughly 40–50% of subject marks. Build intuition for distributions, hypothesis testing, and core ML algorithms from first principles before memorizing formulas.
  2. Code while you study — DA is unique in requiring Python proficiency. Implement ML algorithms (k-means, kNN, decision tree) in Python from scratch; this solidifies both the algorithm and the programming sections simultaneously.
  3. Do not neglect Linear Algebra — SVD, eigenvalues, and PCA bridge multiple sections. Mastering these connects several high-weightage topics at once.
  4. Practice SQL actively — GATE DA consistently includes SQL query and relational algebra questions. Practice queries involving joins, aggregations, and normalization rather than just reading theory.
  5. Supplement with GATE CS questions — DA has been offered only since 2024, so fewer previous year questions exist. Supplement with GATE CS questions on DSA and DBMS to build problem-solving depth.