GATE DA at a Glance
The newest GATE paper introduced for AI and Data Science enthusiasts.
Paper Code
DA
Paper Name
Data Science & AI
Exam Authority
IISc / IITs (Rotational)
Exam Mode
Computer Based Test (CBT)
Duration
3 Hours (180 Minutes)
Important Dates
Check Latest Official Notification
Who Can Appear for GATE DA?
Standard official eligibility requirements for candidates.
Educational Qualification
Candidates currently in the 3rd or higher years of any undergraduate degree program OR who have already completed any government approved degree program in Engineering / Technology / Architecture / Science / Commerce / Arts are eligible.
Age Limit
There is absolutely no age limit criteria defined for candidates appearing for the GATE examination.
Number of Attempts
There is no restriction on the number of times a candidate can appear for the GATE examination.
GATE DA Exam Pattern
Structure and marking scheme of the Data Science & AI paper.
| Section | Total Questions | Total Marks | Weightage |
|---|---|---|---|
| General Aptitude (GA) | 10 Questions | 15 Marks | 15% |
| Core Data Science & AI | 55 Questions | 85 Marks | 85% |
| Total | 65 Questions | 100 Marks | 100% |
GATE DA Syllabus
Official subject-wise topic breakdown for Data Science & AI.
01 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.
02 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.
03 Calculus and Optimization
Functions of a single variable, limit, continuity and differentiability, Taylor series, maxima and minima, optimization involving a single variable.
04 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.
05 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 and discretization, data warehouse and OLAP, Data cube.
06 Machine Learning
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-one-out (LOO) cross-validation, k-folds cross-validation, multi-layer perceptron, feed-forward neural network; Unsupervised Learning: clustering algorithms, k-means/k-medoid, hierarchical clustering, top-down, bottom-up: single-linkage, multiple-linkage, dimensionality reduction, principal component analysis.
07 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.
Master Every GATE DA Subject
Focus on chapter-wise preparation and concept building.
Math Foundations
Linear Algebra and Probability form the mathematical backbone of DA. Master these before studying ML algorithms.
Practice Topics →Machine Learning
The highest weightage subject. Focus on the mathematical derivations and assumptions of Regression, SVMs, and Clustering algorithms.
Practice Topics →Programming & DSA
Unlike CS (which uses C), DA tests Programming in Python. Focus on Python specifics along with standard Algorithms.
Practice Topics →How to Prepare for GATE DA
A structured approach specifically tailored for Data Science & AI.
1 Math First Approach
Do not start with Machine Learning or AI directly. Spend your first 2 months strictly on Linear Algebra, Probability, Statistics, and Calculus. ML algorithms are simply applied versions of these mathematical concepts.
2 Python & DBMS Overlap
If you are a CS student giving DA as a second paper, you already know DSA and DBMS. However, you must specifically practice Python syntax and Data Warehousing (OLAP) which are unique to the DA syllabus.
3 Focus on Fundamentals, Not Code
GATE will test the mathematics behind ML (e.g., calculating entropy for Decision Trees, or finding eigenvectors for PCA), not writing scikit-learn code. Study from standard textbooks like ISLR or Bishop.