{"id":21064,"date":"2026-06-12T16:05:31","date_gmt":"2026-06-12T10:35:31","guid":{"rendered":"https:\/\/blog.madeeasy.in\/?p=21064"},"modified":"2026-05-18T18:46:50","modified_gmt":"2026-05-18T13:16:50","slug":"how-to-prepare-for-gate-data-science-and-ai-da","status":"publish","type":"post","link":"https:\/\/www.madeeasy.in\/blog\/how-to-prepare-for-gate-data-science-and-ai-da","title":{"rendered":"How to prepare for GATE Data Science and Artificial Intelligence (DA)?"},"content":{"rendered":"

Cracking the GATE DA 2027 exam requires a smart plan. The GATE Data Science and Artificial Intelligence paper is one of the fastest growing papers. Whether you are looking for an MTech seat at IIT or a PSU job, understanding the syllabus is the first step.\u00a0<\/span><\/p>\n

The GATE Data Science and Artificial Intelligence (DA) paper was introduced in the GATE exam in 2024 by the Indian Institute of Science, Bangalore. This new paper is for students who want to build careers in Data Science, Machine Learning, and Artificial Intelligence.<\/span><\/p>\n

So, if you are someone who is aiming for <\/span>GATE 2027<\/b><\/a> with the DA paper, this is the right time to start your preparation. With the competition increasing every year, early preparation helps to score well in the exam.<\/span><\/p>\n

So, in this blog, we will help you plan your preparation for <\/span>GATE Data Science and Artificial Intelligence<\/b><\/a>. Because good planning makes all the difference.\u00a0<\/span><\/p>\n

GATE 2027 Important Highlights\u00a0<\/b><\/h2>\n

The official dates for the GATE 2027 exams are not out as of yet. So, aspirants must keep an eye on the IIT Madras GATE official website for exam updates. Let us see some important highlights of GATE 2027 exam from the table below:\u00a0<\/span><\/p>\n

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

Particulars<\/b><\/p>\n<\/th>\n

\n

Details\u00a0<\/b><\/p>\n<\/th>\n<\/tr>\n

\n

Full Name<\/strong>\u00a0<\/span><\/p>\n<\/td>\n

Graduate Aptitude Test in Engineering\u00a0<\/span><\/td>\n<\/tr>\n
Conducting Body<\/strong>\u00a0<\/span><\/td>\n\n

IIT Madras\u00a0<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

Exam Date<\/strong>\u00a0<\/span><\/p>\n<\/td>\n

February 2027 (tentative)<\/span><\/td>\n<\/tr>\n
Notification Date<\/strong>\u00a0<\/span><\/td>\n\n

July 2026 (expected)\u00a0<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

Application Window\u00a0<\/strong><\/p>\n<\/td>\n

Aug – Sept 2026 (tentative)<\/span><\/td>\n<\/tr>\n
Mode\u00a0<\/strong><\/td>\n\n

Online (Computer Based Test)<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

Question Types\u00a0<\/strong><\/p>\n<\/td>\n

MCQ, MSQ and Numerical Answer Test<\/span><\/td>\n<\/tr>\n
Number of Papers\u00a0<\/strong><\/td>\n\n

Tentatively 30 branches of Engineering and science\u00a0<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

Eligibility\u00a0<\/strong><\/p>\n<\/td>\n

Relaxed to 10+2+3 (ongoing)- 3 rd year undergraduate students can apply<\/span><\/td>\n<\/tr>\n
Age Limit\u00a0<\/strong><\/td>\n\n

No age limit\u00a0<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

Purpose\u00a0<\/strong><\/p>\n<\/td>\n

\n

Admission to MTech\/ME\/PhD at IITs, NITs, IIITs. Also used for PSU recruitment.\u00a0<\/span><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

Syllabus of Data Science and Artificial Intelligence<\/strong><\/h2>\n

GATE on their official website has uploaded the syllabus pdf briefly mentioning the topics, which somewhere eases the students to filter out the topics to prepare for.<\/p>\n

\n\n\n\n\n\n\n\n\n\n\n
GATE New Test Paper on (DA) Data Science and Artificial Intelligence Syllabus <\/strong><\/td>\n<\/tr>\n
Probability and Statistics<\/strong><\/td>\nCounting (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.<\/td>\n<\/tr>\n
Linear Algebra<\/strong><\/td>\nVector 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.<\/td>\n<\/tr>\n
Calculus and Optimization<\/strong><\/td>\nFunctions of a single variable, limit, continuity and differentiability, Taylor series, maxima and minima, optimization involving a single variable.<\/td>\n<\/tr>\n
Programming, Data Structures and Algorithms<\/strong><\/td>\nProgramming 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.<\/td>\n<\/tr>\n
Database Management and Warehousing<\/strong><\/td>\nER-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.<\/td>\n<\/tr>\n
Machine Learning<\/strong><\/td>\n\n
    \n
  1. \n
      \n
    1. Supervised Learning:<\/strong> 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;<\/li>\n
    2. Unsupervised Learning:<\/strong> clustering algorithms, k-means\/k-medoid, hierarchical clustering, top-down, bottom-up: single-linkage, multiple linkage, dimensionality reduction, principal component analysis.<\/li>\n<\/ol>\n<\/li>\n<\/ol>\n<\/td>\n<\/tr>\n
AI<\/strong><\/td>\nSearch: informed, uninformed, adversarial; logic, propositional, predicate; reasoning under uncertainty topics – conditional independence representation, exact inference through variable elimination, and approximate inference through sampling.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

GATE Data Science and Artificial Intelligence Preparation Plan<\/b><\/h2>\n

A proper preparation plan is the key to cracking GATE 2027 with subjects like DA, with a good score. Since the syllabus includes multiple technical subjects along with Mathematics and Aptitude, having a structured study plan helps you cover all topics effectively and manage your time wisely. So, now, we will explore a complete preparation plan to help you smartly prepare for GATE DA.<\/span><\/p>\n