GATE 2026 DA Important Topics: Exam Date (OUT), Topic-Wise Weightage, Pattern, and Revision Tips

Graduate Aptitude Test in Engineering 2026 ( GATE )

DeskCalelnderResult - 19 Mar '26

UPASANA
UPASANA PRADHAN
Assistant Manager- Editorial
Updated on Nov 27, 2025 15:42 IST
IIT Guwahati will release the GATE DA syllabus soon on the official website. Check GATE DA syllabus, important topics, books here. A thorough understanding of the syllabus, important GATE DA topics, and their respective weightage is crucial for aspirants aiming to excel in this examination. Read more here

The Graduate Aptitude Test in Engineering (GATE 2026) for Data Science and Artificial Intelligence (DA) syllabus, important topics, high-weightage chapters can be checked here. A thorough understanding of syllabus, important GATE DA topics, and their respective weightage is crucial for aspirants aiming to excel in this examination. Read below for more. 

GATE 2025 DA Important Topics

IIT Guwahati has released the GATE 2026 DA syllabus on official website. Candidates who want to appear for the GATE DA paper must fill out the GATE 2026 application form, which was available on gate2026.iitg.ac.in.

Latest Update: GATE 2026 branch-wise exam schedule released

GATE 2026 DA Marks: Distribution and Weightage

The GATE 2025 DA question paper comprises two sections -

  • General aptitude - 15 marks
  • Subject questions - 85 marks

A total of 100 marks is allotted for the GATE 2026 DA question paper. The question types include Multiple Choice Questions (MCQs), Multiple Select Questions (MSQs), and Numerical Answer Type (NAT) questions. Negative marking applies only to MCQs, with a deduction of 1/3 mark for 1-mark questions and 2/3 mark for 2-mark questions. There is no negative marking for MSQs and NAT questions.

GATE 2026 DA Exam Date

IIT Guwahati will conduct GATE DA exam on February 15 in second shift (2:30 pm to 5:30 pm). Candidates appearing for exam must download their admit card on January 2.

Also Read:

GATE 2026 DA Syllabus- Topics and Weightage

The GATE Data Science and Artificial Intelligence syllabus, as per the GATE 2025 exam, is as follows. Aspirants can also check an approximate weightage section-wise below-

Subjects Approximate Weightage/Marks
Probability and Statistics 16 marks
Linear Algebra 10 marks
Calculus and Optimisation 8 marks
Programming, Data Structures, and Algorithms 21 marks
Database Management and Warehousing 8 marks
Machine Learning 11 marks
Artificial Intelligence 11 marks

GATE 2026 DA Expected Questions (Topic-Wise)

IIT Guwahati conducted the GATE exam in 2018, and looking at the pattern and format of the questions, here are some expected questions that can be asked from following topics covered in the GATE 2026 DA question paper -

Topics

Expected Questions in the following topics

Data Structures and Algorithms

Tree Traversal, Hashing, Stack application, Graph traversal, Time complexities, master theorem, sorting, searching, shortest path, minimum cost spanning tree

Programming in Python

Recursion, List and dictionary comprehension, List, Tuple, set, and dictionary data types, and Scope of variable in a function

Database Management and Warehousing

Checking FD’s, Additional FD’s, Finding Normal Form of the relation, Decomposition properties, Tuple relational calculus, Data normalisation, OLAP operations, Equivalence FD’s, Decomposing the relation, B and B+ tree insertion and deletion, 

Machine Learning

Decision trees -information gain, entropy, K-value selection in KNN, K-means, single linkage clustering in KNN, Model parameters in Naive Bayes, model parameters in ANN, Hyper plane, decision boundary, and linearly non-separable data, distance-weighted KNN, decision trees pruning, cross validation techniques, etc. 

Artificial Intelligence

Iterative deepening search, depth limited search, bi-directional search, rules of inference, tautologies, exact inference through variable elimination, quantifies and multiple quantifiers

GATE 2026 DA Important Formulas (Topic-Wise)

Topics/Subjects Important Formulas to Focus
Probability and Statistics Baye's Theorem, Mean and Variance, Probability and Distributions (Binomial, Poisson, and Normal Distribution)
Linear Algebra Matrix operations, Principal component analysis - (Formula for Eigenvalues of Covariance Matrix)
Calculus and Optimization Gradient Descent Update Rule, Lagrange Multiplier Method
Machine Learning Loss Functions, Naïve Bayes Classifier
Data Structures and Algorithms Time Complexity (Big-O Notation), Dynamic Programming
Artificial Intelligence Minimax Algorithm (Game Theory), Perceptron Rule (Basic Neural Network)

Quick Tips for Memorisation and Problem Solving

  • Revise short notes, mind maps, and cheat sheets for quick concept review.
  • Memorize important formulas, such as Bayes’ Theorem, Eigenvalues, Gradient Descent, etc.
  • Attempt at least 2 to 3 full-length mock tests following real exam guidelines.
  • Attempt 50+ NAT-type questions for numerical accuracy.
  • Follow a 50-40-30 rule during the exam- (50 mins for General aptitude, 40 mins for medium-difficulty questions, and 30 mins for tough questions & review)
  • Get 7-8 hours of sleep before the exam. Avoid starting new topics. Revise what you've read already.
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About the Author
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UPASANA PRADHAN
Assistant Manager- Editorial

Upasana Pradhan is an Mtech graduate in Electronics and Communication Engineering from DTU who specialises in curating postgraduate and undergraduate engineering entrance exam content, such as GATE, TS PGECET, KCET,

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