GATE 2025 DA Important Topics: Check Topic-Wise Weightage, Pattern, Revision Tips and more

Graduate Aptitude Test in Engineering 2025 ( GATE )

DeskCalelnderExam On - 15 Feb '25

UPASANA
UPASANA PRADHAN
Assistant Manager- Editorial
Updated on Feb 5, 2025 14:48 IST

The Graduate Aptitude Test in Engineering (GATE 2025) for Data Science and Artificial Intelligence (DA) is scheduled for February 15, 2025, shift 1 (9:30 am to 12:30 pm). 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 below for more. 

GATE 2025 DA Important Topics

GATE 2025 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 2025 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.

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GATE 2025 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 2025 DA Expected Questions (Topic-Wise)

IIT Roorkee conducted the GATE exam in 2009 and 2017, and looking at the pattern and format of the questions, here are some expected questions that can be asked from the following topics covered in the GATE 2025 DA question paper -

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Topics

Expected Questions in the following topics

Data Structures and Algorithm

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 2025 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 under timed conditions.
  • 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 new topics and focus on confidence-building revision. 
About the Author
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UPASANA PRADHAN
Assistant Manager- Editorial

Upasana has more than six years of experience in the education industry, especially in engineering and medical content articles. She’s an electronics and communication engineering graduate from DTU and covers engine... Read Full Bio

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