GATE 2026 DA Important Topics: Exam Date (OUT), Topic-Wise Weightage, Pattern, and Revision Tips
Graduate Aptitude Test in Engineering 2027 ( GATE )
GATE 2027 Registration Last Date without late fee
27 Sep '26
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.
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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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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