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Most Scoring Topics in DA for GATE 2026 - The GATE Data Science and Artificial Intelligence (DA) 2026 paper is specially designed for candidates aiming for higher studies or careers in AI, machine learning and data driven technologies. This paper, introduced in 2024, is also part of the GATE 2026 exam to be conducted by IIT Guwahati and is scheduled around February 2026. For effective preparation, candidates are looking for the important topics as per past years DA papers. Identifying the most scoring topics in DA for GATE 2026 can highly improve your preparation. This breakdown highlights which areas, like Probability, Statistics, Linear Algebra, or Machine Learning, carry more marks, so you can focus your efforts where they matter most and plan a balanced study strategy for the GATE 2026 exam. Let’s dive in!
Before jumping into the topics, let's understand the GATE exam in a little more detailed manner:
Feature | Details |
Exam Mode | Computer-Based Test (CBT) |
Duration | 3 Hours |
Total Questions | 65 |
Total Marks | 100 |
Question Types | MCQ (Multiple Choice), MSQ (Multiple Select), NAT (Numerical Answer) |
Sections | General Aptitude (15%), Engineering Mathematics (CSE) / Maths (DA), Core Subject |
Marking Scheme | 1 or 2 marks per question; negative marking for MCQs only |
Let’s begin with looking at the chapter-wise marks distribution of the average of the last 2 years as GATE DA is a competitively newer subject that was added in the year 2024. As you know, focusing on the most scoring topics in DA for GATE 2026 helps you maximize marks in limited time. Therefore, based on these dates these are GATE 2026 DA important chapters:
Chapter Name | 2025 | 2024 | Total | Percentage Distribution |
Aptitude | 10 | 10 | 20 | 15.38% |
Artificial Intelligence (AI) | 4 | 7 | 11 | 8.46% |
Calculus and Optimization | 6 | 5 | 11 | 8.46% |
Database Management and Warehousing | 7 | 4 | 11 | 8.46% |
Linear Algebra | 8 | 6 | 14 | 10.77% |
Machine Learning | 9 | 10 | 19 | 14.62% |
Probability and Statistics | 12 | 10 | 22 | 16.92% |
Programming, Data Structures and Algorithms | 9 | 13 | 22 | 16.92% |
Total | 65 | 65 | 130 | 100.00% |
As per the table, Probability and Statistics seem to have the highest weightage along with Programming and Algorithms. Well, this was the overall chapter-wise weightage and now it's time to dive into GATE DA 2025 important topics along with GATE DA 2024 important topics. Mastering the most scoring topics in DA for GATE 2026 ensures better accuracy and faster problem-solving. Since only 2 years have this distribution, we will look at the high scoring topics in the DA for GATE 2026:
Knowing the most scoring topics in DA for GATE 2026 allows you to prioritize high-yield concepts during revision. Please Note: Exams have taken place only twice, therefore to get an understanding of the high scoring topics in DA for GATE 2026 we have combined the following:
GATE DA 2025 important topics and chapters
GATE DA 2024 important topics and chapters
The most scoring topics in DA for GATE 2026 usually include areas with predictable question patterns.
Chapter Name | Topic Name (Specific Subtopic) | Count |
Aptitude | Dice folding and visualization | 2 |
Geometry – cross-section visualization | 2 | |
Graph coloring (minimum colors) | 2 | |
Inference from passage | 2 | |
Infinite series sum | 2 | |
Permutations – divisibility | 1 | |
Permutations – Divisibility rule | 1 | |
Pie chart – percentage calculation | 2 | |
Probability of combinations (girls/boys) | 1 | |
Profit/Interest calculation (returns) | 2 | |
Verbal analogy | 2 | |
Aptitude Total | 19 | |
Artificial Intelligence | AI – Adversarial search (alpha-beta pruning) | 1 |
AI – Heuristic admissibility (h1, h2) | 1 | |
AI – Search strategy (A*) and heuristic admissibility | 1 | |
Alpha-beta pruning in adversarial search | 1 | |
Bayesian network – conditional independence | 1 | |
Bayesian network – joint probability computation | 1 | |
BFS vs DFS – state expansion count | 1 | |
BFS vs DFS – state space expansion | 1 | |
Logic representation – rugby and round balls | 1 | |
Neural network – weight equivalence | 1 | |
Propositional logic – tautology identification | 1 | |
Artificial Intelligence Total | 11 | |
Calculus and Optimization | Function continuity and differentiability (piecewise) | 1 |
Limits and logarithmic expansion | 1 | |
Limits and logarithmic expansions | 1 | |
Local maxima/minima (quartic polynomial) | 1 | |
Local maxima/minima of quartic polynomial | 1 | |
Logistic function derivative (0.4 value) | 1 | |
Optimization – function continuity and differentiability | 1 | |
Optimization – local minima (2nd derivative test) | 2 | |
Optimization – Taylor series and limits | 1 | |
Calculus and Optimization Total | 10 | |
Database Management and Warehousing | ER model – relational schema (DB constraints) | 1 |
Functional dependencies (DB) | 1 | |
Functional dependencies (derivable attributes) | 1 | |
Normalization & z-score | 1 | |
Relational algebra – ensuring team members in defender/forward | 1 | |
Relational algebra – set operations (Team/Defender) | 1 | |
SQL – Index optimization (hash vs B+) | 1 | |
SQL indexing optimization (hash vs B+) | 1 | |
SQL query result count (joins with conditions) | 1 | |
Database Management and Warehousing Total | 9 | |
Linear Algebra | Determinant of M2+12MM^2+12M | 1 |
Eigenvalues and matrix properties | 1 | |
Eigenvalues and signs of matrix | 1 | |
Eigenvalues of matrices | 1 | |
Eigenvalues, determinant and matrix property | 1 | |
Matrix rank and nullity (subspaces) | 1 | |
Matrix solution scenarios (unique/infinite/none) | 1 | |
Matrix solutions (unique/infinite/no solutions) | 1 | |
Projection matrix properties | 2 | |
Python recursion & tree traversal | 1 | |
Singular values and sum | 1 | |
Singular values and their sum (SVD) | 1 | |
Subspaces of R3R^3 | 1 | |
Subspaces of R3R^3R3 | 1 | |
Vector subspace properties | 1 | |
Linear Algebra Total | 16 | |
Machine Learning | Clustering – single linkage algorithm | 2 |
Decision tree – Information gain (entropy) | 2 | |
Fisher Linear Discriminant (between/within scatter matrices) | 1 | |
k-means clustering – point assignment | 1 | |
k-means clustering properties | 2 | |
k-NN classifier (minimum k for classification) | 1 | |
ML – Linear separability (2D datasets) | 1 | |
ML – Linear separability of datasets | 3 | |
Naive Bayes – number of parameters estimation | 1 | |
Neural network – weight equivalence (ReLU) | 1 | |
PCA, Naive Bayes, Logistic regression (classification of models) | 1 | |
SVM – support vectors | 1 | |
Machine Learning Total | 17 | |
Probability and Statistics | Binary search recurrence relation | 1 |
Covariance between random variables | 1 | |
Dynamic programming (prefix computation) | 1 | |
Expected throws until two consecutive even outcomes | 1 | |
Logic – Propositional representation (balls/rugby) | 1 | |
Poisson distribution & Normal distribution properties | 2 | |
Probability – Bayes theorem | 2 | |
Probability – conditional expectation and variance | 1 | |
Probability – conditional/joint events | 3 | |
Probability – event intersection (T ∩ S) | 1 | |
Probability – expected value (die throws) | 1 | |
Probability – exponential distribution parameter | 2 | |
Probability – joint PDF and expectation | 2 | |
Probability – uniform distribution (X,Y) | 1 | |
Probability – uniform distributions | 1 | |
Probability – z-score normalization | 1 | |
Probability of combinations (girls/boys) | 1 | |
Python list reverse (recursion) | 1 | |
Sample mean update with new data | 1 | |
Sorting algorithms – bubble/insertion/selection passes | 1 | |
Probability and Statistics Total | 26 | |
Programming, Data Structures and Algorithms | AI – Heuristic admissibility (h1, h2) | 1 |
Array prefix computation (dynamic programming) | 1 | |
Bayesian network joint probability | 1 | |
Binary search comparisons recurrence | 1 | |
Binary search complexity analysis | 1 | |
Binary tree node relationships (height, leaves) | 1 | |
Binary tree properties (height, nodes) | 1 | |
Covariance between random variables | 1 | |
DFS edge classification (tree/cross/back) | 2 | |
Double-ended queue operations (insert/remove) | 1 | |
k-NN classifier (minimum k for classification) | 1 | |
Python list reverse using recursion | 1 | |
Python recursion – counting tree nodes | 1 | |
Quicksort – swaps count | 1 | |
Relational algebra – SQL tuple verification | 1 | |
Sorting algorithms – bubble/insertion/selection passes | 1 | |
Stack vs queue vs hash table (matching) | 1 | |
Topological sort of DAG | 1 | |
Topological sorting in DAG | 1 | |
Tree traversal combinations (preorder/inorder/postorder) | 1 | |
Uniform hashing – expected probes | 1 | |
Programming, Data Structures and Algorithms Total | 22 | |
Grand Total | Combination of both 2025 and 2024 GATE question paper | 130 |
Probability and Statistics is the number one chapter with 26 questions; therefore it is the most important chapter to study. A strategic study plan around the most scoring topics in DA for GATE 2026 can boost your overall rank.
Right behind them are Programming, Data Structures and Algorithms and Aptitude with 22 questions and 19 questions respectively.
In Machine Learning, Linera separability of datasets is mentioned 3 times which is a sign of acute focus towards such a concept.
The topics of visualization and analytical reasoning are centered on such topics as dice folding, cross-sections and geometry, and coloring of graphs (2 appearances each).
The probability subtopics (conditional/joint events, bayes theorem, exponential distribution) are repeated twice, and they are a reminder of common testing on different probability models.
Frequently Asked Questions (FAQs)
It is math-intensive, with Probability, Statistics, and Linear Algebra leading by far, and AI/ML adding a small, but still concentrated contribution.
More than a few questions are answered with reference to concepts of Linear Algebra, such as eigenvalues, projection matrices, and vector subspaces.
Yes, they come up very commonly, namely when it comes to linear separability, k-means, and decision trees.
Probability and Statistics is given the most weightage as it only contributes to about 20-25% of the paper.
On Question asked by student community
Hello,
Yes, you can change your category from General to OBC during the GATE 2026 correction window.
GATE always allows candidates to correct details like category. You will need to pay a small correction fee for this change. Make sure you upload a valid OBC certificate as per the format and date mentioned by GATE.
So, wait for the correction window, log in to your application, and update your category to OBC.
Hope it helps !
Hello! If you are preparing for GATE, there are plenty of free resources available online that can boost your preparation. You can easily access e-books, handwritten notes, subject wise PYQs, and test series shared by toppers on different platforms. Along with that, free PDFs and study material are available through various websites and Telegram channels. To make it easier, I will be attaching the link where you can find preparation tips, a study timetable, free study materials, and previous year question papers all in one place.
https://engineering.careers360.com/articles/gate-preparation-timetable
https://engineering.careers360.com/articles/how-to-prepare-for-gate
https://engineering.careers360.com/articles/gate-preparation-material
https://engineering.careers360.com/articles/gate-mock-test
https://engineering.careers360.com/articles/gate-question-papers
Hello, with an AIR of 25070 and a score of 499 in NEET PG 2025 under the General category, getting a government seat in West Bengal for highly demanded branches like General Surgery or Orthopedics may be difficult, as these usually close at much higher ranks. However, you still stand a chance for other clinical or para-clinical branches through the state quota counselling. I suggest using the NEET PG college predictor link I’m sharing here, which will give you a clearer idea of the colleges and branches you may get based on your rank and category.
NEET PG College Predictor-
https://medicine.careers360.com/neet-pg-college-predictor
Hello,
You can download GATE Previous Year Question Papers either from the official website of GATE 2026, or from Careers360. Here are the links for both :
Official Website : Download GATE Previous Papers (https://gate2025.iitr.ac.in/download.html)
Careers360 : GATE Question Papers with Solutions
Hope it helps !
To take GATE mock tests, first choose a reliable source like GATE official portal, NPTEL, Made Easy, or ACE Engineering. Sign up or register online to access the full-length tests. Simulate the actual 3-hour exam with proper timing and no distractions. Attempt complete tests to practice time management and exam strategy. After each test, analyze your performance to identify weak areas and revise. Regularly taking 1–2 mocks per week helps improve speed, accuracy, and confidence.
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