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Most Scoring Topics in GATE 2026 Data Analytics (DA) – High-Weightage Areas

Most Scoring Topics in GATE 2026 Data Analytics (DA) – High-Weightage Areas

Edited By Shivani Poonia | Updated on Aug 12, 2025 03:11 PM IST | #GATE
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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!

Most Scoring Topics in GATE 2026 Data Analytics (DA) – High-Weightage Areas
Most Scoring Topics in GATE 2026 Data Analytics (DA) – High-Weightage Areas

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

High Scoring Topics In DA For GATE 2026

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

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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

Insights from the topic distribution (above table):

  1. 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.

  2. Right behind them are Programming, Data Structures and Algorithms and Aptitude with 22 questions and 19 questions respectively.

  3. In Machine Learning, Linera separability of datasets is mentioned 3 times which is a sign of acute focus towards such a concept.

  4. 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).

  5. 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.

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Frequently Asked Questions (FAQs)

1. Which chapter has the highest weightage in GATE DA?

Probability and Statistics is given the most weightage as it only contributes to about 20-25% of the paper.

2. Are Machine Learning topics like clustering and linear separability important?

Yes, they come up very commonly, namely when it comes to linear separability, k-means, and decision trees.

3. How significant is Linear Algebra in the DA syllabus?

More than a few questions are answered with reference to concepts of Linear Algebra, such as eigenvalues, projection matrices, and vector subspaces.

4. What is the balance between AI/ML and the core mathematics in the paper?

It is math-intensive, with Probability, Statistics, and Linear Algebra leading by far, and AI/ML adding a small, but still concentrated contribution.

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Questions related to GATE

Have a question related to GATE ?

Yes, in the GATE exam you can choose any paper/stream, regardless of your B.Tech or graduation branch there is no strict restriction. However,its recommended that should pick a paper in which you can perform well and which is accepted for the M.Tech or PSU roles you’re targeting. For example, a Mechanical Engineering graduate can appear for Computer Science, but admission eligibility for some M.Tech programs or PSU jobs may still require the original degree branch to match. So, while GATE lets you choose freely, your post-exam opportunities will depend on the institute’s or recruiter’s criteria.

Yes for sure if you graduate from ISI with a B.Math degree and clear GATE in Computer Science, you can apply for M.Tech programs in CSE-related fields at IISc and IITs. Most of these institutes accept candidates from any undergraduate discipline as long as they have a valid GATE CS score. However, some specializations or departments may have additional eligibility requirements, like prior coursework in programming, algorithms, or discrete mathematics. Since your B.Math background gives you a strong theoretical foundation, you’ll likely meet the academic criteria, but it’s best to check each IIT/IISc department’s brochure before applying.

Hello,

Yes, you can appear for the GATE, or Graduate Aptitude Test in Engineering. Through this exam you can get admission to prestigious institutions like IITs and N for the M.Tech or Ph.D. program. After this exam you can even open doors for jobs in the public sector.

I hope it will clear your query!!


To prepare for GATE EE 2026, you need to follow a simple and smart study plan. First, understand the exam pattern. GATE EE has three parts. General Aptitude is for 15 marks, Engineering Mathematics is for around 13 marks, and the core Electrical Engineering subjects cover 72 marks.

Start by reading the official syllabus so you know what topics are there. Make a proper timetable. Divide your study time for each subject. Give more time to the subjects that you find difficult.

Use standard books for each subject. For example, you can study Electrical Machines from P.S. Bimbhra, Power Systems from C.L. Wadhwa, Control Systems from Nagrath and Gopal, and Signals and Systems from Oppenheim. For Aptitude, you can use RS Aggarwal or any GATE-specific book.

Start your preparation with General Aptitude and Engineering Mathematics because they are easy to score and will boost your marks. Then focus on the core subjects one by one. Always make notes while studying. These notes will help you revise quickly later.

Solve previous year GATE question papers. This will help you understand what kind of questions are asked. Also, take mock tests and topic-wise tests regularly. This will improve your speed and accuracy.

Make a habit of revising everything you study. Write down important formulas and keep reading them again and again. In the last three months before the exam, start giving full-length mock tests every week. Check your mistakes and try not to repeat them.

Be regular with your studies. Even if you study for a few hours daily, it will make a big difference. Stay focused and believe in yourself. You can do well if you are consistent.

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For more information, click on the link given below,

https://engineering.careers360.com/articles/ap-pgecet-seat-allotment

Regards

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