Preparing for GATE 2026 in Data Science & AI? You’re entering one of the most competitive yet rewarding specializations in GATE—and unlike older papers, you won’t find dozens of previous year questions to guide you. That makes understanding the official syllabus absolutely critical to your success.
The GATE Data Science and Artificial Intelligence Syllabus 2026 is your definitive roadmap. This guide breaks down every topic you need to master, reveals the exam pattern, explains the marking scheme, and shows you exactly where to focus your energy for maximum marks.
Whether you’re a final-year student or a working professional aiming for a career shift, this complete breakdown will help you build a strategic study plan that works.
Why the GATE DS & AI Syllabus 2026 Demands Your Attention
Introduced in 2024, the GATE Data Science and AI exam pattern addresses India’s growing demand for skilled AI and ML professionals. But here’s the challenge: it’s still relatively new. Limited past papers mean you can’t rely on trend analysis or question repetition patterns like candidates in older branches.
This makes the official syllabus your primary asset.
Understanding the gate ds and ai syllabus 2026 helps you:
- Build strong foundations in mathematics, programming, and core AI/ML concepts
- Identify high-weightage topics like Machine Learning and Database Management
- Avoid wasting time on irrelevant or low-priority areas
- Structure a study plan that balances depth with exam strategy
Note: The GATE 2026 exam will be conducted by IIT Guwahati as the organizing institute. While the core structure remains consistent with 2025, always verify any updates from the official GATE portal.
Download Official GATE Data Science and AI Syllabus 2026 (PDF)
You can download the complete GATE 2026 data science syllabus PDF directly from the official website:
The PDF contains the exact topics as published by IIT Guwahati and should be your primary reference throughout your preparation.
GATE Data Science and Artificial Intelligence Exam Pattern 2026
Before diving into topics, let’s understand how the exam is structured:
| Exam Details | Specification |
|---|---|
| Exam Mode | Computer-Based Test (CBT) |
| Total Duration | 3 Hours |
| Total Questions | 65 |
| Total Marks | 100 |
| General Aptitude | 10 Questions, 15 Marks |
| Core Subjects | 55 Questions, 85 Marks |
| Negative Marking | Applies only to MCQs: ⅓ mark for 1-mark MCQs, ⅔ mark for 2-mark MCQs |
Question Type Breakdown
The exam includes three types of questions:
- Multiple Choice Questions (MCQs) – with negative marking
- Multiple Select Questions (MSQs) – no negative marking
- Numerical Answer Type (NAT) – no negative marking
Strategic Tip: Since MSQs and NAT questions carry no penalty, attempt all of them even if you’re uncertain. Use educated guesses to maximize your score.
Section 1: General Aptitude (15 Marks)
This section is common across all gate ai and ds syllabus papers and tests your reasoning, comprehension, and quantitative skills.
| Topics | Key Areas |
|---|---|
| Verbal Aptitude | Grammar, vocabulary, sentence completion, reading comprehension, critical reasoning |
| Quantitative Aptitude | Numerical computation, data interpretation, percentages, ratios, profit-loss, time-speed-distance, probability basics |
| Analytical Aptitude | Logic, patterns, analogies, syllogisms, numerical series |
| Spatial Aptitude | 2D and 3D shape visualization, paper folding, pattern matching |
Preparation Strategy: Allocate 1-2 weeks specifically for General Aptitude. Aim for 12-13 marks out of 15 to stay competitive. Use previous GATE GA questions from any branch—they’re all identical.
Section 2: Core GATE DS AI Subjects (85 Marks)
This is where ranks are decided. The gate ds ai core subjects section tests your depth in mathematics, programming, databases, and AI/ML concepts.
1. Probability and Statistics
Why It Matters: Forms the mathematical backbone of Machine Learning and AI. Expect 10-12 marks from this section.
Official Topics (IIT Guwahati Syllabus):
- Counting & Combinatorics: Permutations, combinations
- Probability Foundations: Sample space, events, independence, mutually exclusive events, conditional/marginal/joint probability, Bayes’ Theorem
- Expectation & Variance: Conditional expectation, mean, median, mode, standard deviation, correlation, covariance
- Discrete Distributions: Uniform, Bernoulli, Binomial, Poisson
- Continuous Distributions: Uniform, Exponential, Normal, Standard Normal, t-distribution, Chi-squared
- Statistical Inference: Cumulative Distribution Function (CDF), Conditional PDF, Central Limit Theorem, confidence intervals
- Hypothesis Testing: z-test, t-test, chi-squared test
Student-Friendly Tip: Don’t just memorize formulas. Understand when to use each distribution and why. Practice Bayes’ Theorem extensively—it appears in both Statistics and AI sections.
2. Linear Algebra
Why It Matters: Essential for understanding Machine Learning algorithms, especially PCA, SVD, and neural networks.
Official Topics:
- Vector Spaces: Subspaces, linear dependence/independence
- Matrices: Projection, orthogonal, idempotent, partition matrices and their properties
- Quadratic Forms: Understanding and applications
- Systems of Linear Equations: Gaussian elimination, solutions
- Eigenanalysis: Eigenvalues, eigenvectors, determinant, rank, nullity
- Matrix Decomposition: LU decomposition, Singular Value Decomposition (SVD)
Common Pitfall: Many students struggle with geometric interpretations. Visualize transformations and projections—use tools like Python to experiment with matrices.
3. Calculus and Optimization
Why It Matters: Core to understanding gradient descent and neural network training.
Official Topics:
- Single-variable functions: limits, continuity, differentiability
- Taylor series expansion
- Maxima and minima problems
- Single-variable optimization techniques
Note: Unlike some engineering branches, the gate ai ml syllabus focuses primarily on single-variable calculus. Multivariable calculus is not explicitly mentioned, though it helps conceptually in ML.
4. Programming, Data Structures and Algorithms
Why It Matters: Tests your ability to implement and analyze algorithms—critical for real-world DS & AI roles.
Official Topics:
- Python Programming: Syntax, data types, control structures, functions
- Data Structures: Stacks, queues, linked lists, trees, hash tables
- Search Algorithms: Linear search, binary search
- Sorting Algorithms: Selection sort, bubble sort, insertion sort, mergesort, quicksort (divide and conquer)
- Graph Theory Basics: Graph traversals (BFS, DFS), shortest path algorithms
Preparation Strategy: Code every algorithm from scratch. Understand time and space complexity (Big O notation) for each. Practice on platforms like LeetCode or HackerRank with easy-to-medium difficulty problems.
5. Database Management and Warehousing
Why It Matters: Data pipelines and storage are fundamental to any DS/AI workflow. Expect 8-10 marks.
Official Topics:
- Data Modeling: ER model, relational model
- Query Languages: Relational algebra, tuple calculus, SQL
- Database Design: Integrity constraints, normalization (1NF, 2NF, 3NF, BCNF), file organization, indexing
- Data Transformation: Normalization, discretization, sampling, compression
- Data Warehousing: Schema design for multidimensional models (star, snowflake), concept hierarchies, measure categorization and computations
Student-Friendly Tip: SQL is directly testable. Practice writing complex queries with joins, subqueries, and aggregations. Understand when to denormalize in warehouse contexts vs. normalize in transactional databases.
6. Machine Learning (High Weightage: 15-18 Marks)
Why It Matters: This is the heart of the gate ds and ai syllabus 2026. Allocate maximum preparation time here.
(i) Supervised Learning
Official Topics:
- Regression: Simple linear regression, multiple linear regression, ridge regression
- Classification: Logistic regression, k-nearest neighbor (k-NN), Naive Bayes classifier, linear discriminant analysis (LDA), support vector machines (SVM), decision trees
- Neural Networks: Multi-layer perceptron, feed-forward neural networks
- Model Evaluation: Bias-variance trade-off, cross-validation (leave-one-out, k-folds)
(ii) Unsupervised Learning
Official Topics:
- Clustering: k-means, k-medoid, hierarchical clustering (top-down/bottom-up, single-linkage, complete-linkage)
- Dimensionality Reduction: Principal Component Analysis (PCA)
Preparation Strategy:
- Understand the mathematics behind each algorithm, not just the implementation
- Know when to use each model (e.g., SVM for high-dimensional data, Naive Bayes for text classification)
- Practice bias-variance trade-off scenarios extensively
- Implement algorithms in Python using NumPy (not just scikit-learn) to understand internals
Common Question Types: Derivations, conceptual MCQs, numerical problems on error rates, NAT questions on cross-validation results.
7. Artificial Intelligence
Why It Matters: Tests foundational AI concepts that complement ML. Expect 10-12 marks.
Official Topics:
Search Algorithms:
- Uninformed search: BFS, DFS, depth-limited search, iterative deepening
- Informed search: A*, heuristic functions, admissibility, consistency
- Adversarial search: Minimax, alpha-beta pruning
Logic:
- Propositional logic: syntax, semantics, inference, resolution
- Predicate logic: quantifiers, unification, forward/backward chaining
Reasoning Under Uncertainty:
- Conditional independence representation (Bayesian networks)
- Exact inference: variable elimination
- Approximate inference: sampling methods
Student-Friendly Tip: AI questions often involve trace-through problems (e.g., “Show the steps of A* search on this graph”). Practice manually executing algorithms to build intuition.
Subject-Wise Weightage Distribution (Expected)
Based on the gate data science and ai exam pattern structure:
| Subject | Expected Marks | Preparation Priority |
|---|---|---|
| Machine Learning | 15-18 | Highest |
| Artificial Intelligence | 10-12 | High |
| Probability & Statistics | 10-12 | High |
| Linear Algebra | 8-10 | Medium-High |
| Database Management | 8-10 | Medium-High |
| Programming & DSA | 8-10 | Medium |
| Calculus & Optimization | 6-8 | Medium |
Strategic Note: These are estimates based on the syllabus depth. Always practice all topics, but if pressed for time, prioritize Machine Learning and AI.
How to Prepare Using the gate ds and ai syllabus 2026
Step 1: Map Each Topic to Quality Resources
- Probability & Statistics: “Introduction to Probability” by Blitzstein and Hwang
- Linear Algebra: Gilbert Strang’s lectures (MIT OpenCourseWare)
- Machine Learning: Andrew Ng’s course (Coursera) + “Hands-On Machine Learning” by Aurélien Géron
- Artificial Intelligence: “Artificial Intelligence: A Modern Approach” by Russell & Norvig
- Python & DSA: “Problem Solving with Algorithms and Data Structures using Python”
Step 2: Create a 4-Month Study Timeline
- Month 1: Mathematics (Linear Algebra, Probability, Calculus)
- Month 2: Programming, DSA, and Databases
- Month 3: Machine Learning (focus area)
- Month 4: AI + Revision + Mock Tests
Step 3: Practice with Previous Papers (Where Available)
Since GATE DS & AI started in 2024, solve:
- GATE 2024 and 2025 DS & AI papers (actual questions)
- Related questions from CS, EC, and other branches for overlapping topics
- Standard ML/AI problem sets from online platforms
Step 4: Take Regular Mock Tests
Simulate exam conditions monthly. Track:
- Time management per section
- Accuracy in MCQs (negative marking impact)
- Question selection strategy (easy → medium → hard)
Common Mistakes to Avoid
- Ignoring General Aptitude: Those 15 marks matter in close rank battles
- Shallow ML Understanding: Don’t just memorize sklearn syntax—know the math
- Skipping Coding Practice: You need hands-on DSA and Python fluency
- Over-reliance on Video Lectures: Reading textbooks builds deeper understanding
- Not Practicing NAT Questions: These are free marks if you’re accurate
Final Thoughts: Your Path Forward
The GATE Data Science and Artificial Intelligence Syllabus 2026 is comprehensive but conquerable with the right strategy. Unlike traditional engineering papers, DS & AI demands you think like a problem-solver, not just a formula-memorizer.
Your action plan starts now:
- Download the official syllabus PDF
- Assess your current strengths in mathematics, programming, and ML
- Create a personalized study timeline with weekly milestones
- Join a study group or online forum for peer learning
- Solve previous year questions from GATE 2024-2025 DS & AI
Remember: this is a marathon, not a sprint. Consistent daily effort beats last-minute cramming every single time.
Ready to begin? Bookmark this guide, download the syllabus, and take your first step toward a top GATE rank today.
Found this helpful? Share it with fellow GATE aspirants preparing for the Data Science and AI paper. Questions or tips? Drop them in the comments below!
FAQs: GATE Data Science and Artificial Intelligence Syllabus 2026
The syllabus includes seven core areas: Probability and Statistics, Linear Algebra, Calculus and Optimization, Programming and Data Structures, Database Management, Machine Learning (both supervised and unsupervised), and Artificial Intelligence covering search, logic, and reasoning under uncertainty.
Yes. IIT Guwahati, the organizing institute for GATE 2026, has published the complete syllabus on the official GATE website. The structure closely follows the 2025 pattern with no major changes reported as of now.
Start by mastering the mathematics foundations (Probability, Linear Algebra, Calculus), then move to programming and databases. Dedicate maximum time to Machine Learning and AI sections as they carry the highest weightage. Use the official syllabus to create a topic-wise checklist and track your progress weekly.
Candidates with a Bachelor's degree (or in their final year) in Engineering, Science, Technology, Commerce, or Arts are eligible. There's no age limit or attempt restriction, making it accessible for both students and working professionals.
The official syllabus PDF is available on the GATE 2026 website hosted by IIT Guwahati. Navigate to the "Test Papers & Syllabus" section and download the DA (Data Science and AI) syllabus document directly.
Machine Learning typically carries 15-18 marks out of the 85-mark core section, making it the highest-weighted individual subject. Combined with AI (10-12 marks), these two subjects account for nearly 30% of your total score.
Yes, the official syllabus specifically mentions Python as the programming language. Questions will assume Python syntax and standard libraries. However, algorithmic thinking and problem-solving skills matter more than language-specific features.
Aim for at least 12-13 marks out of 15 in General Aptitude. Since it's relatively easier to score here than in technical sections, maximizing GA marks gives you a buffer for tougher core questions.
