GATE Data Science & AI (DA) Syllabus 2026: Complete Guide
GATE DA 2026: Everything You Need to Know
GATE Data Science and Artificial Intelligence (DA) is one of the newest and most popular GATE papers. With booming demand for data scientists, this paper opens doors to IITs, IISc, and top tech companies.
Exam Pattern
| Parameter | Details |
|---|---|
| Duration | 3 hours |
| Total Marks | 100 |
| Question Types | MCQ, MSQ, NAT |
| Sections | General Aptitude (15%) + Core (85%) |
Core Syllabus Breakdown
1. Probability and Statistics (15-20%)
- Counting, probability axioms, conditional probability
- Random variables, distributions (Uniform, Binomial, Poisson, Normal, Exponential)
- Joint distributions, covariance, correlation
- Central limit theorem, sampling distributions
- Point and interval estimation, hypothesis testing
2. Linear Algebra (10-15%)
- Vector spaces, linear independence
- Matrices, rank, determinants
- Eigenvalues, eigenvectors, diagonalization
- Singular value decomposition
3. Calculus and Optimization (10-15%)
- Limits, continuity, differentiability
- Taylor series, partial derivatives
- Gradient, Hessian, convexity
- Unconstrained optimization, gradient descent
- Constrained optimization, Lagrange multipliers
4. Machine Learning (25-30%)
- Supervised learning: Regression, Classification
- Linear regression, logistic regression
- Decision trees, Random forests, SVM
- Neural networks basics
- Unsupervised learning: Clustering, PCA
- Bias-variance tradeoff, cross-validation
5. Programming and Data Structures (15-20%)
- Python programming basics
- Arrays, linked lists, stacks, queues
- Trees, graphs, hashing
- Sorting and searching algorithms
- Time and space complexity analysis
6. AI and Deep Learning (10-15%)
- Search algorithms
- Knowledge representation
- Deep learning basics
- CNNs and RNNs overview
Preparation Strategy
GATE DA requires a balance of mathematical foundations and practical knowledge. Start with probability and linear algebra as they form the backbone of ML algorithms.
Month-wise Plan
| Month | Focus Areas |
|---|---|
| Aug-Sep | Math foundations (LA, Probability, Calculus) |
| Oct-Nov | Machine Learning + Programming |
| Dec | AI topics + Practice |
| Jan | Mock tests + Revision |
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