GATE DA Syllabus 2025 PDF: Download Streamwise Topics

The official syllabus of GATE DA 2025 is structured into six main areas. Each stream focuses on a specific set of topics drawn from real-world applications and academic foundations.
1. Probability and Statistics
This section forms the core of data modeling and decision-making under uncertainty. Topics include:
Random variables and probability distributions
Conditional probability, Bayes’ Theorem
Moments, expectation, variance
Sampling theory and Central Limit Theorem
Confidence intervals and standard statistical tests (z-test, t-test, chi-square test)
Correlation, covariance, and hypothesis testing methods
Mastering these topics is vital for understanding both traditional statistical techniques and more advanced probabilistic models in machine learning.
2. Linear Algebra
Linear algebra is essential for modeling high-dimensional data and understanding machine learning algorithms like PCA, regression, and SVM. Key topics include:
Vector spaces, matrices, and matrix operations
Rank, nullity, and linear independence
Determinants, eigenvalues, eigenvectors
Matrix factorizations such as LU and SVD
Orthogonality and projection matrices
Expect multiple-choice and numerical-type questions based on matrix computations and their geometric interpretations.
3. Calculus and Optimization
Optimization is at the heart of training machine learning models. Combined with calculus, it allows understanding how algorithms converge. The syllabus includes:
Functions, limits, and continuity
Differentiation and Taylor series
Maxima, minima, and saddle points
Convexity, gradient descent basics, and one-variable optimization techniques
This section builds the mathematical intuition required to grasp how learning algorithms are tuned.
4. Programming, Data Structures, and Algorithms
This stream tests the candidate’s computational ability and problem-solving skills using algorithms and basic data structures:
Python programming: loops, recursion, functions
Data structures: arrays, linked lists, stacks, queues, trees, graphs
Sorting and searching algorithms: merge sort, quick sort, binary search
Graph algorithms: BFS, DFS, shortest paths (Dijkstra, Floyd-Warshall)
Candidates must be able to analyze code for time and space complexity, and write or debug small Python functions.
5. Database Systems and Data Warehousing
Data is the backbone of any AI pipeline. This stream ensures candidates can store, query, and preprocess large datasets effectively:
ER models and relational database schema
SQL operations: joins, subqueries, group-by
Relational algebra and tuple calculus
Normalization and indexing
Data warehouse modeling, dimensional schemas (star/snowflake), OLAP
Data transformation workflows and ETL (Extract, Transform, Load)
Basic familiarity with file systems, schema design, and querying is expected.
6. Machine Learning and Artificial Intelligence
This section covers the core of predictive modeling and intelligent decision-making:
Supervised Learning: linear regression, logistic regression, SVM, k-NN, decision trees, naive Bayes
Unsupervised Learning: k-means, hierarchical clustering, PCA, k-medoids
Neural Networks: perceptrons, backpropagation, activation functions
AI Reasoning: logic (propositional and predicate), adversarial and informed search, probabilistic inference (Bayesian networks, sampling)
This area requires both conceptual clarity and practical insight. Most questions are scenario-based and test a mix of theory and application.