GATE DA Notes (According to GATE 2025 Syllabus)

Last Updated : 24 Jul, 2025

GATE, or Graduate Aptitude Test in Engineering, is a prominent national-level exam organized by IISc Bangalore and the seven original IITs. For the year 2025, IIT Roorkee is set to conduct the GATE exam, as confirmed by their official notification. Passing the GATE exam qualifies candidates for pursuing Master of Technology (M.Tech) or Master of Engineering (ME) degrees from top-tier institutes, and it opens doors to career opportunities in Public Sector Undertakings (PSUs).

The GATE DA exam is scheduled for February 15, 2025, and the GATE score will remain valid for three years. Exam will include a total of 65 questions, with 10 questions from General Aptitude and 55 from the core subject area. The duration of the GATE exam is 3 hours.

The GATE exam features three types of questions:

Multiple Choice Questions (MCQs)
Multiple Select Questions (MSQs)
Numerical Answer Type (NAT) questions

This GATE DA tutorial is designed to clearly explain the GATE syllabus, aiding your preparation for each subject area effectively. On this tutorial page, you'll find articles corresponding to each topic listed in the GATE DA Syllabus. Additionally, be sure to check out our Last Minute Notes on GATE CS and GATE DA to enhance your revision strategies before the exam.

Probability and Statistics

Below, you can find the syllabus and key topics for Probability and Statistics covered in the GATE DA exam.

Counting	Counting (Permutation and Combinations) Pigeon hole Principle and Inclusion Exclusion Principle Permutation and Combination with repetition
Basics of Probability	Probability axioms Sample Space Events and Types of Events
Conditional Probability	Conditional Probability Law of Total Probability Baye's Theorem
Descriptive Statistics	Mean, Median, Mode Variance, Standard Deviation Correlation and Covariance
Random Variable	Random Variable Discrete Random Variable Continuous Random Variable Expectation Variance Conditional Expectation Conditional Variable Probability Density Function
Joint Random Variable	Joint Probability Marginal Probability Conditional Probability
Probability Distributions	Discrete and Continuous Uniform Distribution Geometric Distribution Bernoulli Trials and Binomial Distribution Poisson Distribution Exponential Distribution Normal Distribution Standard Normal Distribution T distribution Chi-Squared Distribution
Inferential Statistics	Central Limit Theorem Confidence Interval Hypothesis Testing Z-Test T-test Chi-Squared Test Chi-Squared Test for Feature Selection

Refer to Last Minute Notes on Probability and Statistics for quick revision.

Linear Algebra

Below, you can find the syllabus and key topics for Linear Algebra covered in the GATE DA exam.

Basics of Vector Spaces	Vector Space Subspaces Linear Dependence and Independence of Vectors Spanning Set and Basis
Matrices and Determinants	Matrices Types of Matrices Inverse of Matrices Determinants Properties of Determinants Row Reduced Echelon Form of Matrices Rank of Matrices Nullity of Matrix Column Space and Row Space
Systems of Linear Equations	System of Linear Equation Homogeneous System of Linear Equation Non-Homogeneous System of Linear Equation LU Decomposition
Eigenvalues and Eigenvectors	Eigen Values and Eigen Vectors Properties of Eigen Values Cayley Hamilton Theorem
Special Matrices and Decompositions	Partition Matrix Projection Matrix Quadratic Forms Singular Value Decomposition

Refer to Last Minute Notes on Linear Algebra for quick revision.

Calculus and Optimization

Below, you can find the syllabus and key topics for Calculus and Optimization covered in the GATE DA exam.

Introduction to Calculus	Functions Limit L' Hospital's Rule
Continuity and Differentiability	Continuity Differentiability
Series and Function Behavior	Taylor Series Increasing and Decreasing Function
Extremes and Optimization	Maxima and Minima Optimization

Refer to Last Minute Notes on Calculus for quick revision.

Python Programming and Data Structures

Below, you can find the syllabus and key topics for Python Programming and Data Structures covered in the GATE DA exam.

Python Fundamentals	Introduction Input and Output Variables Keywords Operators
Python Data Types	Numbers Boolean Strings Type Casting List Tuples Dictionary Sets Arrays
Python Conditional Statements and Loops	Conditional Statements For Loop While Loop Loop Control Statements (break, continue, pass) List Comprehension
Python Functions	def Keyword Use of pass Statement in Function Return statement Global and Local Variables Recursion in Python args and *kwargs in Function Lambda Function
Python OOPs Concepts	Classes and Objects Polymorphism Inheritance Abstract Encapsulation Iterators
Python Collections	Counters Heapq Deque OrderedDict Defaultdict
Data Structures	Stack Queues Linked List Trees Hash Tables

Algorithms

Below, you can find the syllabus and key topics for Algorithms covered in the GATE DA exam.

Algorithms Notes for GATE CSE
Asymptotic Analysis of Algorithms	Introduction of Algorithms Asymptotic Analysis Worst, Average and Best Cases Asymptotic Notations Analysis of Loops Small ‘o’ and Small ‘Omega’ Notation What does ‘Space Complexity’ mean?
Recurrence Relations	Introduction to Recurrence Relations Master Theorem Different types of recurrence relations and their solutions
Divide and Conquer	Introduction to Divide and Conquer Binary Search Merge Sort Merge Sort for Linked Lists How to make Mergesort to perform O(n) comparisons in best case? QuickSort Iterative Quick Sort QuickSort on Singly Linked List Median of two sorted arrays Count Inversions in an array Using Merge Sort Closest Pair of Points Strassen’s Matrix Multiplication Sort a nearly sorted (or K sorted) array Search in an almost sorted array K-th Element of Two Sorted Arrays K’th Smallest/Largest Element in Unsorted Array
Greedy Techniques	Introduction to Greedy Algorithms Activity Selection Problem Job Sequencing Problem Huffman Coding Efficient Huffman Coding for Sorted Input Fractional Knapsack Problem Optimal File Merge Patterns Kruskal’s Minimum Spanning Tree Algorithm Prim’s Minimum Spanning Tree (MST) Prim’s MST for Adjacency List Representation Dijkstra’s shortest path algorithm Dijkstra’s Algorithm for Adjacency List Representation
Graph	Introduction to Graph Algorithms Breadth First Traversal or BFS for a Graph Depth First Traversal or DFS for a Graph Applications of Depth First Search Detect Cycle in a Directed Graph Topological Sorting Bellman–Ford Algorithm Floyd Warshall Algorithm Shortest path with exactly k edges in a directed and weighted graph Biconnected graph Articulation Points (or Cut Vertices) in a Graph Check if a graph is strongly connected (Kosaraju’s Theoram) Bridges in a graph Transitive closure of a graph
Dynamic Programming	Introduction to Dynamic Programming Overlapping Subproblems Property Optimal Substructure Property Longest Common Subsequence Matrix Chain Multiplication 0-1 Knapsack Problem Min Cost Path Subset Sum Problem Bellman–Ford Algorithm Floyd Warshall Algorithm Total number of non-decreasing numbers with n digits Smallest power of 2 greater than or equal to n
Searching, Sorting, Technique-based Theorem and Hashing	Introduction to Searching Algorithms Introduction to Sorting Algorithm Linear Search Linear Search vs Binary Search Binary Search Selection Sort Bubble Sort Insertion Sort Merge Sort QuickSort Heap Sort Counting Sort

Refer to Last Minute Notes on Algorithms for quick revision.

Database Management System

Below, you can find the syllabus and key topics for Database Management System covered in the GATE DA exam.

Introduction	Introduction to Database Management System DBMS 3-Tier Architecture DBMS 2-Level, 3-Level Architecture Need for DBMS Challenges of Database Security in DBMS Advantages of DBMS over File system Data Abstraction and Data Independence
ER-Model	Introduction to ER Model Recursive Relationships Minimization of ER Diagram Enhanced ER Model Mapping from ER Model to Relational Model
Relational Model (Relational algebra, Tuple Calculus)	Introduction to Relational Model Relational Algebra – Overview Anomalies in Relational Model Relational Model Introduction and Codd Rules Keys in Relational Model (Candidate, Super, Primary, Alternate and Foreign) Relational Algebra – Extended Operators Tuple Relational Calculus How to solve Relational Algebra problems for GATE
Database Design (Integrity Constraints, Normal Forms)	Introduction to Database Normalization Normal Forms in Database Normalization Functional Dependency and Attribute Closure Types of Functional Dependency Finding Attribute Closure and Candidate Keys using Functional Dependencies Number of possible Superkeys Lossy and Lossless Decomposition Dependency Preserving Decomposition Lossless Join and Dependency Preserving Decomposition DBMS \| How to find the highest normal form of a relation Minimum relations satisfying 1NF Equivalence of Functional Dependencies Canonical Cover Multivalued Dependency
Structured Query Languages (SQL)	Introduction to Structured Query Language (SQL) Parts of SQL Data Manipulation Language in SQL Data Definition in SQL Joins in SQL Inner VS Outer Join Having Vs Where Clause Database Objects Nested Queries in SQL Join operation Vs nested query Indexing in Databases SQL Clauses SQL Views SQL Indexes SQL queries on clustered and non-clustered Indexes SQL Tutorial
Transactions and Concurrency Control	Introduction to Concurrency Control Database Recovery Techniques ACID Properties in DBMS Log based recovery Why recovery is needed? Transaction Isolation Levels in DBMS Types of Schedules in Concurrency Control Types of Recoverability of Schedules in DBMS Conflict Serializability Precedence Graph For Testing Conflict Serializability How to test if two schedules are View Equal or not ? Recoverability of Schedules Cascadeless in DBMS Deadlock in DBMS Starvation in DBMS Transaction and Concurrency Control Lock Based Protocol Concurrency Control Techniques Two Phase Locking (2-PL) Categories of Two Phase Locking (2-PL) Thomas Write Rule Timestamp Ordering Protocols Multiple Granularity Locking Graph Based Protocol Introduction to TimeStamp and Deadlock Prevention Schemes Implementation of Locking in DBMS
File Structures (Sequential files, Indexing, B and B+ trees)	Introduction to Indexing in Databases File Organization Hashing in DBMS Introduction to B-Tree Insertion in B-Tree Deletion in B-Tree Introduction to B+ Trees Insertion in a B+ tree Difference between B tree and B+ tree

Refer to Last Minute Notes on DBMS for quick revision.

Data Warehousing

Below, you can find the syllabus and key topics for Data Warehousing covered in the GATE DA exam.

Data Warehousing Basics	Introduction to Data Warehousing Types of Data Warehouses DBMS vs Data Warehousing ETL Process Data Marts
OLAP Technology	Introduction to OLAP Technology OLAP Operations Types of OLAP Systems OLAP Applications
Data Transformation	Introduction to Data Transformation Data Transformation Methods Data Normalization Aggregation Discretization Data Sampling
Data Warehousing Concepts and Models	Introduction to Data Modeling Data Modeling Techniques Multidimensional Data Model Dimensional Modeling Types of Facts in a Multidimensional Data Model
Schema for Multidimensional Data Models	Database Schemas Strategies for Schema Design Star Schema Snowflake Schema
Concept Hierarchies and Measures	Introduction to Concept Hierarchies Measures: Categorization and Computations

Refer to Last Minute Notes on Data Warehousing for quick revision.

Machine Learning

Below, you can find the syllabus and key topics for Machine Learning covered in the GATE DA exam.

Machine Learning Basics	Introduction to Machine Learning Types of Machine Learning Machine Learning Lifecycle Data Cleaning Feature Engineering Data Preprocessing
Supervised Learning	Introduction to Supervised Learning Regression Classification Simple Linear Regression Multiple Linear Regression Ridge Regression Lasso Regression Logistic Regression K - Nearest Neighbor (KNN) Algorithm Naive Bayes Classifiers Linear Discriminant Analysis Support Vector Machine Bias Variance Tradeoff Cross - Validation Methods K - Folds Cross Validation Leave One Out Cross-Validation Multi - Layer Perceptron Feedforward Neural Network
Unsupervised Learning	Introduction to Unsupervised Learning Clustering Dimensionality Reduction K Means Clustering K - Medoids Clustering Hierarchical Clustering Types of Linkage Agglomerative Methods Principal Component Analysis (PCA)

Machine Learning Basics

Introduction to Machine Learning

Types of Machine Learning

Machine Learning Lifecycle

Data Cleaning

Feature Engineering

Data Preprocessing

Supervised Learning

Introduction to Supervised Learning

Regression

Classification

Simple Linear Regression

Multiple Linear Regression

Ridge Regression

Lasso Regression

Logistic Regression

K - Nearest Neighbor (KNN) Algorithm

Naive Bayes Classifiers

Linear Discriminant Analysis

Support Vector Machine

Bias Variance Tradeoff

Cross - Validation Methods

K - Folds Cross Validation

Leave One Out Cross-Validation

Multi - Layer Perceptron

Feedforward Neural Network

Unsupervised Learning

Introduction to Unsupervised Learning

Clustering

Dimensionality Reduction

K Means Clustering

K - Medoids Clustering

Hierarchical Clustering

Types of Linkage

Agglomerative Methods

Principal Component Analysis (PCA)

Refer to Last Minute Notes on Machine Learning for quick revision.

Artificial Intelligence

Below, you can find the syllabus and key topics for Artificial Intelligence covered in the GATE DA exam.

Search in Artificial Intelligence	Uninformed Search (Blind Search) Breadth - First Search (BFS) Uniform Cost Search (UCS) Depth - First Search (DFS) Depth - Limited Search Iterative Deepening Depth-First Search (IDDFS) Bidirectional Search Informed Search (Heuristic Search) Greedy Best-First Search A * Search Algorithm Iterative Deepening A* Search (IDA*) Adversarial Search Minimax Algorithm Alpha-Beta Pruning
Logic	Propositional Logic Predicate Logic
Reasoning Under Uncertainty	Conditional Independence Representation Exact Inference Through Variable Elimination Approximate Inference Through Sampling

Search in Artificial Intelligence

Uninformed Search (Blind Search)

Informed Search (Heuristic Search)

Adversarial Search

Logic

Propositional Logic

Predicate Logic

Reasoning Under Uncertainty

Conditional Independence Representation

Exact Inference Through Variable Elimination

Approximate Inference Through Sampling

Refer to Last Minute Notes on Artificial Intelligence for quick revision.

General Aptitude

Below, you can find the syllabus and key topics for General Aptitude covered in the GATE DA exam.

Verbal Aptitude	Basic English Grammar Tenses Articles Adjectives Prepositions Conjunctions Verb-Noun Agreement Parts of Speech Basic Vocabulary Words Idioms Phrases in context Reading and comprehension Narrative sequencing
Quantitative Aptitude	Data interpretation Data Graphs (Bar Graphs, Pie Charts, and other graphs representing data) 2- and 3-Dimensional Plot Maps Tables Numerical Computation and Estimation Ratios Percentages Powers Exponents and Logarithms Permutations and Combinations Series Mensuration and Geometry Elementary Statistics and Probability
Analytical Aptitude	Logic: Deduction and Induction Analogy Numerical relations and reasoning
Spatial Aptitude	Spatial Aptitude Transformation of Shapes Translation Rotation Scaling Mirroring Assembling Grouping Paper Folding, Cutting, and Patterns in 2 and 3 Dimensions

As you prepare for the GATE DA exam, mastering these core topics and strategies will be crucial for success. Stay consistent with your study plan and refer to the Last Minute Notes for a quick revision closer to the exam date.