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 2026, IIT Guwahati 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, 2026, 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 (MCQ): Standard 4-option questions with negative marking.
- Multiple Select Questions (MSQ): One or more correct options; no negative marking.
- Numerical Answer Type (NAT): Requires a numerical value as the answer; no negative marking.
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
Covers counting methods, probability theory, random variables, key probability distributions, and statistical inference techniques such as hypothesis testing and confidence intervals.
Counting
Covers basic counting principles, permutations and combinations (with and without repetition), and techniques such as the pigeonhole principle and inclusion–exclusion principle.
- Counting (Permutation and Combinations)
- Pigeon hole Principle and Inclusion Exclusion Principle
- Permutation and Combination with repetition
Basics of Probability
Covers probability axioms, sample space, events and their types, and fundamental rules used to model and analyze uncertain outcomes.
Conditional Probability
Covers conditional probability, the law of total probability, and Bayes’ theorem for analyzing dependent events and updating probabilities based on given information.
Descriptive Statistics
Covers measures of central tendency and dispersion, including mean, median, mode, variance, standard deviation, correlation, and covariance for summarizing data.
Random Variable
Covers discrete and continuous random variables, probability mass and density functions, expectation, variance, and conditional expectation.
- Random Variable
- Discrete Random Variable
- Continuous Random Variable
- Expectation
- Variance
- Conditional Expectation
- Conditional Variable
- Probability Density Function
Joint Random Variable
Represents two or more random variables together, describing their joint, marginal, and conditional probabilities and relationships.
Probability Distributions
Describe how probabilities are assigned to values of a random variable, including discrete and continuous distributions, and summarize the likelihood of outcomes.
- 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
Involves making predictions or generalizations about a population based on a sample, using techniques like estimation, hypothesis testing, and regression analysis.
- 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
Covers vector spaces, matrices and determinants, systems of linear equations, eigenvalues and eigenvectors, and special matrices with decompositions.
Basics of Vector Spaces
Focuses on vector spaces, subspaces, linear dependence and independence, spanning sets, and basis.
Matrices and Determinants
Deals with matrix types, operations, inverses, determinants, and their role in solving linear equations.
- 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
Studies methods to solve homogeneous and non-homogeneous linear equations, including LU decomposition.
- System of Linear Equation
- Homogeneous System of Linear Equation
- Non-Homogeneous System of Linear Equation
- LU Decomposition
Eigenvalues and Eigenvectors
Involves finding scalar values and corresponding vectors that satisfy linear transformations of matrices.
Special Matrices and Decompositions
Covers partition and projection matrices, quadratic forms, and singular value decomposition.
Refer to Last Minute Notes on Linear Algebra for quick revision.
Calculus and Optimization
Studies limits, derivatives, and integrals to analyze functions and their behavior.
Focuses on finding maxima, minima, and optimal solutions in mathematical and real-world problems, applying techniques under constraints.
Introduction to Calculus
Studies change and motion through concepts of limits, derivatives, and integrals.
Continuity and Differentiability
Examines whether functions are smooth and unbroken, and how they change at each point.
Series and Function Behavior
Analyzes sequences, series, and the overall behavior of functions, including convergence and divergence.
Extremes and Optimization
Focuses on finding maximum and minimum values of functions and applying them to solve practical optimization problems.
- Maxima and Minima
- Optimization
Refer to Last Minute Notes on Calculus for quick revision.
Python Programming and Data Structures
Covers Python syntax, control structures, functions, and data structures like lists, stacks, queues, and trees for efficient data handling.
Python Fundamentals
Introduces basic Python concepts, including variables, data types, operators, and simple input/output operations.
Python Data Types
Covers different types of data in Python, such as integers, floats, strings, lists, tuples, sets, and dictionaries.
Python Conditional Statements and Loops
Focuses on decision-making using if, elif, else, and repeating tasks using for and while loops.
- Conditional Statements
- For Loop
- While Loop
- Loop Control Statements (break, continue, pass)
- List Comprehension
Python Functions
Introduces defining and using reusable blocks of code, including parameters, return values, and scope.
- 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
Covers object-oriented programming in Python, including classes, objects, inheritance, encapsulation, and polymorphism.
Python Collections
Introduces built-in data structures in Python, such as lists, tuples, sets, and dictionaries, for efficient data storage and manipulation.
Data Structures
Studies ways to organize, store, and manage data efficiently, including arrays, linked lists, stacks, queues, trees, and graphs.
Algorithms
Covers step-by-step procedures for solving problems efficiently, including searching, sorting, and optimization techniques.
Asymptotic Analysis of Algorithms
Evaluates the efficiency of algorithms by analyzing their growth rates and estimating time and space complexity for large inputs.
- 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
Studies equations that define sequences recursively, expressing each term in terms of previous terms.
- Introduction to Recurrence Relations
- Master Theorem
- Different types of recurrence relations and their solutions
Divide and Conquer
Solves problems by breaking them into smaller subproblems, solving each recursively, and combining the results.
- 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
Solves optimization problems by making the best local choice at each step with the hope of finding a global optimum.
- 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
Studies structures consisting of nodes (vertices) and edges to model relationships and solve problems like traversal, shortest paths, and connectivity.
- 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
Solves complex problems by breaking them into overlapping subproblems, storing solutions, and combining them to find the optimal result.
- 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
Covers methods to locate and organize data efficiently, including linear and binary search, various sorting algorithms, algorithmic techniques, and hash-based data storage.
- 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
Focuses on storing, organizing, and managing data efficiently, covering concepts like tables, queries, normalization, and transaction management.
Introduction
Provides an overview of the subject, its purpose, key concepts, and basic applications.
- 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
Represents data and their relationships using entities, attributes, and relationships to design a database schema.
- 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)
Defines data in tables (relations) and uses operations and queries to manipulate and retrieve information.
- 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)
Focuses on structuring databases efficiently while ensuring data accuracy, consistency, and eliminating redundancy.
- 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)
Used to create, manage, and query relational databases, including operations like data retrieval, insertion, updating, and deletion.
- 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
Manages multiple database operations to ensure data consistency, integrity, and isolation in concurrent environments.
- 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)
Covers methods for storing and accessing data efficiently using sequential files, indexes, and tree-based structures like 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
Involves collecting, storing, and managing large volumes of historical data for analysis, reporting, and decision-making.
Data Warehousing Basics
Introduces fundamental concepts of data warehousing, including data integration, storage, and support for analytical processing and decision-making.
- Introduction to Data Warehousing
- Types of Data Warehouses
- DBMS vs Data Warehousing
- ETL Process
- Data Marts
OLAP Technology
Focuses on online analytical processing techniques for multidimensional data analysis, enabling fast querying, aggregation, and reporting.
- Introduction to OLAP Technology
- OLAP Operations
- Types of OLAP Systems
- OLAP Applications
Data Transformation
Deals with modifying, cleaning, and restructuring data to make it suitable for analysis and further processing.
- Introduction to Data Transformation
- Data Transformation Methods
- Data Normalization
- Aggregation
- Discretization
- Data Sampling
Data Warehousing Concepts and Models
Explains core warehousing concepts and data models such as star and snowflake schemas used for efficient analytical processing.
- 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
Defines the structure of multidimensional databases, including fact and dimension tables, to support efficient analysis and reporting.
Concept Hierarchies and Measures
Describes levels of data abstraction and numerical metrics used for aggregation and analysis in multidimensional models.
Refer to Last Minute Notes on Data Warehousing for quick revision.
Machine Learning
Studies algorithms that enable systems to learn from data, identify patterns, and make predictions or decisions without explicit programming.
Machine Learning Basics
Introduces fundamental concepts of learning from data, including supervised and unsupervised learning, features, and model evaluation.
- Introduction to Machine Learning
- Types of Machine Learning
- Machine Learning Lifecycle
- Data Cleaning
- Feature Engineering
- Data Preprocessing
Supervised Learning
Involves training models on labeled data to learn relationships and make predictions or classifications.
- 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
Focuses on discovering patterns, structures, and relationships in unlabeled data.
- 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
Studies the creation of intelligent systems that can perform tasks such as reasoning, learning, problem-solving, and decision-making.
Search in Artificial Intelligence
Deals with techniques to explore problem spaces systematically in order to find solutions or optimal paths.
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)
Logic
Studies formal rules of reasoning and inference used to represent knowledge and derive valid conclusions.
Reasoning Under Uncertainty
Focuses on making decisions and inferences when information is incomplete or probabilistic.
- 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
Assesses logical reasoning, quantitative ability, and verbal skills required for problem-solving and decision-making.
Verbal Aptitude
Measures proficiency in understanding, interpreting, and using language effectively, including grammar, vocabulary, and comprehension.
- 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
Evaluates numerical and mathematical ability, including arithmetic, algebra, geometry, and data interpretation skills.
- 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
Tests logical reasoning, problem-solving, and the ability to analyze and interpret information effectively.
Spatial Aptitude
Assesses the ability to visualize, manipulate, and reason about objects and their relationships in space.
- 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.