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VOOZH | about |
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VOOZH | about |
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:
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.
Covers counting methods, probability theory, random variables, key probability distributions, and statistical inference techniques such as hypothesis testing and confidence intervals.
Covers basic counting principles, permutations and combinations (with and without repetition), and techniques such as the pigeonhole principle and inclusion–exclusion principle.
Covers probability axioms, sample space, events and their types, and fundamental rules used to model and analyze uncertain outcomes.
Covers conditional probability, the law of total probability, and Bayes’ theorem for analyzing dependent events and updating probabilities based on given information.
Covers measures of central tendency and dispersion, including mean, median, mode, variance, standard deviation, correlation, and covariance for summarizing data.
Covers discrete and continuous random variables, probability mass and density functions, expectation, variance, and conditional expectation.
Represents two or more random variables together, describing their joint, marginal, and conditional probabilities and relationships.
Describe how probabilities are assigned to values of a random variable, including discrete and continuous distributions, and summarize the likelihood of outcomes.
Involves making predictions or generalizations about a population based on a sample, using techniques like estimation, hypothesis testing, and regression analysis.
Refer to Last Minute Notes on Probability and Statistics for quick revision.
Covers vector spaces, matrices and determinants, systems of linear equations, eigenvalues and eigenvectors, and special matrices with decompositions.
Focuses on vector spaces, subspaces, linear dependence and independence, spanning sets, and basis.
Deals with matrix types, operations, inverses, determinants, and their role in solving linear equations.
Studies methods to solve homogeneous and non-homogeneous linear equations, including LU decomposition.
Involves finding scalar values and corresponding vectors that satisfy linear transformations of matrices.
Covers partition and projection matrices, quadratic forms, and singular value decomposition.
Refer to Last Minute Notes on Linear Algebra for quick revision.
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.
Studies change and motion through concepts of limits, derivatives, and integrals.
Examines whether functions are smooth and unbroken, and how they change at each point.
Analyzes sequences, series, and the overall behavior of functions, including convergence and divergence.
Focuses on finding maximum and minimum values of functions and applying them to solve practical optimization problems.
Refer to Last Minute Notes on Calculus for quick revision.
Covers Python syntax, control structures, functions, and data structures like lists, stacks, queues, and trees for efficient data handling.
Introduces basic Python concepts, including variables, data types, operators, and simple input/output operations.
Covers different types of data in Python, such as integers, floats, strings, lists, tuples, sets, and dictionaries.
Focuses on decision-making using if, elif, else, and repeating tasks using for and while loops.
Introduces defining and using reusable blocks of code, including parameters, return values, and scope.
Covers object-oriented programming in Python, including classes, objects, inheritance, encapsulation, and polymorphism.
Introduces built-in data structures in Python, such as lists, tuples, sets, and dictionaries, for efficient data storage and manipulation.
Studies ways to organize, store, and manage data efficiently, including arrays, linked lists, stacks, queues, trees, and graphs.
Covers step-by-step procedures for solving problems efficiently, including searching, sorting, and optimization techniques.
Evaluates the efficiency of algorithms by analyzing their growth rates and estimating time and space complexity for large inputs.
Studies equations that define sequences recursively, expressing each term in terms of previous terms.
Solves problems by breaking them into smaller subproblems, solving each recursively, and combining the results.
Solves optimization problems by making the best local choice at each step with the hope of finding a global optimum.
Studies structures consisting of nodes (vertices) and edges to model relationships and solve problems like traversal, shortest paths, and connectivity.
Solves complex problems by breaking them into overlapping subproblems, storing solutions, and combining them to find the optimal result.
Covers methods to locate and organize data efficiently, including linear and binary search, various sorting algorithms, algorithmic techniques, and hash-based data storage.
Refer to Last Minute Notes on Algorithms for quick revision.
Focuses on storing, organizing, and managing data efficiently, covering concepts like tables, queries, normalization, and transaction management.
Provides an overview of the subject, its purpose, key concepts, and basic applications.
Represents data and their relationships using entities, attributes, and relationships to design a database schema.
Defines data in tables (relations) and uses operations and queries to manipulate and retrieve information.
Focuses on structuring databases efficiently while ensuring data accuracy, consistency, and eliminating redundancy.
Used to create, manage, and query relational databases, including operations like data retrieval, insertion, updating, and deletion.
Manages multiple database operations to ensure data consistency, integrity, and isolation in concurrent environments.
Covers methods for storing and accessing data efficiently using sequential files, indexes, and tree-based structures like B and B+ trees.
Refer to Last Minute Notes on DBMS for quick revision.
Involves collecting, storing, and managing large volumes of historical data for analysis, reporting, and decision-making.
Introduces fundamental concepts of data warehousing, including data integration, storage, and support for analytical processing and decision-making.
Focuses on online analytical processing techniques for multidimensional data analysis, enabling fast querying, aggregation, and reporting.
Deals with modifying, cleaning, and restructuring data to make it suitable for analysis and further processing.
Explains core warehousing concepts and data models such as star and snowflake schemas used for efficient analytical processing.
Defines the structure of multidimensional databases, including fact and dimension tables, to support efficient analysis and reporting.
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.
Studies algorithms that enable systems to learn from data, identify patterns, and make predictions or decisions without explicit programming.
Introduces fundamental concepts of learning from data, including supervised and unsupervised learning, features, and model evaluation.
Involves training models on labeled data to learn relationships and make predictions or classifications.
Focuses on discovering patterns, structures, and relationships in unlabeled data.
Refer to Last Minute Notes on Machine Learning for quick revision.
Studies the creation of intelligent systems that can perform tasks such as reasoning, learning, problem-solving, and decision-making.
Deals with techniques to explore problem spaces systematically in order to find solutions or optimal paths.
Uninformed Search (Blind Search)
Informed Search (Heuristic Search)
Studies formal rules of reasoning and inference used to represent knowledge and derive valid conclusions.
Focuses on making decisions and inferences when information is incomplete or probabilistic.
Refer to Last Minute Notes on Artificial Intelligence for quick revision.
Assesses logical reasoning, quantitative ability, and verbal skills required for problem-solving and decision-making.
Measures proficiency in understanding, interpreting, and using language effectively, including grammar, vocabulary, and comprehension.
Evaluates numerical and mathematical ability, including arithmetic, algebra, geometry, and data interpretation skills.
Tests logical reasoning, problem-solving, and the ability to analyze and interpret information effectively.
Assesses the ability to visualize, manipulate, and reason about objects and their relationships in space.
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.
