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Eligibility Criteria

  • Age Limit: There is no age limit to apply for GATE.
  • Nationality: Candidates of all nationalities can apply for the exam
  • Academic Qualification
    • Candidates who are currently studying in the 3rd or higher years of any undergraduate degree program or who have completed any government approved degree program in Engineering/ Technology/ Science/ Architecture/ Humanities are eligible to appear for GATE EXAM.
    • Candidates who have completed any government-approved degree in Engineering/Architecture/ Technology/Science/Commerce/Arts are eligible to apply. However, candidates who have certificates from any professional society need to ensure that the examinations are conducted by AICTE/MoE/UGC/UPSC approved societies.
    • Candidates who have obtained/are pursuing their qualifying degree from countries other than India: Candidates must be currently in the 3rd or higher years or must have completed their Bachelor’s degree (of at least three years duration) in Engineering/ Technology/ Science/ Architecture/ Humanities.
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Degree / ProgramQualifying Degree / ExaminationDescription of Eligible Candidates
B.E. / B.Tech. /B. Pharm.Bachelor’s degree in Engineering / Technology (4 years after 10+2 or 3 years after B.Sc. / Diploma in Engineering / Technology)Currently in 3rd year / ALready Completed
B.ArchBachelor’s degree of Architecture (5-year course) / Naval Architecture (4-year course) / Planning (4-year course)Currently in the 3rd year or higher or already completed
B.Sc. (Research) / B.S.Bachelor’s degree in Science (Post-Diploma / 4 years after 10+2)Currently in the 3rd year or higher or already completed
Pharm. D.(after 10+2)6 years degree program, consisting of internship or residency training, during third year onwardsCurrently in the 3rd/ 4th/ 5th/ 6th year or already completed
M.B.B.S. / B.D.S. / B.V.Sc.Degree holders of M.B.B.S. / B.D.S. / B.V.Sc and those who are in the 5th/ 6th/ 7th semester or higher semester of such programme.5th/ 6th/ 7th or higher semester or already completed
M. Sc. / M.A. / MCA or equivalentMaster’s degree in any branch of Arts / Science / Mathematics / Statistics / Computer Applications or equivalentCurrently in the first year or higher or already Completed
Int. M.E. / M.Tech.(Post-B.Sc.)Post-B.Sc Integrated Master’s degree programs in Engineering / Technology (4-year program)Currently in the 1st/ 2nd/ 3rd/ 4th year or already completed
B.Sc. (Agriculture, Horticulture, Forestry)4-years programCurrently in the 3rd/ 4th year or already completed

Exam Pattern

Examination ModeComputer Based Test (Online)
Duration3 Hours
  • General Aptitude (GA)
  • Candidate Selected Subject
Type of Questions
  • Multiple Choice Questions (MCQs)
  • Multiple Select Questions (MSQs)
  • Numerical Answer Type (NAT) Questions
Design of Questions
  • Application
  • Analysis
  • Comprehension
  • Recall
  • Synthesis
Number of Questions65 Questions (including 10 questions from General Aptitude)
Distribution of Questions in all Papers except AR, CY, DA, EY, GG, MA, PH, ST, XH and XL
  • Engineering Mathematics – 13 Marks
  • Subject Questions – 72 Marks
  • General Aptitude – 15 Marks
Distribution of Questions in AR, CY, DA, EY, GG, MA, PH, ST, XH and XL
  • Questions from Subject Concerned – 85 Marks
  • General Aptitude – 15 Marks
Total Marks100 Marks
Marking SchemeAll of the questions will be worth 1 or 2 marks
GATE Negative MarkingTwo types of MCQs:
  • MCQs – 1 mark for each correct answer; 1/3 mark will be deducted for every wrong answer.
  • MCQs – 2 marks for each correct answer; 2/3 marks will be deducted for every incorrect response. There are no negative marks for Numerical Answer Type (NAT) questions
  • NO negative marks for MSQ & NAT.

Section-wise Marks Distribution

SectionTotal QuestionsMarking Per QuestionsTotal Marks
General Aptitude10 (NAT/MSQ/MCQ) 1 0r 215
Data Analytics and Artificial Intelligence25 (NAT/MCQ) and 30 (NAT/MSQ) 1 and 285(25 + 60)
Total65 100


General Aptitude Syllabus
Verbal AptitudeBasic English grammar: tenses, articles, adjectives, prepositions, conjunctions, verb-noun agreement, and other parts of speech Basic vocabulary: words, idioms, and phrases in context Reading and comprehension Narrative sequencing
Quantitative AptitudeData interpretation: data graphs (bar graphs, pie charts, and other graphs representing data), 2- and 3-dimensional plots, maps, and tables Numerical computation and estimation: ratios, percentages, powers, exponents and logarithms, permutations and combinations, and series Mensuration and geometry Elementary statistics and probability.
Analytical AptitudeLogic: deduction and induction, Analogy, Numerical relations and reasoning
Spatial AptitudeTransformation of shapes: translation, rotation, scaling, mirroring, assembling, and grouping Paper folding, cutting, and patterns in 2 and 3 dimensions
Data Science and Artificial Intelligence Syllabus
Probability and Statistics
  • Counting (Permutations and Combinations)
  • Probability Axioms
  • Sample Space
  • Events
  • Independent Events
  • Mutually Exclusive Events
  • Marginal, Conditional, and Joint Probability
  • Bayes’ Theorem
  • Conditional Expectation and Variance
  • Mean, Median, Mode, and Standard Deviation
  • Correlation and Covariance
  • Random Variables
  • Discrete Random Variables and Probability Mass Functions (Uniform, Bernoulli, and Binomial Distribution)
  • Continuous Random Variables and Probability Distribution Functions (Uniform, Exponential, Poisson, Normal, Standard Normal, t-Distribution, Chi-Squared Distributions)
  • Cumulative Distribution Function
  • Conditional Probability Density Function
  • Central Limit Theorem
  • Confidence Interval
  • z-Test
  • t-Test
  • Chi-Squared Test
Linear Algebra
  • Vector Space
  • Subspaces
  • Linear Dependence and Independence of Vectors
  • Matrices
  • Projection Matrix
  • Orthogonal Matrix
  • Idempotent Matrix
  • Partition Matrix and Their Properties
  • Quadratic Forms
  • Systems of Linear Equations and Solutions
  • Gaussian Elimination
  • Eigenvalues and Eigenvectors
  • Determinant
  • Rank
  • Nullity
  • Projections
  • LU Decomposition
  • Singular Value Decomposition
Calculus and Optimization
  • Functions of a Single Variable
  • Limit
  • Continuity and Differentiability
  • Taylor Series
  • Maxima and Minima
  • Optimization Involving a Single Variable
Programming, Data Structures, and Algorithms
  • Programming in Python
  • Basic Data Structures: Stacks, Queues, Linked Lists, Trees, and Hash Tables
  • Search Algorithms: Linear Search and Binary Search
  • Basic Sorting Algorithms: Selection Sort, Bubble Sort, Insertion Sort
  • Divide and Conquer Techniques: Mergesort, Quicksort
  • Introduction to Graph Theory
  • Basic Graph Algorithms: Traversals and the Shortest Path
Database Management and Warehousing
  • ER-Model (Entity-Relationship Model)
  • Relational Model: Relational Algebra, Tuple Calculus
  • SQL (Structured Query Language)
  • Integrity Constraints
  • Normal Form
  • File Organization
  • Indexing
  • Data Types
  • Data Transformation: Normalization, Discretization, Sampling, and Compression
  • Data Warehouse Modeling: Schema for Multidimensional Data Models
  • Concept Hierarchies
  • Measures: Categorization and Computations
Machine Learning
  • Supervised Learning
  • Regression and Classification Problems
  • Simple Linear Regression
  • Multiple Linear Regression
  • Ridge Regression
  • Logistic Regression
  • k-Nearest Neighbors
  • Naive Bayes Classifier
  • Linear Discriminant Analysis
  • Support Vector Machine
  • Decision Trees
  • Bias-Variance Trade-off
  • Cross-validation Methods: Leave-One-Out (LOO) Cross-validation, k-Folds Cross-validation
  • Multi-layer Perceptron
  • Feed-forward Neural Network
  • Unsupervised Learning:
  • Clustering Algorithms
  • k-Means and k-Medoid Clustering
  • Hierarchical Clustering
  • Dimensionality Reduction
  • Principal Component Analysis (PCA)
Artificial Intelligence (AI)
  • Search: Informed Search, Uninformed Search, Adversarial Search
  • Logic: Propositional Logic, Predicate Logic
  • Reasoning under Uncertainty Topics
  • Conditional Independence Representation
  • Exact Inference through Variable Elimination
  • Approximate Inference through Sampling

Important Dates

ParticularsTentative Dates
Commencement of the GATE online registration processAugust 2024
Deadline of registration process along with late feesOctober 2024
Closing date of extended online registration process along with Late FeeOctober 2024
Modification in the GATE Application formNovember 2024
Release of Admit cardJanuary 2025
Gate 2025 Exam DateFebruary 2025