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.
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

GATE 2024 Exam Date Branch-wise

GATE 2024 Exam DateExam SlotPaper
February 3, 2024 (Saturday)09:30 AM to 12:30 PM DA, AR, CY, ES, PI
02:30 PM to 05:30 PM ME, GE, PH
February 4, 2024 (Sunday)09:30 AM to 12:30 PM CE1, BM, CH, EY, GG, XH
02:30 PM to 05:30 PM CE2, IN, MA, MN, PE
February 10, 2024 (Saturday)09:30 AM to 12:30 PM CS1, AE, AG, BT, MT, NM, TF
02:30 PM to 05:30 PM CS2, ST, XE, XL
February 11, 2024 (Sunday)09:30 AM to 12:30 PMEC
02:30 PM to 05:30 PMEE

Exam Pattern

ParticularsDetails
Examination ModeComputer Based Test (Online)
Duration3 Hours
Number of Papers in GATE 202430 Papers
Section
  • 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.

GATE DA SYLLABUS

General Aptitude Syllabus
TopicsSub-Topics
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
TopicsSubtopics
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