GATE Data Science and Artificial Intelligence Syllabus 2024:

GATE Data Science and Artificial Intelligence Syllabus 2024:

GATE Branches & Streams

GATE AEROSPACE ENGINEERING (AE)

GATE AEROSPACE ENGINEERING (AE)

GATE ARCHITECTURE & PLANNING

GATE ARCHITECTURE & PLANNING (AR)

GATE BIOMEDICAL ENGINEERING

GATE BIOMEDICAL ENGINEERING (BM)

GATE BIOTECHNOLOGY

GATE BIOTECHNOLOGY (BT)

GATE CHEMICAL ENGINEERING

GATE Chemical Engineering (CH)

GATE COMPUTER SCIENCE ENGINEERING

GATE COMPUTER SCIENCE ENGINEERING (CS)

GATE CIVIL ENGINEERING (CE)

GATE CIVIL ENGINEERING (EC)

GATE ENGINEERING SCIENCES

GATE ENGINEERING SCIENCES (XE)

GATE ELECTRONICS & COMMUNICATION ENGINEERING

GATE Electronics and Communication Engineering (EC)

GATE METALLURGY ENGINEERING

GATE METALLURGY ENGINEERING (MT)

GATE MECHANICAL ENGINEERING

GATE Mechanical Engineering (ME)

GATE MINING ENGINEERING

GATE MINING ENGINEERING (MN)

GATE PETROLEUM ENGINEERING

GATE PETROLIUM ENGINEERING COACHING

Data Science and Artificial Intelligence

Data Science and Artificial Intelligenc

GATE Data Science and Artificial Intelligence Syllabus 2024:

The syllabus for the DA paper in GATE 2024 covers the following topics:

General Aptitude
Probability and Statistics
Linear Algebra
Calculus and Optimization
Programming, Data Structures, and Algorithms
Database Management and Warehousing
Machine Learning
AI (Artificial Intelligence)

GATE Data Science and Artificial Intelligence Syllabus 2024 For Core Engineering Selected Subjects

Topics	Subtopics
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

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