GATE DA Syllabus 2025: Data Science & Artificial Intelligence

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Explore the comprehensive GATE Data Science & Artificial Intelligence (DA) syllabus for 2024 to kickstart your exam preparation and achieve success.

GATE DA Syllabus 2024 – GATE 2024 exam will be conduct by IISc, Bangalore on dates 3, 4 and 10, 11 February, 2024. Here we have provided latest Data Science & Artificial Intelligence syllabus & paper pattern for GATE 2024 aspirants.

All candidates with Data Science & Artificial Intelligence subject are advised to download this latest syllabus before starting their GATE 2024 exam preparation.

GATE 2024 Highlights

GATE 2024 Conducting BodyIISc, Bangalore
GATE 2024 Exam Date3, 4, 10, 11 February, 2024
GATE 2024 Total Subjects30
GATE 2024 Exam ModeONLINE Computer Based Test (CBT)
GATE 2024 Exam Duration3 hours (180 minutes)
GATE 2024 Total Questions10 (GA) + 55 (subject)= 65
GATE 2024 Total Marks100
GATE 2024 Question TypeMCQ, MSQ, NAT

GATE Data Science & Artificial Intelligence Engineering Paper Pattern 2024

Paper SectionsMarks Distribution
Subject Questions85% of the total marks.
General Aptitude15% of the total marks.

GATE Data Science and Artificial Intelligence Exam Pattern Details 2024

Examination ModeComputer-based Test
Exam Duration3 Hours
Sections in GATE DA PaperGeneral Aptitude (GA) + Core Engineering Selected Subjects
Distribution of Marks in GATE DA PaperGeneral Aptitude: 15 marks

Subject Questions: 85 marks

Total Marks: 100 marks

Marking SchemeQuestions worth 1 mark or 2 marks
Negative MarkingApplicable only to the wrong answer selected in an MCQ

For 1 Mark Wrong Answer, Deduction of 1/3 Marks Applicable

For 2 Marks Wrong Answer, Deduction of 2/3 Marks Applicable

GATE Data Science & Artificial Intelligence Engineering Syllabus 2024 PDF

General Aptitude Syllabus (Common to all papers) [pdf] Download
GATE Syllabus for Data Science & Artificial Intelligence (DA) [pdf] Download

GATE Data Science and Artificial Intelligence Syllabus for General Aptitude 2024

Verbal AptitudeBasic English grammar: tenses, articles, adjectives, prepositions, conjunctions, verb-nounagreement, and other parts of speech Basic vocabulary: words, idioms, and phrases incontext 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, Paperfolding, cutting, and patterns in 2 and 3 dimensions

GATE Data Science and Artificial Intelligence Syllabus for Core Subjects 2024

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

GATE Data Science & Artificial Intelligence Guidance

GATE Data Science & Artificial Intelligence Syllabus & Exam Pattern FAQs

What is the syllabus for GATE Data Science & Artificial Intelligence (DSAI) paper?

The GATE Data Science & Artificial Intelligence (DSAI) paper's syllabus covers various topics related to data science, artificial intelligence, and machine learning.

It includes subjects like probability and statistics, linear algebra, machine learning, deep learning, natural language processing, computer vision, and more.

The syllabus is designed to assess candidates' knowledge and understanding of these core areas.

What is the exam pattern for GATE Data Science & Artificial Intelligence (DSAI) paper?

The GATE DSAI paper consists of two sections - General Aptitude and Subject-specific.

The General Aptitude section is common for all GATE papers and includes questions on verbal ability and numerical ability.

The Subject-specific section comprises questions related to data science, artificial intelligence, and machine learning, as per the syllabus. The total duration of the exam is three hours.

Are there any specific reference materials for preparing the GATE DSAI paper?

Yes, GATE DSAI aspirants can refer to a variety of resources for preparation.

Textbooks, online courses, study guides, and materials from reputed institutions that cover topics in data science, artificial intelligence, and machine learning are recommended.

It's important to choose resources that align with the syllabus and offer comprehensive coverage of relevant subjects.

How can I effectively prepare for the GATE Data Science & Artificial Intelligence (DSAI) paper?

Effective preparation for the GATE DSAI paper requires a structured approach:

  1. Understand the syllabus thoroughly and create a study plan.
  2. Focus on building a strong foundation in core concepts.
  3. Practice solving previous years' question papers and mock tests to familiarize yourself with the exam pattern.
  4. Work on improving time management skills to solve questions within the allocated time.
  5. Stay updated with the latest trends and developments in data science and artificial intelligence.

What are the career prospects for candidates with a good score in GATE Data Science & Artificial Intelligence (DSAI) paper?

A good score in GATE DSAI can open up various career opportunities for candidates.

They can pursue higher studies by applying for M.Tech/MS programs in data science, artificial intelligence, and related fields in prestigious institutes.

Additionally, a high GATE score can enhance job prospects in industries such as data analytics, machine learning, AI research, and technology-driven organizations, offering roles such as data scientists, AI engineers, machine learning engineers, and more.

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