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GATE 2024 Newly Introduced Branch, Data Science & Artificial Intelligence (DA)

Discover the newly introduced branch which is Data Science & Artificial Intelligence (DA). Explore the Data Science & Artificial Intelligence Exam Pattern, Syllabus and Best Books in this detailed article.
authorImageRiya Sharma7 Nov, 2023
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GATE 2024 Newly Introduced Branch: DA

GATE 2024 Newly Introduced Branch- The Graduate Aptitude Test in Engineering (GATE) is a highly competitive exam that provides a doorway to prominent postgraduate programs and research opportunities at India's top educational institutions. Every year, hundreds of candidates from across the country carefully prepare for this exam, hoping to secure a bright career in their respective disciplines.

Data Science and Artificial Intelligence (DA) has been included as a new subject for the GATE 2024 exam by IISc Bangalore. Candidates must be interested in the key topics of Data Science and Artificial Intelligence (DA) because it is an emerging field. The best books, key subjects, and other related information for the GATE Data Science & Artificial Intelligence course will be covered in this article.

Also Read- What are Data Science, Machine Learning, AI, Deep Learning, and Big Data?

GATE 2024 Newly Introduced Branch - Data Science and Artificial Intelligence (DA)

GATE 2024 DA (Code for Data Science and Artificial Intelligence) is a new paper introduced by IISc Bangalore in the academic year 2024-25. This is a positive step that indicates the growing significance of these sectors.

Data science and artificial intelligence are fast changing the way we live and work, and they are becoming increasingly important for a wide range of careers.The official website, gate2024.iisc.ac.in, has published the GATE 2024 DA Syllabus PDF.

GATE 2024 Newly Introduced Branch - Data Science and Artificial Intelligence (DA) Significance

In recent years, Data Science and Artificial Intelligence have emerged as two of the most transformational and sought-after areas. As these sectors continue to drive innovation and transform industries around the world, the value of a standardized grading process like GATE cannot be emphasized.

GATE guarantees that candidates have the knowledge and skills essential to succeed in these competitive sectors, which are essential for the future of technology and data-driven decision-making.

Also Read: GATE 2024 New Syllabus Data Science and AI

In the GATE two-paper combination, candidates who have chosen GATE 2024 DA as their primary choice can choose a second paper from a list of options that includes  EC (Electronics and Communication Engineering), CS (Computer Science and Information Technology), EE (Electrical Engineering), ST (Statistics), MA (Mathematics), ME (Mechanical Engineering), and XE (Engineering Sciences).

Click Here - Parakaram GATE 2024 - Data Science & AI

GATE 2024 Newly Introduced Branch - Data Science and Artificial Intelligence (DA)  Overview

The GATE 2024 Data Science and AI exam format will include a total of 100 marks. The DA exam paper will be divided into two sections namely General Aptitude and Data Science and AI Subject Questions, worth 15 and 85 marks, respectively. Check out the table below for the complete GATE 2024 DA Exam Pattern.

GATE Data Science and Artificial Intelligence Overview
Particular Details
Name of the Exam Graduate Aptitude Test in Engineering
Commonly Known As GATE
Organizing Authority IISc Bangalore
GATE 2024 Exam Date February 3, 4, 10, and 11, 2024
Examination Mode Computer-based Test
Exam Duration 3 Hours
Sections in GATE DA Paper General Aptitude (GA) Core Engineering Chosen Subjects
Distribution of Marks in GATE DA Paper General Aptitude: 15 marks Subject Questions: 85 marks Total Marks: 100 marks
Marking Scheme Questions worth 1 mark or 2 marks
Design of Questions
  • Application
  • Comprehension
  • Analysis & Synthesis
  • Recall
Negative Marking Only applicable to the incorrect answer chosen in MCQ. Deduction of 1/3 mark for each incorrect answer worth one mark. Deduction of 2/3 mark for each incorrect answer worth two marks.
Sectional Time-Limit None
GATE 2024 Scorecard Validity 3 Years
GATE 2024 Exam Pattern for DS & AI Check Now
GATE 2024 Official Website www.gate.iisc.ac.in

GATE 2024 Newly Introduced Branch - Data Science and Artificial Intelligence (DA) Exam Pattern

Applicants can check the GATE 2024 Data Science & Artificial Intelligence (DA) Exam Pattern to succeed in the upcoming exam.

GATE Data Science  & AI Exam Pattern 2024
Particulars Details
Name of the Paper GATE Data Science and Artificial Intelligence Paper
Paper Code DA
Duration of Exam 3 Hours
Sections General Aptitude Data Science and AI
Type of Questions Multiple Choice Questions (MCQs) Multiple Select Questions (MSQs) Numerical Answer Type (NAT)
GATE DA Paper Marks Distribution
  • General Aptitude: 15 Marks
  • Data Science and AI based Questions: 85 Mark
Negative Marking
  • For a one mark MCQ, an incorrect response will result in a deduction of 1/3 mark.
  • For a two mark MCQ, an incorrect response will result in a deduction of 2/3 marks.
  • There is no penalty for incorrect answer to MSQ or NAT questions.

GATE 2024 Newly Introduced Branch - Data Science and Artificial Intelligence (DA) Syllabus

The GATE Data Science & Artificial Intelligence (DA) test is divided into two parts: general aptitude and core Data Science & AI subjects. General aptitude and core Data Science & Artificial Intelligence are weighted at 15% and 85%, respectively.

Candidates should also thoroughly review the GATE Data Science and AI syllabus to understand the main topics that will be covered in the exam. We are going to discuss significant Data Science and AI subjects here.

The GATE 2024 Data Science and AI Syllabus is divided into seven sections: Probability and Statistics, Linear Algebra, Calculus and Optimization, Machine Learning, and AI, among others. The table below contains the entire GATE 2024 Data Science and AI Syllabus.

GATE 2024 Data Science and AI Syllabus
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, Binomial Distribution)
  • Continuous Random Variables
  • 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: Queues, Linked Lists, Stacks, 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

Click Here: GATE 2024 Data Science and AI Syllabus PDF Download

GATE 2024 Newly Introduced Branch - Data Science and Artificial Intelligence (DA) Important Points

  • The GATE 2024 DA Syllabus is divided into several sections. About 40% of it belongs to Mathematics, 25% to Programming and Databases from Computer Science, and the rest is devoted to AI and Machine Learning.
  • If you're taking examinations like CS, EE, EC, or ME, the GATE 2024 DA papers may be easier to understand because they share a few concepts with Mathematics, CS, EE, and ECE.
  • Experts believe that the GATE 2024 DA paper from IISc Bangalore may be designed in such a way that the Mathematics section is difficult while the AI and machine learning sections are simple. This is due to the fact that this is the first year they are introducing this paper.
  • Subjects such as Probability and Statistics, as well as Machine Learning, are expected to be more essential in the GATE 2024 DA Syllabus than other courses due to the quantity of content they contain.

Data Science Courses Important Links

Master in Data Science Data Science Internship Programs
IIT Madras Data Science Course BSC Data Science Syllabus
Data Science Interview Questions and Answers

The publication of the GATE 2024 syllabus for Data Science and Artificial Intelligence is a big step forward for applicants interested in these subjects. They can start on a rewarding journey toward postgraduate courses and research possibilities in India's best engineering universities with proper planning and devotion, ultimately contributing to the growth and innovation of these revolutionary disciplines.

Click Here- PW GATE 2024 Online Courses

GATE 2024 Newly Introduced Branch - Data Science and Artificial Intelligence (DA) Best Books

Choosing the appropriate study resources is critical when studying for the GATE Data Science & AI exam. As this paper will be included in the GATE 2024 exam, it is necessary to familiarize yourself with the best books for this paper. You should also read the GATE Syllabus. Here is a list of some of the best books-

GATE 2024 Data Science  & AI Best Books
Book Author
Data Science for Dummies Lillian Pierson (Author), Jake Porway (Foreword)
Introduction to Data Science:Practical Approach with R and Python B. Uma Maheswari (Author), R. Sujatha (Author)
Data Science from Scratch: First Principles with Python Joel Grus
Artificial Intelligence: A Modern Approach Textbook by Peter Norvig and Stuart J. Russell
‘Deep Learning’ Ian Goodfellow, Yoshua Benjio, Aaron Courville
Introduction to Probability Dimitri P. Bertsekas & John N. Tsitsiklis
Introduction to Linear Algebra Gilbert Strang
Learning Python Mark Lutz
Database Management Systems Raghu Ramakrishnan and Johannes Gehrke
Machine Learning for Beginners Chris Sebastian
Artificial Intelligence A Modern Approach by Stuart Russell and Peter Norvig

Also Read – PW GATE 2024 Preparation Books

PW Best Recommended Books for GATE 2024
Book Name Purchase Link
GATE Wallah Topic Wise PYQ Combo: Computer Science & IT + Engineering Mathematics & Aptitude Click Here
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GATE Wallah Topic Wise PYQ Combo: Electronics & Communication Engineering + Engineering Mathematics & Aptitude Click Here
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GATE Wallah Topic Wise PYQ Combo: Computer Science & IT Engineering Click Here
GATE Wallah Topic Wise PYQ Combo: Electronic and Communication Engineering Click Here
GATE Wallah Topic Wise PYQ Combo: Electrical Engineering Click Here
GATE Wallah Topic Wise PYQ Combo: Engineering Mathematics & General Aptitude Click Here

GATE 2024 Important Links

GATE 2024 Registration GATE Exam Date 2024
GATE Exam Pattern GATE Syllabus 2024
GATE Application Form 2024 GATE 2024 FAQs
GATE Admit Card 2024 GATE Cut Off
GATE 2024 Preparation GATE 2024 Test Series

GATE 2024 Preparation Important Links

GATE 2024 ECE Preparation GATE 2024 EE Preparation
GATE 2024 CSE Preparation GATE 2024 Preparation
GATE 2025 Preparation PW GATE 2024 Preparation Books
GATE 2024 ME Preparation

GATE 2024 Newly Introduced Branch FAQs

Which new branch has been introduced for GATE 2024?

The Data Science and Artificial Intelligence paper, which was recently launched by the exam-conducting authority, will be included in GATE 2024.

Is it possible to take a paper in addition to the GATE Data Science and AI paper?

Yes, Aspirants are permitted to take several GATE papers, provided that the examination dates do not overlap.

Is there negative marking in the GATE 2024 Data Science and AI paper?

Yes. The GATE 2024 Data Science and AI paper, as well as the remaining 29 papers, are subject to negative marking.

How many subjects will be covered in the GATE DA 2024?

The GATE DA syllabus has seven subjects- -Probability and Statistics -Linear Algebra -Calculus and optimization -Programming, Data Structures and Algorithms -Database Management and Warehousing -Machine Learning -AI
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