The Graduate Aptitude Test in Engineering (GATE) is India’s most prestigious national-level entrance examination for admission to M.Tech, MS, PhD, and recruitment in Public Sector Undertakings (PSUs). Among the newest and fastest-growing papers is Data Science & Artificial Intelligence (DA), introduced to meet the increasing demand for professionals in AI, Machine Learning, Big Data, and Analytics With the rapid expansion of Artificial Intelligence across industries, the GATE DA paper has become one of the most sought-after choices for engineering and science graduates. A good GATE score can help candidates secure admission to premier institutes such as IITs, IISc, IIITs, and NITs, while also opening doors to high-paying careers in the technology sector.This guide covers everything you need to know about GATE Data Science & AI 2027, including the latest syllabus, eligibility criteria, exam pattern, preparation books, expected cut-offs, IIT admissions, safe scores, and career opportunities.

What is GATE Data Science & Artificial Intelligence (DA)?

The GATE DA paper is specifically designed for students interested in Artificial Intelligence, Machine Learning, Data Science, Statistical Analysis, Data Engineering, and Intelligent Systems. The examination evaluates candidates on mathematics, programming, databases, machine learning, and artificial intelligence fundamentals.The paper is ideal for students from Computer Science, Information Technology, Mathematics, Statistics, Electronics, and related disciplines.

GATE Data Science & AI 2027 Eligibility Criteria

Candidates can appear for GATE DA if they are:

Third-year or higher undergraduate students
B.E./B.Tech graduates
B.Sc. graduates (eligible disciplines)
M.Sc. students
MCA students
M.Tech students
Integrated Master’s students
Engineering graduates from recognized universities

Age Limit

There is no age limit for appearing in the GATE examination.

Number of Attempts

There is no restriction on the number of attempts.

GATE Data Science & AI 2027 Exam Pattern

Particular	Details
Examination Mode	Computer Based Test (CBT)
Duration	3 Hours
Total Questions	65
Maximum Marks	100
General Aptitude	15 Marks
Core Subject	85 Marks
Question Types	MCQ, MSQ & NAT
Negative Marking	Only for MCQs

GATE Data Science & AI 2027 Syllabus

Unit	Section	Complete Topics Covered
1. Probability and Statistics	Counting	Permutations, Combinations
	Probability	Probability Axioms, Sample Space, Events, Independent Events, Mutually Exclusive Events, Marginal Probability, Conditional Probability, Joint Probability, Bayes’ Theorem
	Statistical Measures	Conditional Expectation, Conditional Variance, Mean, Median, Mode, Standard Deviation, Correlation, Covariance
	Random Variables	Random Variables, Discrete Random Variables, Continuous Random Variables
	Probability Functions	Probability Mass Function (PMF), Probability Density Function (PDF), Conditional PDF, Cumulative Distribution Function (CDF)
	Probability Distributions	Uniform Distribution, Bernoulli Distribution, Binomial Distribution, Exponential Distribution, Poisson Distribution, Normal Distribution, Standard Normal Distribution, t-Distribution, Chi-Squared Distribution
	Statistical Inference	Central Limit Theorem (CLT), Confidence Interval, Z-Test, t-Test, Chi-Squared Test
2. Linear Algebra	Vector Spaces	Vector Space, Subspaces, Linear Dependence, Linear Independence
	Matrices	Matrices, Projection Matrix, Orthogonal Matrix, Idempotent Matrix, Partition Matrix, Properties of Matrices
	Linear Systems	Systems of Linear Equations, Solutions of Linear Equations, Gaussian Elimination
	Matrix Concepts	Determinant, Rank, Nullity, Projections, Quadratic Forms
	Eigen Concepts	Eigenvalues, Eigenvectors
	Matrix Factorization	LU Decomposition, Singular Value Decomposition (SVD)
3. Calculus and Optimization	Single Variable Calculus	Functions of a Single Variable, Limit, Continuity, Differentiability
	Series	Taylor Series
	Optimization	Maxima, Minima, Optimization Involving a Single Variable
4. Programming, Data Structures and Algorithms	Programming	Programming in Python
	Data Structures	Stacks, Queues, Linked Lists, Trees, Hash Tables
	Searching Algorithms	Linear Search, Binary Search
	Sorting Algorithms	Selection Sort, Bubble Sort, Insertion Sort
	Divide and Conquer	Merge Sort, Quick Sort
	Graph Theory	Introduction to Graph Theory
	Graph Algorithms	Graph Traversals, Shortest Path Algorithms
5. Database Management and Warehousing	Database Models	ER Model, Relational Model
	Database Query Languages	Relational Algebra, Tuple Calculus, SQL
	Database Design	Integrity Constraints, Normal Forms
	Database Storage	File Organization, Indexing
	Data Processing	Data Types, Normalization, Discretization, Sampling, Compression
	Data Warehousing	Data Warehouse Modelling, Schema for Multidimensional Data Models, Concept Hierarchies, Measures, Categorization, Computations
6. Machine Learning	Supervised Learning	Regression Problems, Classification Problems, Simple Linear Regression, Multiple Linear Regression, Ridge Regression, Logistic Regression, k-Nearest Neighbour (k-NN), Naïve Bayes Classifier, Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Decision Trees
	Model Evaluation	Bias-Variance Trade-off, Leave-One-Out (LOO) Cross-Validation, k-Fold Cross-Validation
	Neural Networks	Multi-Layer Perceptron (MLP), Feed-Forward Neural Network
	Unsupervised Learning	Clustering Algorithms, k-Means, k-Medoids, Hierarchical Clustering, Top-Down Clustering, Bottom-Up Clustering, Single-Linkage, Multiple-Linkage
	Dimensionality Reduction	Principal Component Analysis (PCA)
7. Artificial Intelligence (AI)	Search Techniques	Informed Search, Uninformed Search, Adversarial Search
	Logic	Propositional Logic, Predicate Logic
	Reasoning Under Uncertainty	Conditional Independence Representation, Exact Inference through Variable Elimination, Approximate Inference through Sampling

Data Science & Artificial Intelligence (DA) 2026 Paper Analysis

Chapter No.	Chapter Name	No. of Questions	Weightage (%)
1	Probability and Statistics	11	20.00%
2	Linear Algebra	8	14.55%
3	Calculus and Optimization	4	7.27%
4	Programming, Data Structures and Algorithms	12	21.82%
5	Database Management and Warehousing	6	10.91%
6	Machine Learning	8	14.55%
7	Artificial Intelligence	6	10.91%
Total	—	55	100%

Best Books for GATE Data Science & AI Preparation

Chapter	Topics Covered	Best Books (Recommended)	Author(s)
1. Probability and Statistics	Probability, Bayes Theorem, Random Variables, Distributions, CLT, Hypothesis Testing	Introduction to Probability	Dimitri P. Bertsekas & John N. Tsitsiklis
		A First Course in Probability	Sheldon M. Ross
		Mathematical Statistics and Data Analysis	John A. Rice
2. Linear Algebra	Vector Spaces, Matrices, Eigenvalues, SVD, LU Decomposition	Introduction to Linear Algebra	Gilbert Strang
		Linear Algebra and Its Applications	Gilbert Strang
		Matrix Computations	Gene H. Golub & Charles F. Van Loan
3. Calculus and Optimization	Limits, Continuity, Differentiation, Taylor Series, Maxima & Minima	Calculus	James Stewart
		Thomas’ Calculus	George B. Thomas
		Calculus and Analytical Geometry	G. B. Thomas & R. L. Finney
4. Programming, Data Structures and Algorithms	Python, Data Structures, Searching, Sorting, Graph Algorithms	Learning Python	Mark Lutz
		Python Programming: An Introduction to Computer Science	John M. Zelle
		Data Structures and Algorithms in Python	Michael T. Goodrich, Roberto Tamassia & Michael H. Goldwasser
		Fundamentals of Data Structures	Ellis Horowitz & Sartaj Sahni
		Grokking Algorithms	Aditya Y. Bhargava
5. Database Management and Warehousing	ER Model, SQL, Relational Algebra, Normalization, Warehousing	Database Management Systems	Raghu Ramakrishnan & Johannes Gehrke
		Fundamentals of Database Systems	Ramez Elmasri & Shamkant Navathe
		Database System Concepts	Silberschatz, Korth & Sudarshan
		The Data Warehouse Toolkit	Ralph Kimball & Margy Ross
6. Machine Learning	Regression, Classification, SVM, Decision Trees, Clustering, PCA, Neural Networks	Pattern Recognition and Machine Learning	Christopher M. Bishop
		Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow	Aurélien Géron
		Machine Learning: A Probabilistic Perspective	Kevin P. Murphy
		Mathematics for Machine Learning	Marc Peter Deisenroth, A. Aldo Faisal & Cheng Soon Ong
7. Artificial Intelligence	Search, Logic, Reasoning Under Uncertainty, Inference	Artificial Intelligence: A Modern Approach	Stuart Russell & Peter Norvig
		Artificial Intelligence: Foundations of Computational Agents	David L. Poole & Alan K. Mackworth
		Artificial Intelligence by Example	Denis Rothman

Recommended Book’s

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GATE Data Science & Artificial Intelligence (DA) – Minimum Qualifying Score

Year	General (UR)	OBC-NCL / EWS	SC / ST / PwD
2026	26.4	23.7	17.5
2025	29.0	26.1	19.3
2024	37.1	33.3	24.7

GATE Closing Score 2025 for M.tech

IIT	Programme (Examples)	Closing GATE Score (General)
IIT Madras	Computational Engineering	625–700
IIT Hyderabad	M.Tech Artificial Intelligence	Around 700–780
IIT Hyderabad	M.Tech Data Science	Around 680–760
IIT Delhi	M.Tech in Machine Intelligence and Data Science (MINDS)	Around 720
IIT Guwahati	Data Science / AI-related programmes	Around 650–730
IIT Kanpur	AI / CSE-related programmes	Around 720–800
IIT Kharagpur	AI / Data Analytics-related programmes	Around 680–760
IIT Jodhpur	Artificial Intelligence	Around 620–700
IIT Palakkad	Data Science programmes	Around 550–650

IITs Offering Seats By GATE Data Science & AI

Institute	M.Tech Programme	GATE DA Accepted	Approx. Seats (2025)
IIT Hyderabad	M.Tech in Artificial Intelligence (2-Year)	✔	30
IIT Hyderabad	M.Tech in Artificial Intelligence (Research Assistant)	✔	25
IIT Delhi	M.Tech in Machine Intelligence & Data Science (MINDS)	✔	30
IIT Guwahati	M.Tech in Data Science	✔	20
IIT Bhilai	M.Tech in Data Science & Artificial Intelligence	✔	20
IIT Jodhpur	M.Tech in Artificial Intelligence	✔	20–25
IIT Ropar	M.Tech in Artificial Intelligence	✔	15–20

IIT	Department	M.Tech Programme	Approx. Seats (2025)	GATE DA Accepted
IIT Bhubaneswar	School of Electrical Sciences	M.Tech in Artificial Intelligence	20	✔

Courses Available Through GATE DA

A good GATE DA score can help secure admission into:

M.Tech in Data Science
M.Tech in Artificial Intelligence
M.Tech in Machine Learning
M.Tech in Computational Data Science
M.Tech in Data Engineering
M.Tech in AI & Robotics
MS by Research
PhD Programs

Career Opportunities After GATE Data Science & AI

Graduates with expertise in Data Science and Artificial Intelligence are in high demand across industries such as IT, finance, healthcare, e-commerce, manufacturing, and research.

Popular job roles include:

Data Scientist
Machine Learning Engineer
Artificial Intelligence Engineer
Data Engineer
Business Intelligence Analyst
Deep Learning Engineer
NLP Engineer
Computer Vision Engineer
Analytics Consultant
Research Scientist
AI Product Engineer

Top Recruiters

Some of the leading recruiters hiring professionals with AI and Data Science expertise include:

Google
Microsoft
Amazon
NVIDIA
Oracle
IBM
Adobe
Intel
Qualcomm
TCS
Infosys
Wipro
Accenture

Preparation Tips to Crack GATE DA

Complete the entire syllabus systematically.
Strengthen your mathematics fundamentals.
Practice Python programming regularly.
Solve previous years’ GATE questions.
Attempt full-length mock tests.
Revise formulas and concepts weekly.
Focus on accuracy and time management.
Analyze mock test performance to identify weak areas.

GATE Data Science & Artificial Intelligence (DA) – Frequently Asked Questions (FAQs)

1. What is GATE Data Science & Artificial Intelligence (DA)?

GATE DA is a specialized GATE paper introduced in 2024 for students interested in Data Science, Machine Learning, Artificial Intelligence, and related fields. It is conducted as one of the official GATE test papers.

2. Who can apply for GATE DA?

Candidates pursuing or having completed a bachelor’s degree (3rd year or above) in Engineering, Technology, Science, Arts, Commerce, or Humanities are eligible as per the GATE eligibility criteria.

3. What is the syllabus of GATE DA?

The syllabus consists of seven core subjects:

Probability and Statistics
Linear Algebra
Calculus and Optimization
Programming, Data Structures & Algorithms
Database Management and Warehousing
Machine Learning
Artificial Intelligence
Along with General Aptitude (15 Marks).

4. What is the exam pattern of GATE DA?

Mode: Computer-Based Test (CBT)
Duration: 3 Hours
Total Questions: 65
Total Marks: 100
General Aptitude: 15 Marks
Core Subject: 85 Marks
Question Types: MCQ, MSQ and NAT.

5. Is there negative marking in GATE DA?

Yes. Negative marking is applicable only for MCQs:

1-mark MCQ: −1/3 mark
2-mark MCQ: −2/3 mark
There is no negative marking for MSQ and NAT questions.

6. Which programming language is used in GATE DA?

The official syllabus includes Python Programming. Candidates should have a good understanding of Python syntax, data structures, and algorithms.

7. Which subjects carry the highest weightage in GATE DA?

Generally, the highest weightage is from:

Programming, Data Structures & Algorithms
Probability & Statistics
Machine Learning
Linear Algebra
These subjects together contribute a major portion of the paper.

8. Is Engineering Mathematics a separate section in GATE DA?

No. Unlike many other GATE papers, Engineering Mathematics is not a separate section. Mathematics topics are integrated into the DA syllabus through Probability, Statistics, Linear Algebra, and Calculus.

9. Which IITs accept GATE DA scores?

Several IITs accept GATE DA scores for admission to M.Tech programmes in Artificial Intelligence, Data Science, Machine Learning, Computer Science, Computational Engineering, and related interdisciplinary programmes. Acceptance varies by institute and programme.

10. What is a good GATE DA score for admission to IITs?

A score of 700+ is generally competitive for many IIT programmes, while 800+ significantly improves the chances of admission to top institutes and highly competitive AI/Data Science programmes.

11. How long is the GATE score valid?

A GATE score remains valid for three years from the date of the announcement of results.

12. Can I appear for two GATE papers along with DA?

Yes. Candidates may appear for up to two GATE papers, provided the chosen paper combination is permitted by the organizing institute.

13. What are the best books for GATE DA preparation?

Some highly recommended books include:

Introduction to Linear Algebra – Gilbert Strang
A First Course in Probability – Sheldon Ross
Pattern Recognition and Machine Learning – Christopher Bishop
Artificial Intelligence: A Modern Approach – Russell & Norvig
Data Structures and Algorithms in Python – Goodrich, Tamassia & Goldwasser

14. What career opportunities are available after qualifying GATE DA?

Qualified candidates can pursue:

M.Tech/MS in AI, Data Science, and Machine Learning
Ph.D. programmes
Research positions
AI Engineer
Machine Learning Engineer
Data Scientist
Data Analyst
Business Intelligence Analyst
Research Scientist

15. How should I prepare for GATE DA?

A good preparation strategy includes:

Build a strong foundation in Mathematics.
Master Python programming and Data Structures.
Study Machine Learning and AI concepts thoroughly.
Solve previous year questions.
Take regular mock tests.
Revise formulas and key concepts consistently

GATE Data Science & AI

GATE Data Science & AI 2027: Syllabus, Eligibility, Exam Pattern, Books, Cut-Off, IIT Courses, Safe Score & Paper Analysis

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