GATE Data Science and Artificial Intelligence Syllabus 2025

GATE 2025 DA (Data Science and Artificial Intelligence) is a new paper added by GATE Authorities last year. With the addition of Data Science and Artificial Intelligence in GATE, students can choose one more field for their master’s (ME) or postgraduate engineering (M.Tech).

In this GATE Data Science and Artificial Intelligence Syllabus 2025 Guide, We have briefly explained the section-wise syllabus, eligibility criteria, exam pattern, marking scheme, exam tips, and book recommendations to help students for the upcoming GATE exam.

GATE Data Science and Artificial Intelligence Syllabus 2025

GATE Data Science and Artificial Intelligence Syllabus PDF 2025

The GATE Data Science and Artificial Intelligence Syllabus 2025 is briefly described in this article. Because the subject was only recently established, applicants should consult credible and authentic sources for the syllabus to prepare for the GATE 2025 DA Exam.

GeeksForGeeks is a reliable and trustworthy source for the GATE 2025 Data Science and Artificial Intelligence exam.

Download the latest GATE Data Science and Artificial Intelligence Syllabus PDF here “GATE Data Science and Artificial Intelligence Syllabus“

GATE 2025 Eligibility Criteria for Data Science and Artificial Intelligence

GATE Eligibility Criteria 2025: Here you will find details about the GATE 2025 Eligibility Criteria like the exam’s age restriction, nationality, relaxation, requirements, etc. To appear in the Graduate Aptitude Test in Engineering and be deemed qualified for the test, candidates must fulfill the requirements of GATE 2025. We have Summarized the eligibility criteria for the GATE 2025 Data

Science and Artificial Intelligence exam below:

Criteria	Eligibility
Nationality	Indian nationality candidates will be eligible. Candidates from other than India will be also eligible
Qualification for the GATE exam	Applicants must hold a master’s degree in any relevant science field or a diploma in engineering or technology. Applicants may also appear for GATE 2025 if they are in the pre-final or final year of their qualifying degree. Candidates from nations other than India who have earned or are working towards their qualifying degree: Must be in their third year or above, or have finished their Bachelor’s degree in Engineering, Technology, Science, Arts, or Commerce (duration: at least 3 years).
GATE Age Limit	There is no age limit for GATE 2025.
GATE Attempt	There is no constraint on the number of GATE attempts.

GATE Data Science and Artificial Intelligence (DA) Subjects

A variety of topics are covered in the GATE Data Science and Artificial Intelligence courses that are crucial for comprehending and succeeding in the discipline. The following are some of the main GATE data science and artificial intelligence topics covered in the curriculum:

• 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 2025 Data Science and AI Syllabus (Core Subjects)

The syllabus for GATE Data Science and Artificial Intelligence in 2025 is categorized into 7 sections, covering topics such as Probability and Statistics, Linear Algebra, Calculus and Optimization, Machine Learning, and AI. 

You can refer to the table below for a detailed breakdown of the GATE Data Science and Artificial Intelligence Syllabus 2025.

Sections-Wise (Topics)	Sub-Topics
Section 1: Probability and Statistics	Counting (permutation 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 and probability distribution function uniform, exponential, Poisson, normal, standard normal, t-distribution chi-squared distributions cumulative distribution function Conditional PDF Central limit theorem confidence interval z-test t-test chi-squared test.
Section 2: 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.
Section 3: Calculus and Optimization	Functions of a single variable limit, continuity and differentiability Taylor series maxima and minima optimization involving a single variable.
Section 4: Programming, Data Structures and Algorithms	Programming in Python basic data structures: stacks, queues, linked lists, trees, hash tables Search algorithms: linear search and binary search basic sorting algorithms: selection sort, bubble sort and insertion sort divide and conquer: mergesort, quicksort introduction to graph theory basic graph algorithms: traversals and shortest path.
Section 5: Database Management and Warehousing	ER-model, relational model relational algebra tuple calculus SQL integrity constraints normal form file organization indexing data types data transformation such as normalization, discretization, sampling, compression data warehouse modelling: schema for multidimensional data models, concept hierarchies measures: categorization and computations.
Section 6: Machine Learning	Supervised Learning regression and classification problems simple linear regression multiple linear regression ridge regression logistic regression k-nearest neighbour naive Bayes classifier linear discriminant analysis support vector machine decision trees bias-variance trade-off cross-validation methods such as leave-one-out (LOO) cross-validation, k-folds cross-validation, multi-layer perceptron, feed-forward neural network Unsupervised Learning: clustering algorithms k-means/k-medoid hierarchical clustering top-down boƩom-up: single-linkage, multiple-linkage, dimensionality reduction, principal component analysis.
Section 7: Artificial Intelligence (AI)	Search: informed, uninformed, adversarial Logic: Propositional Logic, Predicate Logic Reasoning under Uncertainty Topics Conditional Independence Representation Exact Inference through Variable Elimination Approximate Inference through Sampling

GATE 2025 Preparation Tips for Data Science and Artificial Intelligence

Here are some tips for cracking GATE 2025 with AI and DS:

• Before you start studying, familiarize yourself with the GATE 2025 exam pattern and syllabus.
• Create a disciplined study schedule and follow it religiously.
• Determine which GATE 2025 themes are more important and focus more on them.
• Choose relevant reference sources for your research.
• Acknowledge your strengths and concentrate on strengthening your weaknesses.
• To get accustomed to the format of the question paper, take practice exams.

GATE 2025 Data Science and Artificial Intelligence Exam Pattern

For the GATE 2025 data science and artificial intelligence exam, here is the detailed exam pattern:

GATE 2025  Artificial Intelligence and Data Science(DA) Exam Pattern
Exam Duration	3 hour
Mode of Examination	Online Computer Based Test(CBT)
Total Marks	100
Total Questions	65 Questions Split in: General Aptitude-10 questions Artificial Intelligence and Data Science(DA)-55 questions
Types of Question	MCQs(Multiple Choice Questions) MSQs(Multiple Select Questions) NAT(Numerical Answer Type Questions)
Marks Distribution	General Aptitude= 15 questions worth 25 marks Core Subject= 50 Questions worth 75 marks
Negative Marking	*Applicable only to wrongly answered MCQ -1/3 for 1 mark MCQ -2/3 for 2 mark MCQ

GATE 2025 Data Science and Artificial Intelligence Marking Scheme

please find below the revised marking scheme for gate 2025 Data Science and Artificial Intelligence:

Gate 2025 Data Science and Artificial Intelligence Marking scheme
SECTIONS	Total Questions	Marking
General Aptitude	15	5 question x 1 marks +1 marks(correct) -1/3 for incorrect answer 5 question x 2 marks +2 mark (Correct) -2/3 (incorrect) Total marks=15
Core Discipline	50	25 question x 1 marks +1 marks(Correct) -1/3 (incorrect) 25 question x 2 marks +1 mark(Correct) -1/3 mark(Incorrect) Total Marks= 85
	Total Questions = 65	Total Marks= 100

How Do I Prepare for GATE 2025 Data Science and Artificial Intelligence?

GATE is one of the toughest competitive exams, which requires practice and preparation to crack it. There are various ways to prepare for a GATE exam. Some of them are listed below.

• Make Appropriate Plans: Before beginning to study for an exam, it is always a good idea to make appropriate plans. Make weekly, monthly, and annual plans out of the plan.
• Recognize Your Strengths and Weaknesses: Knowing your strengths and weaknesses is a necessary first step before delving deeply into anything.
• Learn Well: It is quite hard to pass the test without doing your homework. We are giving you the necessary resources to study the GATE syllabus. For GATE studies, you might consult the GATE CS Notes.
• Review Thoroughly: Whether it’s for the GATE or any other exam, you must thoroughly review it to pass it. For revision, you can consult the GATE CSE brief notes and the Last Minute Notes.
• Practice Previous Year Questions: To pass any test, it is crucial to practice Previous Year Questions. We provide last year’s questions for every subject, which will aid you in your preparation.
• Practice Mock Tests: Before the real exam, mock tests assist in the analysis of the exam papers. It is also incredibly helpful for any exam. To help students get ready for the test, we also offer free practice exams.

Book Recommendations to Prepare for Gate 2025 AI and DS Exam

To crack the tough exam at the gate, one should be fully prepared. You will need good Study materials for this, so we have curated the list of best books to prepare for the GATE 2025 artificial intelligence and data science exam:

Books Recommendations to Prepare for GATE DS and AI 2025
Book	Author
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	Stuart Russell and Peter Norvig
Pattern Recognition and Machine Learning	Christopher M. Bishop
Deep Learning	Ian Goodfellow, Yoshua Bengio, and Aaron Courville
Elements of Statistical Learning	Trevor Hastie, Robert Tibshirani, and Jerome Friedman
Speech and Language Processing	Daniel Jurafsky and James H. Martin
Computer Vision: Algorithms and Applications	Richard Szeliski
Python Machine Learning	Sebastian Raschka and Vahid Mirjalili
Introduction to the Theory of Computation	Michael Sipser
Bayesian Reasoning and Machine Learning	David Barber
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow	Aurélien Géron

GATE 2025 Data Science and Artificial Intelligence Syllabus -FAQs

What is GATE DS and AI?

The Graduate Aptitude Test in Engineering for Data Science and Artificial Intelligence is referred to as GATE DS and AI. This particular GATE test is intended to evaluate applicants’ knowledge and skills in the areas of artificial intelligence and data science.

When is the GATE DS and AI Exam conducted?

The GATE DS and AI examination is conducted along with other GATE Exams. For the most recent information on exam dates and enrollment deadlines, visit the official GeeksforGeeks website or the official GATE website.

Who is conducting GATE 2025 Examination?

IIT Roorkee is going to conduct GATE 2025 Examination.

Which is better GATE CS or GATE DA?

GATE CS and GATE DA, both are important streams that will help you in shaping your career. Some part of syllabus is same in both the Papers. Any candidate can attempt both the papers.

Is GATE DA tough?

GATE DA is little bit more complex than GATE CS, as it includes a depth in Artificial Intelligence, as well as it requires solid foundation in math, machine learning and statistics.