GATE Data Science and Artificial Intelligence (DA) Syllabus 2026

Summary:  The GATE DA syllabus 2026 covers a wide range of topics, including Probability, statistics, linear algebra, algorithms, Programming, Data Structures, database management systems, warehousing, machine learning, and Artificial intelligence.

GATE DA syllabus 2026: Candidates who are planning to attempt the exam must be familiar with the GATE Syllabus for Data Science and Artificial Intelligence. We have included detailed topics in this article to help readers better understand the syllabus and make a well-planned study schedule.

Read more: How To Prepare For GATE Exam – Common Challenges And How To Overcome Them.

 

GATE DA syllabus 2026

GATE DA syllabus 2026 is divided into seven sections. These sections include topics such as Probability and Statistics, linear algebra, Calculus and Optimization, and Machine Learning and Artificial Intelligence.

 

The detailed table provided below provides a more detailed look at the GATE DA Syllabus 2025.

Read more: GATE Syllabus: GATE Subject Wise Syllabus.

 

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.
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, hash tables; Search algorithms: linear search and binary search; basic sorting algorithms: selection sort, bubble sort and insertion sort; divide and conquer: merge sort, quicksort; introduction to graph theory; basic graph algorithms: traversals and shortest path.
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.
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;         2.    Unsupervised Learning: clustering algorithms, k-means/k-medoid, hierarchical clustering, top-down, bottom-up: single-linkage, multiple linkages, dimensionality reduction, principal component analysis.
AI	Search: informed, uninformed, adversarial; logic, propositional, predicate; reasoning under uncertainty topics – conditional independence representation, exact inference through variable elimination, and approximate inference through sampling

 

GATE DA syllabus: The Data Science and AI GATE exam is not just an examination; it’s a gateway to a future powered by innovation, insight, and intelligence. The rewards are abundant for engineering graduates who dare to step into this dynamic world – from lucrative career prospects to the thrill of shaping the future. So, if you are an engineering graduate with a thirst for knowledge and a passion for technology, consider taking the Data Science and AI GATE exam – your pathway to unlocking a world of limitless opportunities.

 

Read More: GATE Eligibility Criteria 2025 – Age Limit, Qualification, Marks, Documents Required

GATE DA Syllabus 2026: Preparation Tips 

To prepare effectively for the GATE 2025 with a focus on AI and DS, it’s important to have a strategic approach. Here’s a breakdown of some smart strategies:

 

1. Understanding the Exam Structure: You should start by getting to understand the structure and content of the GATE 2025 exam. This means familiarize yourself with the types of questions that can be asked in the examination, the topics covered, and the overall format. This step ensures that you have initiated the process and that you are not caught off guard. You can prepare for the GATE DA exam accordingly.

 

2. Structured Study Plan: It is important to create a study schedule that you can follow. This does not mean allocating time daily for study, but doing it in such a way that you learn about its importance and know what and when you study, ensuring a balanced approach to all topics.

 

3. Prioritize Key Topics: The sections and topics are distributed in weightage in any examination; similarly, the topics are divided in the GATE exam. It is necessary to identify which topics are significant or given more emphasis in the past GATE DA paper. Dedicating more time and effort to these sections impacts your overall score, so decide wisely!

 

4. Selecting the Right Materials: When you try to search for study materials, for instance, on Google or the Internet in general, several materials are available, but not all are helpful or relevant. Choose study sources that are updated, comprehensive, and align well with the GATE preparation.

 

5. Play to Your Strengths, Work on Your Weaknesses: It is an unsaid rule that you must know your strengths and weaknesses effectively to prepare well for the exam. Recognize the sections you are strong in and maintain those strengths. By identifying your weaknesses, you can invest more time in understanding, improving, and overcoming those challenges.

         

6. Practice Makes Perfect: Practising with mock tests is a handy resource for familiarizing yourself with the exam format. These simulate the real exam environment, helping to reduce anxiety and improve time management skills during the actual exam.

 

By following these tailored strategies, you are preparing for GATE 2025 and setting a foundation for success in the AI and DS fields.

 

By following these planned strategies, you are preparing for GATE 2025 and setting a foundation for success in the AI and DS fields.

Read More: GATE Marking Scheme 2025: Subject Wise Paper Pattern
GATE Data Science & Artificial Intelligence (DA) Analysis 2024

 

FAQs: GATE DA Syllabus 2026 (Data Science and Artificial Intelligence)

What is the GATE DA exam?

The GATE DA (Data Science and Artificial Intelligence) exam is a national-level test that evaluates your understanding of data science, machine learning, AI, programming, and mathematics. It is designed for candidates who want to pursue higher studies or a career in data-driven fields.

 

What are the main subjects in the GATE DA syllabus 2026?

The GATE DA syllabus covers seven major sections:

1. Probability and Statistics
2. Linear Algebra
3. Calculus and Optimization
4. Programming, Data Structures, and Algorithms
5. Database Management and Warehousing
6. Machine Learning
7. Artificial Intelligence

 

What are the important topics under Probability and Statistics?

Key topics include:

• Permutations and Combinations
• Bayes’ Theorem
• Random Variables and Distributions (Binomial, Normal, Poisson, etc.)
• Mean, Variance, and Standard Deviation
• Hypothesis Testing (z-test, t-test, chi-square test)
• Central Limit Theorem and Confidence Intervals

 

What programming languages are included in the GATE DA syllabus?

The syllabus mainly focuses on Python. You’ll also need a strong grasp of data structures like stacks, queues, linked lists, trees, and algorithms like sorting, searching, and graph traversal.

 

What is covered in the Machine Learning section?

The ML section includes:

• Supervised Learning: Regression, Logistic Regression, SVM, Decision Trees, Neural Networks
• Unsupervised Learning: Clustering, PCA, Dimensionality Reduction
• Model Evaluation: Cross-validation, Bias-Variance Tradeoff

 

What are the AI topics in the syllabus?

The AI section covers:

• Search Techniques (informed, uninformed, adversarial)
• Logic (propositional and predicate)
• Reasoning under uncertainty
• Inference methods like variable elimination and sampling

 

How should I prepare for the GATE DA exam?

Here are a few effective strategies:

• Understand the exam pattern and topic weightage
• Follow a structured study plan
• Focus on important topics first
• Use standard books and updated online resources
• Practice mock tests and previous year papers

 

Is there a negative marking on the GATE DA exam?

Yes, GATE follows a negative marking scheme for MCQ-type questions:

• 1-mark questions: –0.33 for each wrong answer
• 2-mark questions: –0.66 for each wrong answer

 

Who can apply for the GATE DA exam?

Any candidate with a Bachelor’s degree in Engineering, Technology, or Science (with a relevant background in computing, mathematics, or AI) can apply. There is no age limit for appearing in the GATE DA exam.

 

What are the career opportunities after GATE DA?

After clearing GATE DA, you can pursue:

• M.Tech or Ph.D. in AI, Data Science, or related fields
• Research positions in premier institutes
• High-paying roles in data analytics, machine learning, and AI companies

 

Where can I find the official GATE DA syllabus PDF?

Once it’s released, you can download the official syllabus PDF for GATE DA 2026 from the GATE official website under the “Syllabus” section.