DSAI-GATE

A community-maintained collection of syllabus-aligned resources and practice material for the GATE Data Science and Artificial Intelligence paper.

Explore the syllabus: Interactive explorer · Overall memory map

We are still in work in progress phase, Stay tuned !!

Looking for solid-contributors checkout : contributors-guide

🚀 About the Repository

Discover the ultimate GATE (Graduate Aptitude Test in Engineering) Resource: All-in-One curated for Data Science and Artificial Intelligence (DSAI) 🌟 🌟 🌟 🌟 🌟
This repository is designed to collaborate and share resources for preparation, including study materials, online courses, and code examples that cover the DSAI Gate syllabus.

📚 Syllabus Coverage

Syllabus: Data Science and Artificial Intelligence Gate (Released by GATE 2026 organizing institute)

Our repository is meticulously organized to cover the complete syllabus outlined for the DSAI section of the GATE exam. From Probability and Statistics to Math, Programming,DSA, DBMS, Machine Learning, and AI, you'll find comprehensive resources that address each topic in detail.

Explore the power of open source including featured tutorials, course videos, books, articles, courses, websites, code examples in Python.
Theoretical explanations, practice examples, or MCQ exercises, we've got you covered in this all-encompassing guide.
We aim to present a one stop resource in this Preparation-to-Interviews guide.

Updates Created the GitHub Pages deployment workflow at .github/workflows/pages.yml.

• Tests and builds on pull requests.
• Deploys automatically after merging to main.
• Rebuilds when topics, notebooks, resources, PYQs, or webapp files change.
• Uses current GitHub Actions versions.
• Publishes to: https://ds-ai-gate.github.io/dsai-gate/
• Provides the map directly at: https://ds-ai-gate.github.io/dsai-gate/#map
• Adds deployment status and app links to README.

Verified workflow YAML, tests, and static export. In repository Settings → Pages, set Source to GitHub Actions once.

📁 Repository Structure

To ensure a smooth and efficient learning experience, we've structured the repository with the following sections:

Topic	Description	Resources
Probability and Statistics	Probability fundamentals, counting methods, key concepts in hypothesis testing	Prob-Stats
Linear Algebra	Detailed coverage on vector spaces, matrices, eigenvalues, Singular Value Decomposition(SVD)	Linear-Alg
Calculus and Optimization	Exploring single-variable functions, limits, optimization techniques, and more	Calculus-Opt
Programming and Algorithms	Comprehensive coverage of Python programming, data structures, and algorithms.	Prog-Algo
Database Management	ER-models, relational algebra, SQL, normalization	DBMS
Machine Learning	Extensive explanations and hands-on examples of supervised and unsupervised learning algorithms.	ML-Notes
Artificial Intelligence	In-depth discussions on search algorithms, logic, reasoning, and uncertainty.	AI-Notes

🌟 How to Use This Repository

1. Navigation : Use the Repository Structure to navigate to different topics and find the resources you need in Subsection-ReadMe for example Probability-Statistics-Readme.md.
2. Study Materials: In each Subsection-ReadMe dive into detailed explanations, examples, and theoretical content for each topic by opening notes.
3. Code Snippets : Explore code snippets and implementations to understand practical applications.
4. Practice : Engage with MCQ exercises, quizzes, and practice problems to reinforce your understanding inside subsections.

  Each Subsection-Readme is organised in the following format:
  [Table of Contents] 
  * Books
  * NPTEL and Courses
  * Notes
  * Articles 
  * Programming : Examples and tutorial such as Kaggle for ML, GFG for Python and Algo
  * Practice Problems
  * Interview 

🤝 Contributions

A Warm Invitation to Support and Share: Star the Repo and Spread the Word

Please consider starring 🌟 the repo and sharing it with others who might be interested. This repository is a collaborative effort, and we welcome contributions from the community. If you find any errors, have additional resources to share, or want to improve existing content, feel free to contribute through pull requests.

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🚀 Looking for contributors

Calling Data Science & AI folks!

Join the team as official contributor for DSAI-GATE prep resource. Elevate your expertise by contributing to this repository.
Your knowledge or years of industry experience is invaluable. Claim your spot now: Contribute to DSAI-GATE. Let's shape the future of GATE prep together! 💼📚 #DSAI #GATE

How to Contribute

1. Choose a topic or sub-topic that interests you from the syllabus.
2. Create comprehensive notes or resources for that topic.
3. If your content is ready, submit a pull request or open an issue to indicate your contribution.
4. Collaborate with the community to enhance and refine the content.

All repository changes, including maintainer changes, must be submitted and merged through a pull request. See the repository agent guide for the required workflow.

For any questions or clarifications, feel free to reach out!

Join our Discord Server for real-time interactions with fellow contributors.

Contributors

🧑‍🏫 DS-AI-Gate (Project) 💻

🧑‍🏫 Yash Singhal (IIT Roorkee) 💻

🧑‍🏫 Kunal Dargan (IIT Delhi) 💻

🧑‍🏫 Sarvesh Gharat (IIT Bombay) 💻

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Let's make DSAI preparation an enriching and collaborative journey together! 🚀

🔗 Connect with Us

Stay connected with us for updates, announcements, and discussions

Maintainer documentation: implementation plan | official resources and paper analysis | agent guide | stale-link audit

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GATE Data Science and AI Syllabus

GATE Data Science and AI Syllabus
Topic wise notebooks	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 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

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GATE Data Science and AI Exam Pattern

The GATE Data Science and AI exam pattern will carry a total of 100 marks. The paper will be divided into two sections, General Aptitude and Data Science and AI Subject Questions, worth 15 and 85 marks, respectively.

Check out the complete GATE DA Exam Pattern in the table outlined below.

GATE DA Exam Pattern
Particulars	Details
Paper Name	GATE Data Science and Artificial Intelligence Paper
Paper Code	DA
Exam Duration	3 Hours
Sections	General Aptitude + Data Science and AI
Type of Questions	MCQs, MSQs, and NATS
GATE DA Paper Marks Distribution	General Aptitude: 15 Marks Data Science and AI Subject Questions: 85 Mark
Negative Marking	For a 1-mark MCQ, an incorrect answer will result in a deduction of 1/3 mark. For a 2-mark MCQ, an incorrect answer will lead to a deduction of 2/3 marks. There is no penalty for incorrect responses to MSQ or NAT questions.

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Useful Collections

• Achint Chaudhary's concise gist of resouces
• Official previous-year papers and answer keys
• Recent AI Questions

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Previous Year Papers

Access previous year GATE Data Science and AI question papers and their answer keys:

2024:

• GATE DA 2024 Question Paper
• GATE DA 2024 Answer Key

2025:

• GATE DA 2025 Question Paper
• GATE DA 2025 Answer Key

2026:

• GATE DA 2026 Question Paper
• GATE DA 2026 Answer Key

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Sample Paper

IISC released a DS/AI sample paper on their website. It's expected the questions in the main exams would be on similar lines Sample Paper

Tasks

• Public repo and Landing page
• Example structure for contributions in topic notes : (Probability-Statistics-Readme.md)
• Coding example notebooks in colab for ML

Start your DSAI-GATE preparation journey today with the DSAI-GATE repository. Let's ace the GATE exam together🌟