GATE 2025 Data Science and Artificial Intelligence is a new paper added by GATE Authorities last year. In GATE 2024, 52,493 candidates registered for the GATE 2024 DA paper even though the DA paper was added to the GATE examination for the first time. 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).
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. IIT, Roorkee is conducting the GATE 2025 examination, which will include 30 subjects.
- GATE Data Science and Artificial Intelligence Syllabus 2025
1.1 GATE Data Science and AI General Aptitude
1.2 GATE Data Science and AI Syllabus
1.3 Mathematics for Data Science
1.4 Programming and Data Structures
1.5 Data Handling and Analysis
- GATE AI Weightage Subject Wise
- GATE Data Science and AI Scoring
- GATE Data Science And AI Preparation Strategies
4.3 General Tips from Toppers:
GATE Data Science and Artificial Intelligence Syllabus 2025
There are two sections in Data Science and AI GATE syllabus. They're General Aptitude and Data Science. The weightage is also different. The General Aptitude section has 15% weightage and Data Science has 85% weightage. We will review the details of both sections separately.
Check some GATE Previous Question Papers:
| Exam Year | GATE Previous Year Question Papers with Solutions |
|---|---|
| GATE 2024 | Download Question Paper |
| GATE 2023 | Download Question Paper |
| GATE 2022 | Download Question Paper |
| GATE 2021 | Download Question Paper |
| GATE 2020 | Download Question Paper |
GATE Data Science and AI General Aptitude
The General Aptitude section of the GATE Data Science and Artificial Intelligence Syllabus 2025 is similar to the remaining papers. It carries a weightage of 15 percent and includes the following topics:
| GATE 2025 DA Syllabus for General Aptitude | |
|---|---|
| Topics | Subtopics |
| Verbal Aptitude | Basic English grammar: tenses, articles, adjectives, prepositions, conjunctions, verb noun agreement, and other parts of speech Basic vocabulary: words, idioms, and phrases in context Reading and comprehension Narrative sequencing |
| Quantitative Aptitude | Data interpretation: data graphs (bar graphs, pie charts, and other graphs representing data),2 and 3-dimensional plots, maps, and tables Numerical computation and estimation: ratios, percentages, powers, exponents and logarithms, permutations and combinations, and series mensuration and geometry elementary statistics and probability |
| Analytical Aptitude | Logic: deduction and induction, analogy, numerical relations and reasoning |
| Spatial Aptitude | Transformation of shapes: translation, rotation, scaling, mirroring, assembling, grouping, Paper folding, cutting, and patterns in 2 and 3 dimensions |
GATE Data Science and AI Syllabus
The GATE Data Science and AI syllabus covers several core topics, balancing foundational knowledge with advanced concepts. The GATE Data Science and Artificial Intelligence subjects cover a range of topics that are important for understanding and excelling in the field. Some of the key GATE Data Science and Artificial Intelligence subjects included in the syllabus are:
- Probability and Statistics
- Linear Algebra
- Calculus and Optimization
- Programming, Data Structures, and Algorithms
- Database Management and Warehousing
- Machine Learning
- AI (Artificial Intelligence)
Mathematics for Data Science
- Linear Algebra: Vectors, matrices, eigenvalues, and eigenvectors.
- Calculus: Differentiation, integration, and optimization techniques.
- Probability and Statistics: Random variables, probability distributions, hypothesis testing, and statistical inference.
Programming and Data Structures
- Programming Languages: Proficiency in Python and R is essential.
- Data Structures: Understanding arrays, linked lists, trees, graphs, and hash tables.
- Algorithms: Familiarity with sorting, searching, and basic algorithm design.
Data Handling and Analysis
- Data Preprocessing: Techniques for cleaning, transforming, and manipulating data.
- Exploratory Data Analysis (EDA): Methods for summarizing and visualizing data.
- Data Visualization: Utilizing libraries (e.g., Matplotlib, Seaborn) and tools (e.g., Tableau) for effective visualization.
Machine Learning and AI
- Supervised Learning: Regression, classification, decision trees, and support vector machines (SVM).
- Unsupervised Learning: Clustering techniques, dimensionality reduction, and association rules.
- Deep Learning: Understanding neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transfer learning.
- Model Evaluation: Techniques for cross-validation and metrics (accuracy, precision, recall, F1 Score).
Big Data Technologies
- Introduction to Big Data: Concepts, challenges, and popular tools.
- Frameworks: Understanding Hadoop, Spark, and distributed computing.
- NoSQL Databases: Key Value stores, document stores, and graph databases.
Ethics and Responsible AI
- Ethical Considerations: Addressing bias in AI, data privacy, and security issues.
- Regulatory Frameworks: Familiarity with data protection laws and guidelines.
We have provided a table below with the details of all the subjects included in the GATE Data Science and Artificial Intelligence syllabus.
| Topics | Details |
|---|---|
| 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 |
| 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, quick sort; introduction to graph theory; basic graph algorithms: traversals and shortest path. |
| Database Management and Warehousing | ERmodel, 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 modeling: schema for multidimensional data models, concept hierarchies, measures: categorization and computations. |
| Machine Learning | (i) Supervised Learning: regression and classification problems, simple linear regression, multiple linear regression, ridge regression, logistic regression, nearest neighbor, naive Bayes classifier, linear discriminant analysis, support vector machine, decision trees, bias-variance tradeoff, cross-validation methods such as leave out (LOO) cross-validation, k folds cross-validation, multilayer perceptron, feedforward neural network; (ii) Unsupervised Learning: clustering algorithms, k means/k medoid, hierarchical clustering, top-down, bottom-up: single linkage, multiple linkage, 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 AI Weightage Subject Wise
You will receive different questions in the GATE exam. Preparing for GATE is very important for your success. The types and number of questions vary and their weightage are:
| Chapter/Topic | Weightage (%) |
|---|---|
| Mathematics for Data Science | 20% |
| Linear Algebra | 5% |
| Calculus | 5% |
| Probability and Statistics | 10% |
| Programming and Data Structures | 15% |
| Python Programming Constructs | 5% |
| Data Structures | 5% |
| Algorithm Complexity Analysis | 5% |
| Data Handling and Analysis | 15% |
| Data Preprocessing | 5% |
| Exploratory Data Analysis (EDA) | 5% |
| Data Visualization | 5% |
| Machine Learning and AI | 30% |
| Supervised Learning | 10% |
| Unsupervised Learning | 5% |
| Deep Learning | 10% |
| Model Evaluation | 5% |
| Big Data Technologies | 10% |
| Hadoop Ecosystem | 5% |
| NoSQL Databases | 5% |
| Ethics and Responsible AI | 5% |
| Interdisciplinary Applications | 5% |
| NLP | 3% |
| Computer Vision | 2% |
GATE Data Science and AI Scoring
The GATE Data Science and AI exam includes multiple choice questions (MCQs), multiple select questions (MSQs), and numerical answer type questions (NATs). The questions will have 1 or 2 marks each. You will also have negative marks for incorrect answers.
GATE Data Science And AI Preparation Strategies
To excel in the GATE Data Science and AI examination, candidates can follow these strategies:
- Study Resources: Utilize textbooks, online courses, and tutorial videos that comprehensively cover the syllabus.
- Mock Tests: Practice with mock exams to familiarize yourself with the exam format and improve time management.
- Group Study: Collaborating with peers can enhance understanding and provide diverse perspectives on complex topics.
Here’s the revised study plan for the GATE Data Science and AI exam, including suggested study times for both 6-month and 3-month preparations in a tabular format:
6-Month Study Plan
| Month | Focus Areas | Study Plan | Time | Tips from Toppers |
|---|---|---|---|---|
| 1 | Mathematics, Programming, Data Structures | Week 1 and 2: Linear Algebra and Calculus review. Week 3 and 4: Probability and Statistics exercises. Revise Python and R basics. | 23 hours/day | Create flashcards for formulas; join online forums for doubt clearance. |
| 2 | Data Handling and Analysis | Week 1 and 2: Learn data cleaning techniques. Week 3: Perform EDA on datasets. Week 4: Data visualization with Matplotlib and Seaborn. | 23 hours/day | Use Kaggle datasets for practical experience; document projects. |
| 3 | Machine Learning and AI | Week 1: Supervised Learning (Regression, SVM). Week 2: Unsupervised Learning (Clustering, PCA). Week 3: Deep Learning concepts. | 34 hours/day | Create a cheat sheet for algorithms; engage in study groups. |
| 4 | Big Data Technologies and Ethics | Week 1: Basics of Hadoop and Ecosystem. Week 2: Study Spark and distributed computing. Week 3: Data privacy and ethical AI practices. | 23 hours/day | Watch online tutorials; read case studies on ethical issues. |
| 5 | Revision and Mock Tests | Week 1: Revise Mathematics and Programming. Week 2: Revise Data Handling and ML concepts. Week 3: Take full-length mock tests. | 34 hours/day | Simulate exam conditions during mocks; practice time management. |
| 6 | Final Revision | Week 1: Final revisions of weak areas. Week 2: Focus on all topics, mock tests, and analyze performance. | 23 hours/day | Prioritize revision based on test results; stay calm and focused. |
3-Month Study Plan
| Month | Focus Areas | Study Plan | Time Commitment | Tips from Toppers |
|---|---|---|---|---|
| 1 | Mathematics, Programming, Data Structures | Week 1: Cover Linear Algebra and Probability. Week 2: Focus on Calculus and Statistics. Week 3: Revise Python and data structures. | 34 hours/day | Daily problem solving; utilize online coding platforms for practice. |
| 2 | Core Data Science Topics | Week 1: Learn data preprocessing techniques. Week 2: Focus on EDA and visualization. Week 3: Dive into supervised learning. | 34 hours/day | Create summary notes for algorithms; engage in online discussions. |
| 3 | Revision, Mock Tests, Final Touches | Week 1: Revise all mathematics topics. Week 2: Review data science and ML concepts. Week 3: Take multiple mock tests. | 45 hours/day | Prioritize revision based on mock results; manage exam stress. |
General Tips from Toppers:
- Consistency: Stick to your study schedule.
- Practice: Solve previous years' papers regularly.
- Resources: Use a variety of study materials (textbooks, online courses).
- Discussion: Engage with peers or online forums for doubts.
- Health: Maintain a balanced diet and take regular breaks.
This structured approach with time commitments will help candidates prepare effectively for the GATE Data Science and AI exam.
*The article might have information for the previous academic years, which will be updated soon subject to the notification issued by the University/College.
Comments