| Updated On - Jul 4, 2024
DA is a new paper that was introduced in 2024. The syllabus for AI & DS consists of three types of questions asked in the DA Paper: multiple-choice questions (MCQs), multiple-select questions (MSQs), and numerical answer type (NAT) questions. The DS & AI exam is worth 100 marks, with each question worth one or two marks.
| Table of Contents |
Detailed Syllabus for Data Science and AI
The exam pattern for AI & DS Engineering GATE 2025 consists of 65 questions worth 100 marks. The candidates are given three hours to complete the questions. Negative marking is only applicable to multiple-choice questions (MCQs).
| 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: mergesort, 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 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, k-nearest neighbor, 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; (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. |
Important Sections to Note
| Important Sections | Topics |
|---|---|
| Section 1: Engineering Mathematics | Linear Algebra |
| Calculus | |
| Differential Equations | |
| Vector Analysis | |
| Complex Analysis: | |
| Probability and Statistics | |
| Section 2: Networks, Signals and Systems | Circuit analysis |
| Continuous-time signals | |
| Discrete-time signals | |
| Section 3: Electronic Devices | Carrier transport |
| Section 4: Analog Circuits | Diode circuits |
| BJT and MOSFET amplifiers: | |
| Op-amp circuits | |
| Section 5: Digital Circuits | Number representations: |
| Sequential circuits | |
| Data converters | |
| Semiconductor | |
| Computer organization | |
| Section 6: Control Systems | Basic control system components |
| Feedback principle | |
| Transfer function | |
| Section 7: Communications | Random processes |
| Analog communications | |
| Information theory | |
| Digital communications | |
| Section 8: Electromagnetics | Maxwell's equations |
| Plane waves and properties | |
| Transmission lines |
Preparation Tips from GATE AIR 1- Raja Majhi
There are following tip for the candidates who would be appearing for the GATE 2025: Make Notes for formulas: It is a good practice to write formulas as by writing and solving the formulas and their derivation, would make you learn and memorize the formulas. Solving Test Series: If you solve the test series and sample papers this will boost up the confidence of the candidates and would make them realize about the mistakes committed in the test series which will improve them for the main exam. Online Lectures: Watching the online lectures with the physical coaching would act as a booster and help the candidates to solve any practice problems given on the same day. Improving Time Management: The exam has set time limitations for answering all questions, so practicing the previous year papers in a regular basis assists applicants in improving their ability to solve questions within the time limit.
Which topics are common between the CSE syllabus and ML, AI & Data Science? I want to prepare those topics first so it can help for simultaneous preparation of GATE and placements.
To prepare for both GATE and your placements, focusing on the common topics between the Computer Science Engineering (CSE) syllabus and specializations in Machine Learning (ML), Artificial Intelligence (AI), and Data Science will be beneficial. Here are the main overlapping areas:
- Programming Languages and Data Structures: Core programming concepts, languages like Python and Java, and data structures are fundamental in both CSE and AI/ML/Data Science.
- Algorithms: Learning about algorithms, their design, and analysis is crucial. This includes sorting, searching, and optimization techniques.
- Mathematics: Mathematics, particularly linear algebra, probability, statistics, and calculus, is essential for understanding machine learning algorithms and data science models.
- Database Systems: Knowledge of databases, SQL, and data warehousing is important as managing and querying large datasets is common in both fields.
- Operating Systems: Concepts related to operating systems, such as process management, memory management, and file systems, are part of the core CSE curriculum and are relevant in AI/ML for system-level optimization and resource management.
- Computer Networks: Understanding the principles of computer networks is useful, especially for data transfer and communication in distributed systems and IoT applications.
- Machine Learning: Basic machine learning topics like supervised and unsupervised learning, decision trees, clustering, and regression models are included in both CSE and specialized courses.
- Artificial Intelligence: Fundamental AI topics like search algorithms, knowledge representation, and basic neural networks are covered in both CSE and AI specializations.
- Data Science: Topics such as big data analytics, data visualization, and data mining are part of the CSE curriculum with a focus on data science.
- Software Engineering: Concepts of software development life cycles, testing, and project management are common and crucial for developing reliable AI and data science applications.
Topic-wise Weightage and Marking Scheme
| Sections | Total Questions | Total Marks |
|---|---|---|
| General Aptitude | 5+5 | 5 Questions carry 1 Marks (5 x 1) plus 5 Questions carry 2 Marks (5 x 2) = 15 |
| Core Discipline | 25+30 | 25 Questions carry 1 Marks (25 x 1) plus 30 Questions carry 2 Marks (30 x 2) = 85 |
| Total | 30 | 100 |
Previous Year Question Papers for Data Science and Artificial Intelligence
The following is the previous question paper:
| Year | Original Paper | Answer key |
|---|---|---|
| 2024 | DA Question Paper 2024 | DA Answer Key 2024 |
Books to refer for Data Science and AI Syllabus
The students can refer the following table for the list of books for preparation. The names of the books are mentioned below:
| Book | Author |
|---|---|
| Artificial Intelligence: A Modern Approach | Textbook by Peter Norvig and Stuart J. Russell |
| ‘Deep Learning’ | by Ian Goodfellow, Yoshua Benjio, Aaron Courville |
| Introduction to Data Science: Practical Approach with R and Python | B. Uma Maheswari (Author), R. Sujatha (Author) |
| Data Science for Dummies | Lillian Pierson (Author), Jake Porway (Foreword) |
| Data Science from Scratch: First Principles with Python | Joel Grus |
Frequently Asked Questions
Ques. What is the exam pattern for GATE DA and AI 2025?
Ans. The exam for DA and AI 2025 consists of GA and Subject based sections for a total mark of 100.
Ques. What is a good score in GATE 2025?
Ans. A score of 90+ is considered the a good score since it drastically increases the chances of admission.
Ques. What are the types of questions asked?
Ans. The questions based on GATE 2025 consist of three types: multiple-choice (MCQ), multiple-select (MSQ), and numerical answer type (NAT).
Ques. Is there any negative marking in the exam?
Ans. Yes, there is negative marking in the exam Data Science and AI 2025, but only for MCQs.
*The article might have information for the previous academic years, which will be updated soon subject to the notification issued by the University/College.
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