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    GATE Data Science and Artificial Intelligence (DA) Syllabus 2025

    20 October, 2024
    Parthiva Mewawala

    Summary: IIT Roorkee is organizing the GATE examination for 2025, and the Data Science and Artificial Intelligence (DA) paper was recently announced for GATE 2024. The GATE DA syllabus 2025 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 Data Science and AI Syllabus

    GATE DA syllabus 2025: 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

     

     

    Get GATE Exam Prepared with Free Mock Tests.

    GATE DA syllabus 2025

    GATE DA syllabus 2025 is divided into seven sections. These sections include topics such as Probability and Statistics, Linear Algebra, Calculus and Optimization, as well as Machine Learning and Artificial Intelligence among others. 

     

    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
    1. 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

     

    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: 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.

     

    1. 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.

     

    1. 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!

     

    1. Selecting the Right Materials: When you try to search for the study material, 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.

     

    1. 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, invest more time in understanding, making improvements, and overcoming those challenges.

             

    1. Practice Makes Perfect: Practising with mock tests is a very useful 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 not only preparing for GATE 2025 but also setting a foundation for success in AI and DS fields.

     

    By following these planned strategies, you are not only preparing for GATE 2025 but also 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