Cracking the GATE DA 2027 exam requires a smart plan. The GATE Data Science and Artificial Intelligence paper is one of the fastest growing papers. Whether you are looking for an MTech seat at IIT or a PSU job, understanding the syllabus is the first step.
The GATE Data Science and Artificial Intelligence (DA) paper was introduced in the GATE exam in 2024 by the Indian Institute of Science, Bangalore. This new paper is for students who want to build careers in Data Science, Machine Learning, and Artificial Intelligence.
So, if you are someone who is aiming for GATE 2027 with the DA paper, this is the right time to start your preparation. With the competition increasing every year, early preparation helps to score well in the exam.
So, in this blog, we will help you plan your preparation for GATE Data Science and Artificial Intelligence. Because good planning makes all the difference.
GATE 2027 Important Highlights
The official dates for the GATE 2027 exams are not out as of yet. So, aspirants must keep an eye on the IIT Madras GATE official website for exam updates. Let us see some important highlights of GATE 2027 exam from the table below:
|
Particulars |
Details |
|---|---|
|
Full Name |
Graduate Aptitude Test in Engineering |
| Conducting Body |
IIT Madras |
|
Exam Date |
February 2027 (tentative) |
| Notification Date |
July 2026 (expected) |
|
Application Window |
Aug – Sept 2026 (tentative) |
| Mode |
Online (Computer Based Test) |
|
Question Types |
MCQ, MSQ and Numerical Answer Test |
| Number of Papers |
Tentatively 30 branches of Engineering and science |
|
Eligibility |
Relaxed to 10+2+3 (ongoing)- 3 rd year undergraduate students can apply |
| Age Limit |
No age limit |
|
Purpose |
Admission to MTech/ME/PhD at IITs, NITs, IIITs. Also used for PSU recruitment. |
Syllabus of Data Science and Artificial Intelligence
GATE on their official website has uploaded the syllabus pdf briefly mentioning the topics, which somewhere eases the students to filter out the topics to prepare for.
| GATE New Test Paper on (DA) Data Science and Artificial Intelligence 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: 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 modelling: schema for multidimensional data models, concept hierarchies, measures: categorization and computations. |
| Machine Learning |
|
| 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 Data Science and Artificial Intelligence Preparation Plan
A proper preparation plan is the key to cracking GATE 2027 with subjects like DA, with a good score. Since the syllabus includes multiple technical subjects along with Mathematics and Aptitude, having a structured study plan helps you cover all topics effectively and manage your time wisely. So, now, we will explore a complete preparation plan to help you smartly prepare for GATE DA.
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- Comprehending the DA syllabus: Before preparing for any competitive exam, it is very important to look for the syllabus and determine which concepts need to be covered. While the GATE Data Science and AI syllabus has already been discussed, it is also important to identify the significant topics and lay a solid basis for the DA exam to expect the best outcome.
- The study schedule: You must make a proper study plan. For this, understand the syllabus well. A time table will help you focus and manage time easily.
- Choosing the appropriate study resource: Selecting the right books, online classes, or study materials is very important for effective preparation. You can refer to MADE EASY for the same. Once you choose your study method, try to follow it properly instead of switching between too many resources. This helps you stay focused and prepare in a more organized way.
- Understanding the basics: Knowing the basics for each topic is very important. Make sure you know the basics well.
- Practical Knowledge: Reading and revising the concepts, making notes, and memorizing all these are essential. However, you must not just read and memorise. It’s also necessary to practice coding and apply concepts using well-known libraries like TensorFlow, scikit-learn, and Python.
- Revising and Mock Tests: Frequent revision of the concepts enhances the vast knowledge that you have studied. Memorizing helps the mind retain knowledge. A mock test provides you with an environment similar to the real examination, which really helps. Check out the MADE EASY GATE mock test series to revise during your GATE DA preparation.
- Staying updated with the current trends: Data Science and Artificial Intelligence are changing very quickly. Read blogs and watch expert sessions. Moreover, you can follow industry updates and learn about new tools and techniques to help you during GATE 2027 preparation.
- Healthy Lifestyle and Stress Management: It is very usual to experience worry and anxiety during the preparation process. And, while studying, the majority of students neglect their health. Maintaining a healthy diet and controlling your stress levels will not only help you in the long run.
How to make a time table for GATE DA 2027?
Let us see a time table guide for GATE DA 2027 preparation. Students can take reference from this time-table and make their own:
| Phase | Duration | Focus Areas | Daily Target |
|---|---|---|---|
| Phase 1 (foundation) | Aug – Sep 2026 | Probability & Statistics, linear algebra, calculus & optimization- build the base first | 4-5 hours/day |
| Phase 2 – core technical | Oct – Nov 2026 | Programming & DSA (python), DBMS & warehousing, SQL & NoSQL basics | 5-6 hours/day |
| Phase 3 – high weightage | Nov – Dec 2026 | Machine learning and probability are high weightage – prioritise them. Cover AI (search logic, bayesian networks | 6 hours/day |
| Phase 4 – Revision + PYQs | Nov – Dec 2026 | Full syllabus revision, PYQs, weak topic reinforcement | 6-7 hours/day |
| Phase 5- Mock Tests | Feb 2027 (pre exam) | Full-length mock tests, time management, General Aptitude | 1 mock/day |
Pro tip: Pick the topics that carry most marks. However, you must not ignore General Aptitude tests such as basic math, reasoning and English. Give yourself 1 day per week for revision and mock tests.
MADE EASY GATE 2027 DS & AI Course
Keeping in mind the growing future scope of Data Science and Artificial Intelligence, MADE EASY has launched a specially designed GATE 2027 DS & AI course to help GATE 2027 aspirants prepare effectively and systematically for the exam. Let’s explore the features of this amazing course.
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- Complete Syllabus Coverage
- Experienced Faculty
- Interactive Learning Experience
- Live Online Classes
- DA Test Series
- Quality Study Material
- Doubt Support
🚀 Start Your GATE DA 2027 Preparation – Watch Full Strategy Video
Final Thoughts
So, dear aspirants, Data Science and Artificial Intelligence offer great opportunities for students who want to build a career in AI and Data Science. With the right study plan you can prepare effectively for GATE 2027.
Stay focused, stay consistent, and give your best for the exam.
All the best for GATE 2027!
FAQs
1. How many months do I need to prepare for the GATE DA 2027?
Ans: You will need a 10-month preparation timeline for the GATE DA 2027. If you start now, you have 8 months more which is enough if you can study for 5-6 hours daily.
2. What is the difference between GATE DA and GATE CS?
Ans: GATE CS focuses on computer systems (OS, networking, compilers, hardware) with C programming. While, GATE DA focuses on data science (ML, AI, statistics, databases) with Python. DA excludes hardware and software system topics.
3. How can I use mock tests and PYQs to get the best results?
Ans: Practicing PYQs of GATE previous year papers helps to understand question patterns. Time your mock tests to check speed. Also practice python coding. You can also work on small AI projects like building a simple model to apply your concepts.
4. What are the topics that carry high marks for GATE DA?
Ans: Probability and statistics together carry roughly 25-30 marks. The programming and DSA is the highest weighted core subject carrying 21 marks. Moreover, Machine learning and Linear Algebra also carry weightage.
5. How important is General Aptitude for GATE DA?
Ans: General Aptitude is important for GATE DA. You can achieve 15/15 marks in General Aptitude with 2-3 weeks of focused practice.
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