The GATE 2025 syllabus for Data Science and Artificial Intelligence (DA) covers a blend of core computer science, machine learning, statistics, and artificial intelligence topics. This paper is relatively new and has been introduced to reflect the growing demand for AI and data-related skills in both academia and industry. The syllabus includes programming, data structures, linear algebra, probability, deep learning, and more—making it different from traditional CSE GATE papers.
I’m writing about this topic because a lot of students are confused about how this paper is different from Computer Science (CS) and how to prepare for it. Since the DA paper is still new, not everyone has a clear idea of what exactly to expect. Many coaching institutes also mix up CS and DA topics, which leads to gaps in preparation. The best way to begin your preparation is to go through the official syllabus first and understand what’s included, what’s not, and how the paper is structured. This will help you stay focused and avoid the mistake of studying unrelated topics or spending time on too much theory without practising numerical questions. In this article, I’ll walk you through the exam pattern, give tips on how to use the syllabus smartly, and provide the link to download the official syllabus PDF.
GATE 2025 DA Exam Pattern
The Data Science and Artificial Intelligence paper has its own unique structure, although it shares some similarities with other GATE papers:
- Total Marks: 100
- Exam Duration: 3 hours
- Sections in the Paper:
- General Aptitude – 15 marks
- Data Science and AI subjects – 85 marks
- Question Types:
- Multiple Choice Questions (MCQ)
- Multiple Select Questions (MSQ)
- Numerical Answer Type (NAT)
There is no sectional timing. You can attempt the questions in any order within the 3-hour window. Time management is crucial since the paper mixes theoretical understanding with application-based questions.
Why You Must Understand the Syllabus First
The DA paper is different from the regular CS paper in several ways. While CS focuses more on core computer science subjects like operating systems, compiler design, and networks, the DA paper leans more into statistics, linear algebra, machine learning, and AI-related algorithms. If you try to study for this exam using a CS approach, you’ll end up leaving out entire topics that are key for DA.
Knowing the syllabus will help you prepare the right way. For example, topics like supervised and unsupervised learning, dimensionality reduction, optimisation, probability distributions, and graph theory are explicitly mentioned. If you start preparing without knowing that, you might miss topics that regularly appear in the paper. I’ve seen students make this mistake in the first year when the DA paper was introduced.
The syllabus also helps you pick the correct reference books. For machine learning, you might choose Bishop or Hastie’s books. For statistics and probability, books like Sheldon Ross can be helpful. And for coding-based sections, practising problems from platforms like Leetcode or Codeforces (focusing on data structures) can be helpful.
Download PDF
You can download the GATE 2025 Data Science and Artificial Intelligence syllabus PDF from the official GATE organising institute’s website. Here’s the download link:
Click here to download the syllabus PDF
It’s a good idea to keep a copy saved on your phone or pinned near your study table so you can check off topics as you complete them.
Final Words
GATE DA is a good opportunity if you’re aiming for data-focused roles or research areas in AI and machine learning. But don’t approach it the same way as GATE CS. This paper has its own focus areas, and the level of math involved—especially in probability, linear algebra, and statistics—is quite deep. Start by understanding the syllabus clearly, divide it into weekly targets, and keep solving topic-wise questions. If you’re confused about where to begin or need book recommendations or a study plan, feel free to reach out. I’ll be happy to share what has worked for other students preparing for the same paper.
