Data Science & Artificial Intelligence

This is a complete, free study companion for Data Science & Artificial Intelligence — built around what the exam actually asks. The topics examiners repeat most are Probability Distributions: Bernoulli, Binomial, Poisson, Normal, Exponential, Expectation, Variance, Covariance and Conditional Probability, and Linear Algebra: Rank, Determinant, Inverse, Eigenvalues and Eigenvectors. The syllabus runs to 6 chapters. Below you'll find the full topic-frequency ranking, the exam paper pattern, every chapter, a step-by-step study plan, and the official downloads — everything in one place.

Everything here is free. We're an independent student resource, not the official GATE body, so always confirm the current syllabus and exam dates on the official GATE website before you rely on them.

Key information

Level: DA
Programme: Data Science & Artificial Intelligence
Exam: GATE Data Science & Artificial Intelligence Exam (DA)
Conducted by: IISc and IIT

Most frequently examined topics

#	Topic	Times asked
1	Probability Distributions: Bernoulli, Binomial, Poisson, Normal, Exponential	3
2	Expectation, Variance, Covariance and Conditional Probability	3
3	Linear Algebra: Rank, Determinant, Inverse, Eigenvalues and Eigenvectors	3
4	Algorithmic Complexity and Data Structures	3
5	Searching, Sorting, Hashing, BFS/DFS and Graph/Tree Algorithms	3
6	Decision Trees: Entropy, Information Gain and Gini Impurity	3
7	Linear and Logistic Regression: Prediction, Loss, Gradient and Decision Boundary	3
8	Bias-Variance Tradeoff, Overfitting and Regularization	3
9	Classification Metrics from Confusion Matrix	3
10	Bayes Theorem and Diagnostic Probability	2
11	Central Limit Theorem and Normal Approximation	2
12	Projection Matrices, Symmetry, Idempotence and Trace	2

Counted across the official previous-year question papers we have analysed for this subject. It shows what has been asked before — it does not predict what will appear in your exam. Always confirm the current syllabus on the official portal.

What you will study (chapters)

Chapter 1: Overview of Data Science & AI
Chapter 2: Mathematics for Data Science (Linear Algebra & Probability)
Chapter 3: Programming Tools for Data Science (Python & Libraries)
Chapter 4: Data Wrangling, Exploration & Visualization
Chapter 5: Foundations of Machine Learning
Chapter 6: Model Evaluation, Validation & Responsible AI

How to study Data Science & Artificial Intelligence and score well

Start with the highest-frequency topics — In Data Science & Artificial Intelligence, Probability Distributions: Bernoulli, Binomial, Poisson, Normal, Exponential, Expectation, Variance, Covariance and Conditional Probability, Linear Algebra: Rank, Determinant, Inverse, Eigenvalues and Eigenvectors, and Algorithmic Complexity and Data Structures appear again and again in past papers. Master these first — they return the most marks for the time you put in.
Practise with previous-year papers — Solve the last 5–10 years of GATE Data Science & Artificial Intelligence papers under timed, exam-like conditions. Past papers show exactly which topics repeat and how questions are worded.
Revise actively, not passively — Write a one-page summary for each of the 6 chapters — key definitions, formulas and the points examiners reward — then re-test yourself instead of re-reading.
Mark your answers with the official scheme — After each practice paper, score yourself against the official marking scheme. It shows how marks are awarded step-by-step, so you learn to present answers the way examiners expect.

Exam tips: how to score higher in Data Science & Artificial Intelligence

Where students lose marks: attempting questions they have not truly eliminated options for, spending too long on one hard question, and rushing the sections they know best. Read the whole paper first, bank the marks you are sure of, and come back to the rest.

Manage your time and guess carefully: split your time in proportion to the marks each section carries and keep a few minutes at the end to check. This paper carries negative marking, so attempt a question only when you can rule out at least one or two options — a blind guess costs you marks. Confirm the exact marking scheme for your session on the official portal.

Frequently asked questions

What are the most important topics in Data Science & Artificial Intelligence?

Based on past papers, the most frequently asked topics include Probability Distributions: Bernoulli, Binomial, Poisson, Normal, Exponential, Expectation, Variance, Covariance and Conditional Probability, Linear Algebra: Rank, Determinant, Inverse, Eigenvalues and Eigenvectors. The full ranked list with how often each appears is in the "Most important topics" section above.

Where can I download Data Science & Artificial Intelligence previous-year question papers?

Official GATE previous-year question papers are available on the official GATE website. Open the Question Papers section for the direct link, plus the exam pattern and the topics that repeat most.

How can I prepare for Data Science & Artificial Intelligence faster?

Start with the highest-frequency topics, learn the exam pattern so you know how each section is marked, and practise with past papers. A subject-aware study tutor can quiz you on exactly these topics.

A Gyani AI tutor trained on the Data Science & Artificial Intelligence syllabus and past papers can quiz you on the most-asked topics and show you exactly what to revise.