GATE DA Syllabus 2026 PDF - Check Data Science and AI Important Topics
Updated on March 24th, 2025 - 07:15 AM by Samridhi Mishra
GATE Data Science and AI Syllabus 2026 - IIT Guwahati will release the GATE 2026 Data Science and Artificial Intelligence
syllabus pdf on its official website. GATE exam aspirants can check the GATE DA syllabus on this page based on the previous
year's notification. The GATE 2026 DA and AI syllabus comprises all the topics that will be tested in the GATE exam. The
syllabus comprises topics such as Probability, Statistics, Linear Algebra, Algorithms, Programming, Data Structures,
Database Management Systems, Data Warehousing, Machine Learning, and Artificial Intelligence. The authority will also
provide the GATE 2026 exam pattern online. Candidates must refer to the GATE syllabus to ensure that they study all the
relevant topics. IIT Guwahati will conduct the GATE 2026 exam in online mode in the month of February 2026.
Understanding the syllabus of DA for GATE 2026 is the first and foremost step while preparing for the GATE exam. The
previous year GATE DA syllabus is mentioned in this article. Candidates can also check the GATE Data Science and AI
question paper for more understanding of the exam pattern and frequently asked topics in the exam.
Download the previous year's GATE Data Science and AI Syllabus
GATE DA Syllabus 2026 for General Aptitude
IIT Guwahati will release the GATE 2026 Data Science and AI Syllabus on its official website. Candidates will be able to check
the updated GATE General Aptitude syllabus 2026 from the table below. However, until the GATE 2026 DA & AI syllabus pdf
is released candidates can check the previous year's syllabus below.
GATE 2026 syllabus for General Aptitude
Chapters Topics
Basic English grammar: tenses, articles, adjectives,
prepositions, conjunctions, verb-noun agreement, and
other parts of speech
GATE GA syllabus for Verbal Aptitude
Basic vocabulary: words, idioms, and phrases in
context, reading and comprehension, Narrative
sequencing.
Data interpretation: data graphs (bar graphs, pie
charts, and other graphs representing data), 2- and 3-
dimensional plots, maps, and tables
Numerical computation and estimation: ratios,
GATE GA syllabus for Quantitative Aptitude
percentages, powers, exponents and logarithms,
permutations and combinations, and series
Mensuration and geometry Elementary statistics and
probability
Logic: deduction and induction, Analogy, Numerical
GATE GA syllabus for Analytical Aptitude
relations and reasoning
Transformation of shapes: translation, rotation, scaling,
GATE GA syllabus for Spatial Aptitude mirroring, assembling, and grouping paper folding,
cutting, and patterns in 2 and 3 dimensions.
GATE DS and AI Syllabus 2026
IIT Guwahati will upload the GATE Syllabus for Artificial Intelligence and Data Science online as a pdf. The syllabus topics
such as Probability, Statistics, Linear Algebra, Algorithms, Programming, Data Structures, Database Management Systems,
Data Warehousing, Machine Learning, and Artificial Intelligence. For detailed information, refer to the GATE syllabus for Data
Science and AI below.
GATE 2026 DA Syllabus
Subject Topics
GATE DA syllabus Counting (permutation and combinations), probability axioms, Sample space, Events, independent
for Probability and events, mutually exclusive events, marginal, conditional and joint probability, Bayes Theorem,
Statistics 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,
Subject Topics
cumulative distribution function, Conditional PDF, Central limit theorem, confidence interval, z-test, t-
test, chi-squared test.
Vector space, subspaces, linear dependence and independence of vectors, matrices, projection matrix,
orthogonal matrix, idempotent matrix, partition matrix and their properties, quadratic forms, systems
GATE DA syllabus of linear equations and solutions
for Linear Algebra
Gaussian elimination, eigenvalues and eigenvectors, determinant, rank, nullity, projections, LU
decomposition, singular value decomposition
GATE DA syllabus
Functions of a single variable, limit, continuity and differentiability, Taylor series, maxima and minima,
for Calculus and
optimization involving a single variable
optimization
ER-model, relational model: relational algebra, tuple calculus, SQL, integrity constraints, normal
GATE DA syllabus
form, file organisation, indexing, data types, data transformation such as normalisation,
for Database
discretization, sampling, compression
Management and
Data warehouse modelling: schema for multidimensional data models, concept hierarchies,
Warehousing
measures: categorization and computations.
Programming in Python
GATE DA syllabus Basic data structures: stacks, queues, linked lists, trees, hash tables
for Programming, Search algorithms: linear search and binary search
Data Structures and Basic sorting algorithms: selection sort, bubble sort and insertion sort
Algorithms Divide and conquer: mergesort, quicksort; introduction to graph theory
Basic graph algorithms: traversals and shortest path
(i) 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, mulo-layer
GATE DA syllabus
perceptron, feed-forward neural network;
for Machine
Learning
(ii) Unsupervised Learning: clustering algorithms, k-means/k-medoid, hierarchical clustering, top-
down, bottom-up: single-linkage, multiple linkages, dimensionality reduction, principal component
analysis.
Subject Topics
Search: informed, uninformed, adversarial; logic, propositional, predicate; reasoning under
GATE DA syllabus
uncertainty topics - conditional independence representation, exact inference through variable
for AI
elimination, and approximate inference through sampling.
Related links:
GATE Mock tests
GATE Preparation Tips
GATE Preparation Timetable
GATE DA and AI Topic Wise Weightage
Topic Name Number of Questions Total Marks
General Aptitude 10 15
Probability and Statistics 10 16
Linear Algebra 6 10
Calculus and Optimization 5 8
Programming, Data Structures, and Algorithms 13 21
Database Management and Warehousing 6 8
Machine Learning 8 11
Artificial Intelligence (AI) 7 11
Total 65 100
GATE Data Science and AI Marking Scheme 2026
The GATE exam consists of two sections, General Aptitude and subject-oriented. Check the marking scheme of the GATE
2026 DA exam here. Candidates must note that the For a wrong answer chosen in a MCQ, there will be a negative marking.
For a 1-mark question, 1/3 mark will be deducted and for the 2-mark question, 2/3 mark will be deducted.
GATE 2026 Data Science and AI Marking Scheme
Subject Marks Allotted
General Aptitude (GA) 15
Subject marks 85
Total 100
Related links:
GATE Physics Syllabus GATE Syllabus for Instrumentation Engineering
GATE ECE Syllabus GATE CSE Syllabus
GATE Data Science and AI Books 2026
Students can refer to the following table for the list of books for GATE preparation. A useful tip to aspirants is to make GATE
Data Science and AI notes in a short and precise manner for better revision. Moreover, aspirants must check their level of
preparation regularly with GATE Data Science and AI sample papers and mock tests.
GATE 2026 Data Science and Artificial Intelligence Books
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 Question (FAQs) - GATE DA Syllabus 2026 PDF - Check Data Science
and AI Important Topics
Question: What is the syllabus for GATE data science 2026?
Answer:
The GATE Data Science and AI syllabus 2026 includes such as Probability, statistics, linear algebra, algorithms, Programming, Data
Structures, database management systems, warehousing, machine learning, and Artificial intelligence.
Question: Is 6 months enough for GATE 2026 preparation?
Answer:
It depends on the individual calibre of the aspirant. But yes, with proper planning, and good study material, 6 months is enough
for GATE 2026 preparation. Provided that the students must have previous knowledge of the basics.
Question: Is there negative marking in GATE 2026 Data Science and AI paper?
Answer:
Yes, there is a negative marking in the GATE Data Science and AI 2026.
Question: What type of questions are asked in GATE 2026 DA?
Answer:
GATE DA 2026 will have three types of questions, multiple-choice (MCQ) type, multiple-select (MSQ) type, and
numerical answer type (NAT).
Question: What is a good score in GATE 2026?
Answer:
A score of 90+ is considered the best for GATE 2026, since it increases the chances of admission drastically.
Question: What is the exam pattern for GATE Data Science and Artificial Intelligence 2026?
Answer:
As per the exam pattern for GATE Data Science and Artificial Intelligence, the exam will have 15 marks worth GA section and the
remaining 85 for the core subject. The question paper will comprise a mix of MCQs, MSQs and NATs.
