GATE DA Syllabus 2027: Complete Topic‑Wise Guide for Data Science and Artificial Intelligence

This is your definitive, topic‑by‑topic breakdown of the GATE DA Syllabus 2027, covering all 8 sections, the exam pattern, marking scheme, subject‑wise weightage, and actionable preparation strategies trusted by serious GATE aspirants.

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What Is GATE DA? A Quick Overview

The GATE Data Science and Artificial Intelligence (DA) paper was introduced in 2024, making it one of the newest additions to the GATE lineup. Conducted jointly by IITs and IISc, it was created to meet India's rapidly growing demand for skilled professionals in machine learning, data analytics, and AI systems.

A strong GATE DA score unlocks admission to M.Tech, M.S., and Ph.D. programs at IITs, IISc, NITs, and IIITs, eligibility for data‑driven PSU recruitment, and research opportunities at premier Indian and global institutions.

With AI reshaping every sector, a GATE DA qualification is fast becoming one of the most career‑defining credentials in Indian engineering.

GATE DA 2027 Exam Pattern at a Glance

Before diving into the syllabus, you need to understand the structure of the exam you are preparing for.

The GATE DA 2027 exam is a 3‑hour computer‑based test (CBT) with 65 questions carrying a total of 100 marks. The paper is divided into two sections. The General Aptitude section carries 15 marks across 10 questions, and the Core DA Subject section carries 85 marks across 55 questions.

The question paper includes three types of questions: Multiple Choice Questions (MCQs) with one correct option, Multiple Select Questions (MSQs) where one or more options may be correct, and Numerical Answer Type (NAT) questions where the answer is typed as a number directly.

Negative marking applies only to MCQs. For a wrong answer to a 1‑mark MCQ, one‑third of a mark is deducted. For a wrong answer to a 2‑mark MCQ, two‑thirds of a mark is deducted. MSQs and NAT questions carry no negative marking whatsoever.

This is an important strategic point. Since MSQs and NAT questions carry zero penalty, attempt every single one even if you are not completely certain. Use elimination and estimation to maximize your score on these question types.

GATE DA 2027 Important Dates

The official notification for GATE DA 2027 is expected to release on July 1, 2026. The application form will open from August 1, 2026 with the last date being September 30, 2026. The application correction window will open around October 15, 2026. Admit cards are expected to release on January 10, 2027. The GATE DA exam is scheduled for February 8, 2027. The answer key will be released around February 20, 2027, results declared on March 20, 2027, and score cards available from March 25, 2027.

GATE DA Syllabus 2027: All 8 Sections at a Glance

The GATE DA Syllabus 2027 is divided into 8 sections spanning 172 individual topics. Here is a quick overview of all subjects before we go section by section:

• General Aptitude
• Probability and Statistics
• Linear Algebra
• Calculus and Optimization
• Programming, Data Structures and Algorithms
• Database Management and Warehousing
• Machine Learning
• Artificial Intelligence

Section 1: General Aptitude (15 Marks)

The General Aptitude section is common across all GATE papers and carries 15 percent of the total marks. It tests four domains:

Verbal Aptitude

• Basic English grammar: tenses, articles, adjectives, prepositions and conjunctions
• Vocabulary and word usage
• Reading comprehension passages
• Sentence completion exercises

Quantitative Aptitude

• Data interpretation from bar graphs, pie charts and tables
• Arithmetic covering ratio, proportion and percentages
• Elementary geometry and mensuration
• Basic statistics

Analytical Aptitude

• Logical reasoning and deduction
• Pattern recognition
• Argument analysis

Spatial Aptitude

• Shape transformations and mirroring
• Visualization of 2D and 3D figures
• Spatial pattern recognition

Dedicate 1 to 2 focused weeks to GA preparation. Solve 15 to 20 GA questions daily from previous GATE papers. The pattern rarely deviates and a high GA score is very achievable with consistent practice.

Section 2: Probability and Statistics (29 Topics)

This section is the mathematical backbone of the entire GATE DA paper. Questions appear both directly and embedded within Machine Learning problems, making it doubly important to master.

Counting and Combinatorics

• Permutations and combinations
• General counting principles

Probability Foundations

• Probability axioms and sample spaces
• Independent events and mutually exclusive events
• Conditional probability and Bayes theorem
• Marginal and joint probability distributions

Random Variables and Distributions

• Discrete random variables and probability mass functions: Uniform, Bernoulli, Binomial
• Continuous random variables and probability density functions: Uniform, Exponential, Poisson, Normal, Standard Normal, t‑Distribution, Chi‑squared
• Cumulative distribution functions and conditional PDFs
• Expectation, variance and higher moments
• Conditional expectation

Statistical Inference

• Point estimation and interval estimation
• Maximum Likelihood Estimation (MLE)
• Maximum A Posteriori (MAP) estimation
• Hypothesis testing: Z‑test, t‑test, chi‑squared test
• p‑values and confidence intervals

Convergence Theorems

• Central Limit Theorem
• Law of Large Numbers

Correlation and Regression

• Covariance and correlation
• Simple and multiple linear regression
• Least squares estimation

This section requires genuine conceptual mastery rather than formula memorization. Work through derivations. Understand why Bayes theorem works, not just how to apply it. This understanding pays dividends throughout the Machine Learning section as well.

Section 3: Linear Algebra (16 Topics)

Linear Algebra is the language in which machine learning is written. Mastery here directly accelerates your ML and AI preparation.

Vector Spaces and Subspaces

• Vector spaces, subspaces, basis and dimension
• Linear independence and dependence of vectors
• Span and orthogonality

Matrix Operations

• Types of matrices: symmetric, skew‑symmetric, orthogonal, diagonal
• Matrix rank and nullity
• Determinants and their properties
• Matrix inverses and trace

Systems of Linear Equations

• Existence and uniqueness of solutions
• Gaussian elimination
• Homogeneous and non‑homogeneous systems

Matrix Decompositions

• Eigenvalues and eigenvectors
• Characteristic polynomial
• Diagonalization of matrices
• Singular Value Decomposition (SVD)
• LU decomposition
• QR decomposition

Projections and Quadratic Forms

• Orthogonal projections
• Positive definite and positive semi‑definite matrices
• Classification of quadratic forms

Do not memorize formulas in isolation. Build geometric intuition for each concept. SVD and eigendecomposition are directly tested in the context of PCA questions, so spend extra time connecting these ideas across sections.

Section 4: Calculus and Optimization (9 Topics)

Optimization is the engine behind every machine learning algorithm. This section tests the mathematical tools used to minimize loss functions and tune model parameters.

Single‑Variable Calculus

• Limits and continuity
• Differentiability
• Taylor series expansion
• Maxima and minima of functions of one variable

Multivariable Calculus

• Partial derivatives
• Gradient and Hessian matrix
• Conditions for local and global optima
• Lagrange multipliers for constrained optimization

Optimization Methods

• Gradient Descent and its variants
• Convex functions and convex optimization
• Unconstrained and constrained optimization problems

Focus particularly on gradient descent because it appears across multiple GATE DA contexts, from neural network training to logistic regression optimization. Understand the geometry of convex functions rather than treating optimization as a purely mechanical exercise.

Section 5: Programming, Data Structures and Algorithms (27 Topics)

This section bridges mathematical theory with practical computing. It demands both coding ability and algorithmic thinking.

Python Programming

• Basic syntax, data types and control structures
• Functions, modules and libraries (NumPy, Pandas)
• Object‑oriented programming basics
• File I/O and exception handling

Data Structures

• Arrays, linked lists, stacks and queues
• Binary trees, binary search trees and AVL trees
• Heaps and priority queues
• Directed and undirected graphs, weighted and unweighted graphs
• Hash tables

Algorithms

• Linear and binary search
• Sorting: quicksort, mergesort, heapsort, insertion sort
• Graph algorithms: BFS, DFS, Dijkstra's, Bellman‑Ford, Prim's, Kruskal's
• Dynamic programming fundamentals
• Greedy algorithms

Algorithm Analysis

• Time and space complexity using Big O notation
• Recurrence relations
• Best, worst and average‑case analysis

Practice Python implementations of every data structure and algorithm listed above. GATE DA tests the correctness of code logic in Python, not just abstract algorithm knowledge. Solving easy to medium‑level programming problems on practice platforms is highly relevant preparation.

Section 6: Database Management and Warehousing (22 Topics)

DBMS is one of the most reliable scoring sections in GATE DA. Concepts are well‑defined, questions follow predictable patterns, and with focused effort this section is learnable in a short amount of time.

Relational Model

• ER models: entities, attributes, relationships and cardinality
• Relational model fundamentals: tuples, attributes, domains and keys
• ER to relational mapping

SQL and Query Processing

• DDL, DML and DCL commands
• All join types: inner, outer, cross, self
• Nested queries and subqueries
• Aggregate functions and GROUP BY clauses
• Views, indexes and triggers

Normalization

• Functional dependencies
• First, Second and Third Normal Forms
• Boyce‑Codd Normal Form (BCNF)
• Decomposition with lossless joins

Transactions and Concurrency

• ACID properties
• Transaction management
• Concurrency control: locking protocols and two‑phase locking
• Deadlock detection and prevention

Data Warehousing

• OLTP versus OLAP systems
• Star schema and snowflake schema design
• Data cubes and multidimensional data models
• ETL processes: Extract, Transform, Load

GATE DA DBMS questions often combine SQL query writing with normalization in the same problem. Solve gate‑level SQL problems daily. Multi‑step normalization scenarios are tested frequently.

Section 7: Machine Learning (38 Topics)

Machine Learning is the core of the GATE DA paper and carries the highest individual weightage. Allocate the most preparation time here.

Supervised Learning: Regression

• Simple linear regression and multiple linear regression
• Ridge regression and Lasso regression
• Polynomial regression
• Loss functions and cost functions

Supervised Learning: Classification

• Logistic regression
• k‑Nearest Neighbors (k‑NN)
• Naive Bayes classifier
• Linear Discriminant Analysis (LDA)
• Support Vector Machines (SVM): hard margin, soft margin, kernel trick
• Decision Trees: entropy, information gain, Gini index, pruning

Neural Networks

• Multi‑layer perceptron (MLP)
• Feed‑forward neural networks
• Backpropagation algorithm
• Activation functions: sigmoid, ReLU, tanh, softmax

Model Evaluation and Selection

• Bias‑variance trade‑off
• Overfitting and underfitting
• Train‑validation‑test splits
• Cross‑validation: leave‑one‑out (LOO) and k‑fold
• Confusion matrix metrics: precision, recall, F1‑score, accuracy
• ROC curve and AUC
• Regularization techniques

Unsupervised Learning: Clustering

• k‑Means clustering
• k‑Medoid clustering
• Hierarchical clustering: agglomerative (bottom‑up) and divisive (top‑down)
• Linkage criteria: single‑linkage, complete‑linkage, average‑linkage

Unsupervised Learning: Dimensionality Reduction

• Principal Component Analysis (PCA)
• Singular Value Decomposition in the ML context
• Feature selection techniques

Do not just learn what each algorithm does. Learn when to use which algorithm. GATE DA frequently presents scenario‑based questions asking which classifier or approach is most appropriate for a given dataset property. Implement algorithms in NumPy rather than only scikit‑learn so you understand the internals, not just the interface.

Section 8: Artificial Intelligence (18 Topics)

Artificial Intelligence rounds out the GATE DA syllabus with three core areas: search algorithms, knowledge representation, and reasoning under uncertainty.

Search Algorithms

• Uninformed search: BFS, DFS, Iterative Deepening
• Informed search: Best‑First Search, A* algorithm
• Heuristic functions and admissibility
• Local search: Hill Climbing, Simulated Annealing
• Adversarial search: Minimax algorithm, Alpha‑Beta pruning

Logic and Knowledge Representation

• Propositional logic: syntax, semantics, inference rules
• First‑Order Logic (FOL): predicates, quantifiers, unification
• Forward chaining and backward chaining
• Resolution and theorem proving

Reasoning Under Uncertainty

• Bayesian networks: structure, inference, conditional independence
• Exact and approximate inference
• Markov Decision Processes (MDPs): states, actions, rewards, policies
• Value iteration and policy iteration

Bayesian Networks and MDPs are conceptually challenging but high‑yield topics. Spend time understanding the graphical structure of Bayesian networks before attempting inference problems. The A* algorithm and heuristic design appear frequently in MCQ‑style questions and are worth drilling thoroughly.

Subject‑Wise Weightage Guide for GATE DA 2027

Based on patterns observed in recent GATE DA papers, here is the approximate marks contribution from each section:

• General Aptitude: 15 marks
• Machine Learning: 20 to 25 marks
• Probability and Statistics: 12 to 15 marks
• Programming, Data Structures and Algorithms: 10 to 12 marks
• Artificial Intelligence: 10 to 12 marks
• Linear Algebra: 8 to 10 marks
• Database Management and Warehousing: 8 to 10 marks
• Calculus and Optimization: 5 to 8 marks

Machine Learning combined with Probability and Statistics can together account for 35 to 40 marks out of 100. These two sections deserve the majority of your preparation time and the deepest level of conceptual understanding.

Track Your GATE DA 2027 Syllabus Progress

One of the biggest mistakes GATE aspirants make is studying without tracking what has actually been covered. Without a tracker, you end up revising topics you already know while leaving critical gaps untouched for weeks at a time.

The Aspirant Mitraa GATE DA Syllabus Tracker at https://www.aspirantmitraa.com/syllabus/gate‑da‑data‑science‑and‑artificial‑intelligence solves this problem directly. It gives you a topic‑by‑topic interactive checklist across all 172 GATE DA topics, visual progress tracking so you always know where you stand, section‑wise completion percentages, and all important exam dates and notification alerts in one place.

It is free, built specifically for GATE DA aspirants, and is the smartest way to stay fully accountable throughout your GATE DA 2027 preparation journey.

Month‑by‑Month GATE DA 2027 Preparation Strategy

Months 1 and 2: Mathematical Foundations

Cover Probability and Statistics, Linear Algebra, and Calculus and Optimization. These are foundational. A weak mathematical base will cap your ML and AI scores no matter how hard you work on those sections later.

Months 3 and 4: Programming and Databases

Work through Python programming, Data Structures, Algorithms, and DBMS. Solve 10 to 15 coding problems per week. Complete SQL query practice comprehensively.

Months 5 and 6: Machine Learning Deep Dive

This is your most critical preparation phase. Cover all ML algorithms, implement each one in Python, and understand evaluation metrics deeply. Solve all available GATE DA ML questions exhaustively.

Month 7: Artificial Intelligence

Cover search algorithms, propositional and first‑order logic, Bayesian networks, and MDPs. The AI section is relatively self‑contained and learnable in a few focused weeks.

Months 8 and 9: Revision and Mock Tests

Take full‑length mock tests every week. Analyze every wrong answer systematically. Revisit weak topics. Polish your General Aptitude score.

Final Two Weeks: Light Revision Only

Revise only formulas, key concepts, and high‑error topics. Avoid picking up any new material. Focus entirely on confidence, accuracy, and rest.

Recommended Books for GATE DA 2027 Preparation

• Probability and Statistics: "Introduction to Probability" by Blitzstein and Hwang
• Linear Algebra: "Linear Algebra and Its Applications" by Gilbert Strang
• Machine Learning (advanced): "Pattern Recognition and Machine Learning" by Christopher Bishop
• Machine Learning (accessible): "Hands‑On Machine Learning" by Aurelien Geron
• Artificial Intelligence: "Artificial Intelligence: A Modern Approach" by Russell and Norvig
• Database Management: "Database System Concepts" by Silberschatz, Korth and Sudarshan
• Programming and DSA: "Python for Data Analysis" by Wes McKinney

Eligibility Criteria for GATE DA 2027

To appear in the GATE 2027 DA paper, you must hold a Bachelor's degree or be in the final year of a Bachelor's program in Engineering, Technology, Science, Architecture, Commerce, Arts, or Humanities recognized by UGC or AICTE. Students in the third year or higher of their undergraduate degree are eligible to apply. There is no upper age limit for GATE. Indian nationals as well as foreign nationals under specific provisions may apply.

Unlike most engineering GATE papers, GATE DA is open to graduates from a wide range of disciplines including non‑CS and non‑engineering backgrounds, making it one of the most inclusive papers in the entire GATE exam.

Frequently Asked Questions

What is the GATE DA Syllabus 2027?

The GATE DA Syllabus 2027 covers 8 sections: General Aptitude, Probability and Statistics, Linear Algebra, Calculus and Optimization, Programming, Data Structures and Algorithms, Database Management and Warehousing, Machine Learning, and Artificial Intelligence. The syllabus spans 172 topics in total.

Has the GATE DA Syllabus changed for 2027?

The core structure of the GATE DA paper has remained consistent across years. Candidates should always verify any updates from the official GATE 2027 portal once it is released by IIT Madras.

Which section carries the highest marks in GATE DA 2027?

Machine Learning carries the highest individual weightage at approximately 20 to 25 marks, making it the most critical section to prepare thoroughly.

Is there negative marking in GATE DA 2027?

Yes, but only for MCQs. A wrong answer to a 1‑mark MCQ results in a deduction of one‑third mark. A wrong answer to a 2‑mark MCQ results in a deduction of two‑thirds mark. MSQ and NAT questions carry no negative marking.

How many months of preparation is enough for GATE DA 2027?

With consistent effort and a structured plan, 6 to 9 months is generally sufficient. Aspirants with a strong mathematics background may be comfortable with 6 months, while others should aim for 9 to 12 months.

Can non‑CS students appear for GATE DA 2027?

Yes. GATE DA is open to graduates from Engineering, Science, Technology, Commerce, and Arts backgrounds, making it one of the most inclusive GATE papers available.

What is a good score in GATE DA?

A normalized score above 600 out of 1000 is generally considered competitive for top IITs. A score above 700 significantly improves chances of admission to premier M.Tech programs.

Where can I track all 172 GATE DA 2027 syllabus topics interactively?

You can track your complete GATE DA 2027 preparation at the Aspirant Mitraa GATE DA Syllabus Tracker: https://www.aspirantmitraa.com/syllabus/gate‑da‑data‑science‑and‑artificial‑intelligence. It covers every topic across all 8 sections with progress tracking, completion percentages, and important date reminders built in.