The GATE DA (Data Science and Artificial Intelligence) paper provides candidates with an additional opportunity to pursue postgraduate programs such as M.E. and M.Tech., subject to the eligibility requirements and admission criteria prescribed by the respective institute.
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In this GATE Data Science and Artificial Intelligence Syllabus 2027, we have briefly explained the section-wise syllabus, eligibility criteria, exam pattern, marking scheme, exam tips and book recommendations to help students for the upcoming GATE 2027 exam.
GATE Data Science and Artificial Intelligence Syllabus PDF 2027
IIT Madras has released the official syllabus for the GATE DA 2027 exam, giving candidates a clear idea of the topics and concepts they need to prepare.
Download the latest GATE Data Science and Artificial Intelligence Syllabus PDF here "GATE Data Science and Artificial Intelligence Syllabus"
GATE Data Science and Artificial Intelligence (DA) Subjects
A variety of topics are covered in the GATE Data Science and Artificial Intelligence courses that are crucial for comprehending and succeeding in the discipline. The following are some of the main GATE data science and artificial intelligence topics covered in the curriculum:
- General Aptitude
- Probability and Statistics
- Linear Algebra
- Calculus and Optimization
- Programming, Data Structures, and Algorithms
- Database Management
- Data Warehousing
- Machine Learning
- Artificial Intelligence (AI)
GATE DA 2027 Syllabus (Core Subjects)
The syllabus for GATE Data Science and Artificial Intelligence in 2027 is categorized into 7 sections, covering topics such as Probability and Statistics, Linear Algebra, Calculus and Optimization, Machine Learning, and AI.
We can refer to the table below for a detailed breakdown of the GATE Data Science and Artificial Intelligence Syllabus 2027.
Section 1: Probability and Statistics
Covers basic and advanced probability concepts, descriptive statistics, random variables, probability distributions, statistical tests, and methods for analyzing data.
- 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
Section 2: Linear Algebra
Covers vector spaces, matrices and their properties, linear dependence and independence of vectors, systems of linear equations, matrix decompositions, eigenvalues and eigenvectors, projections, and quadratic forms.
- 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
Section 3: Calculus and Optimization
Covers functions of a single variable, limits, continuity, differentiability, Taylor series, maxima and minima, and methods for optimization involving a single variable.
- Functions of a Single Variable
- Limit, Continuity and Differentiability
- Taylor Series
- Maxima and Minima
- Optimization involving a single variable
Section 4: Programming, Data Structures and Algorithms
Covers programming in Python, basic data structures, searching and sorting algorithms, divide-and-conquer techniques, introduction to graph theory, and basic graph algorithms including traversals and shortest path.
- 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: merge sort, quicksort
- Introduction to graph theory.
- Basic graph algorithms: traversals and shortest path.
Section 5: Database Management and Warehousing
Covers database concepts including ER model, relational model, relational algebra, tuple calculus, SQL, integrity constraints, normal forms, file organization, indexing, data types, data transformation techniques, and data warehouse modeling.
- ER-model, relational model
- Relational algebra
- Tuple calculus
- SQL
- Integrity constraints
- Normal form
- File organization
- Indexing
- Data types
- Normalization
- Discretization
- Sampling
- Compression
- Data Warehousing Tutorial
Section 6: Machine Learning
Covers supervised and unsupervised learning techniques, regression and classification models, clustering algorithms, dimensionality reduction, neural networks, and methods to evaluate model performance.
- 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, multi-layer perceptron, feed-forward neural network
- Unsupervised Learning: clustering algorithms
- k-means/k-medoid
- Hierarchical clustering
- Top-down
- Bottom-up: single-linkage, multiple-linkage, dimensionality reduction, principal component analysis.
Section 7: Artificial Intelligence (AI)
Covers search techniques, logic-based reasoning, and methods for handling uncertainty, including exact and approximate inference in AI systems.
- Search: informed, uninformed, adversarial
- Logic: Propositional Logic, Predicate Logic
- Reasoning under Uncertainty Topics
- Conditional Independence Representation
- Exact Inference through Variable Elimination
- Approximate Inference through Sampling
Also Check: GATE 2027 Syllabus For CSE
GATE 2027 Eligibility Criteria for DA and AI
GATE Eligibility Criteria 2027: Here you will find details about the GATE 2027 Eligibility Criteria like the exam's age restriction, nationality, relaxation, requirements, etc. To appear in the Graduate Aptitude Test in Engineering and be deemed qualified for the test, candidates must fulfill the requirements of GATE 2027. We have Summarized the eligibility criteria for the GATE 2027 Data Science and Artificial Intelligence exam below:
Criteria | Eligibility |
|---|---|
| Nationality |
|
| Qualification for the GATE exam |
|
| GATE Age Limit | There is no age limit for GATE 2027. |
| GATE Attempt | There is no constraint on the number of GATE attempts. |
GATE 2027 Preparation Tips for Data Science and Artificial Intelligence
Here are some tips for cracking GATE 2027 with AI and DS:
- Before you start studying, familiarize yourself with the GATE 2027 exam pattern and syllabus.
- Create a disciplined study schedule and follow it religiously.
- Determine which GATE 2027 themes are more important and focus more on them.
- Choose relevant reference sources for your research.
- Acknowledge your strengths and concentrate on strengthening your weaknesses.
- To get accustomed to the format of the question paper, take practice exams.
GATE 2027 Data Science and Artificial Intelligence Exam Pattern
For the GATE 2027 data science and artificial intelligence exam, here is the detailed exam pattern:
GATE 2027 Artificial Intelligence and Data Science(DA) Exam Pattern | |
|---|---|
| Exam Duration | 3 hour |
| Mode of Examination | Online Computer Based Test(CBT) |
| Total Marks | 100 |
| Total Questions | 65 Questions Split in:
|
| Types of Question |
|
| Marks Distribution |
|
| Negative Marking | Applicable only to wrongly answered MCQ
There is no negative marking for MSQ and NAT questions. |
GATE 2027 Data Science and Artificial Intelligence Marking Scheme
Please find below the revised marking scheme for gate 2027 Data Science and Artificial Intelligence:
Gate 2027 Data Science and Artificial Intelligence Marking scheme | ||
|---|---|---|
| SECTIONS | Total Questions | Marking |
| General Aptitude | 10 | 5 question x 1 marks
5 question x 2 marks
Total marks=15 |
| Core Discipline | 55 | 25 question x 1 marks
30 question x 2 marks
Total Marks= 85 |
| Total Questions = 65 | Total Marks= 100 | |
How Do I Prepare for GATE 2027 Data Science and Artificial Intelligence?
GATE is one of the toughest competitive exams, which requires practice and preparation to crack it. There are various ways to prepare for a GATE exam. Some of them are listed below.
- Make Appropriate Plans: Before beginning to study for an exam, it is always a good idea to make appropriate plans. Make weekly, monthly, and annual plans out of the plan.
- Recognize Your Strengths and Weaknesses: Knowing your strengths and weaknesses is a necessary first step before delving deeply into anything.
- Learn Well: It is quite hard to pass the test without doing your homework. We are giving you the necessary resources to study the GATE syllabus. For GATE studies, you might consult the GATE CS Notes.
- Review Thoroughly: Whether it's for the GATE or any other exam, you must thoroughly review it to pass it. For revision, you can consult the GATE CSE brief notes and the Last Minute Notes.
- Practice Previous Year Questions: To pass any test, it is crucial to practice Previous Year Questions. We provide last year's questions for every subject, which will aid you in your preparation.
- Practice Mock Tests: Before the real exam, mock tests assist in the analysis of the exam papers. It is also incredibly helpful for any exam. To help students get ready for the test, we also offer free practice exams.
Book Recommendations to Prepare for Gate 2027 AI and DS Exam
To crack the tough exam at the gate, one should be fully prepared. You will need good Study materials for this, so we have curated the list of best books to prepare for the GATE 2027 artificial intelligence and data science exam:
Books Recommendations to Prepare for GATE DS and AI 2027 | |
|---|---|
| Book | Author |
| Introduction to Probability | Dimitri P. Bertsekas & John N. Tsitsiklis |
| Introduction to Linear Algebra | Gilbert Strang |
| Learning Python | Mark Lutz |
| Database Management Systems | Raghu Ramakrishnan and Johannes Gehrke |
| Machine Learning for Beginners | Chris Sebastian |
| Artificial Intelligence: A Modern Approach | Stuart Russell and Peter Norvig |
| Pattern Recognition and Machine Learning | Christopher M. Bishop |
| Deep Learning | Ian Goodfellow, Yoshua Bengio, and Aaron Courville |
| Elements of Statistical Learning | Trevor Hastie, Robert Tibshirani, and Jerome Friedman |
| Speech and Language Processing | Daniel Jurafsky and James H. Martin |
| Computer Vision: Algorithms and Applications | Richard Szeliski |
| Python Machine Learning | Sebastian Raschka and Vahid Mirjalili |
| Introduction to the Theory of Computation | Michael Sipser |
| Bayesian Reasoning and Machine Learning | David Barber |
| Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow | Aurélien Géron |
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