Free GATE Data Science & Artificial Intelligence (DA) Online Test Series 2027
Preparing for the Data Science & Artificial Intelligence (DA) paper requires a strong foundation in mathematics, programming, statistics, machine learning, and data analysis. The Free GATE Data Science & Artificial Intelligence (DA) Online Test Series 2027 is designed to help aspirants strengthen these concepts through chapter-wise practice tests, topic-wise quizzes, subject-wise assessments, and full-length mock examinations based on the latest GATE examination pattern.
GATE 2026 DA Section 1: Probability and Statistics Test Series Online
GATE 2026 DA Test Series for Probability and Statistics covers Counting, probability axioms, events, independence, conditional probability, Bayes Theorem, expectation, variance, mean, median, mode, standard deviation, correlation, covariance, random variables, distributions (uniform, Bernoulli, binomial, exponential, Poisson, normal, t, chi-squared), CDF, Conditional PDF, Central limit theorem, confidence intervals, and statistical tests (z-test, t-test, chi-squared test).
| Sub-Topic | Topic Test Link | Sub-Topic | Topic Test Link |
|---|---|---|---|
| Counting (permutation and combinations) | Start Test | Probability axioms | Start Test |
| Sample space | Start Test | Events | Start Test |
| Independent events | Start Test | Mutually exclusive events | Start Test |
| Marginal probability | Start Test | Conditional probability | Start Test |
| Joint probability | Start Test | Bayes Theorem | Start Test |
| Conditional expectation | Start Test | Conditional variance | Start Test |
| Mean | Start Test | Median | Start Test |
| Mode | Start Test | Standard deviation | Start Test |
| Correlation | Start Test | Covariance | Start Test |
| Random variables | Start Test | Discrete random variables | Start Test |
| Probability mass functions | Start Test | Uniform distribution (discrete) | Start Test |
| Bernoulli distribution | Start Test | Binomial distribution | Start Test |
| Continuous random variables | Start Test | Probability distribution function | Start Test |
| Uniform distribution (continuous) | Start Test | Exponential distribution | Start Test |
| Poisson distribution | Start Test | Normal distribution | Start Test |
| Standard normal distribution | Start Test | t-distribution | Start Test |
| Chi-squared distribution | Start Test | Cumulative distribution function | Start Test |
| Conditional PDF | Start Test | Central limit theorem | Start Test |
| Confidence interval | Start Test | z-test | Start Test |
| t-test | Start Test | Chi-squared test | Start Test |
GATE 2026 DA Section 2: Linear Algebra Test Series Online
GATE 2026 DA Test Series for Linear Algebra covers Vector space, subspaces, linear dependence/independence, matrices, projection matrix, orthogonal matrix, idempotent matrix, partition matrix, quadratic forms, systems of linear equations, Gaussian elimination, eigenvalues, eigenvectors, determinant, rank, nullity, projections, LU decomposition, and singular value decomposition.
| Sub-Topic | Topic Test Link | Sub-Topic | Topic Test Link |
|---|---|---|---|
| Vector space | Start Test | Subspaces | Start Test |
| Linear dependence of vectors | Start Test | Linear independence of vectors | Start Test |
| Matrices | Start Test | Projection matrix | Start Test |
| Orthogonal matrix | Start Test | Idempotent matrix | Start Test |
| Partition matrix | Start Test | Properties of matrices | Start Test |
| Quadratic forms | Start Test | Systems of linear equations | Start Test |
| Solutions of linear equations | Start Test | Gaussian elimination | Start Test |
| Eigenvalues | Start Test | Eigenvectors | Start Test |
| Determinant | Start Test | Rank | Start Test |
| Nullity | Start Test | Projections | Start Test |
| LU decomposition | Start Test | Singular value decomposition | Start Test |
GATE 2026 DA Section 3: Calculus and Optimization Test Series Online
GATE 2026 DA Test Series for Calculus and Optimization covers Functions of a single variable, limit, continuity and differentiability, Taylor series, maxima and minima, and optimization involving a single variable.
| Sub-Topic | Topic Test Link | Sub-Topic | Topic Test Link |
|---|---|---|---|
| Functions of a single variable | Start Test | Limit | Start Test |
| Continuity | Start Test | Differentiability | Start Test |
| Taylor series | Start Test | Maxima and minima | Start Test |
| Optimization involving a single variable | Start Test |
GATE 2026 DA Section 4: Programming, Data Structures and Algorithms Test Series Online
GATE 2026 DA Test Series for Programming, Data Structures and Algorithms covers Programming in Python, basic data structures (stacks, queues, linked lists, trees, hash tables), search algorithms (linear search, binary search), sorting algorithms (selection sort, bubble sort, insertion sort, mergesort, quicksort), introduction to graph theory, and basic graph algorithms (traversals and shortest path).
| Sub-Topic | Topic Test Link | Sub-Topic | Topic Test Link |
|---|---|---|---|
| Programming in Python | Start Test | Stacks | Start Test |
| Queues | Start Test | Linked lists | Start Test |
| Trees | Start Test | Hash tables | Start Test |
| Linear search | Start Test | Binary search | Start Test |
| Selection sort | Start Test | Bubble sort | Start Test |
| Insertion sort | Start Test | Mergesort | Start Test |
| Quicksort | Start Test | Introduction to graph theory | Start Test |
| Graph traversals | Start Test | Shortest path algorithms | Start Test |
GATE 2026 DA Section 5: Database Management and Warehousing Test Series Online
GATE 2026 DA Test Series for Database Management and Warehousing covers ER-model, relational model, relational algebra, tuple calculus, SQL, integrity constraints, normal forms, file organization, indexing, data types, data transformation (normalization, discretization, sampling, compression), data warehouse modelling (schema for multidimensional data models, concept hierarchies, measures: categorization and computations).
| Sub-Topic | Topic Test Link | Sub-Topic | Topic Test Link |
|---|---|---|---|
| ER-model | Start Test | Relational model | Start Test |
| Relational algebra | Start Test | Tuple calculus | Start Test |
| SQL | Start Test | Integrity constraints | Start Test |
| Normal forms | Start Test | File organization | Start Test |
| Indexing | Start Test | Data types | Start Test |
| Normalization (data transformation) | Start Test | Discretization | Start Test |
| Sampling | Start Test | Compression | Start Test |
| Schema for multidimensional data models | Start Test | Concept hierarchies | Start Test |
| Measures – Categorization | Start Test | Measures – Computations | Start Test |
GATE 2026 DA Section 6: Machine Learning Test Series Online
GATE 2026 DA Test Series for Machine Learning covers (i) Supervised Learning: regression (simple linear, multiple linear, ridge, logistic), classification (k-nearest neighbour, naive Bayes, LDA, SVM, decision trees), bias-variance trade-off, cross-validation (LOO, k-folds), multi-layer perceptron, feed-forward neural network; (ii) Unsupervised Learning: clustering (k-means/k-medoid, hierarchical clustering, single-linkage, multiple-linkage), dimensionality reduction (PCA).
| Sub-Topic | Topic Test Link | Sub-Topic | Topic Test Link |
|---|---|---|---|
| Supervised Learning – Introduction | Start Test | Simple linear regression | Start Test |
| Multiple linear regression | Start Test | Ridge regression | Start Test |
| Logistic regression | Start Test | k-nearest neighbour | Start Test |
| Naive Bayes classifier | Start Test | Linear discriminant analysis | Start Test |
| Support vector machine | Start Test | Decision trees | Start Test |
| Bias-variance trade-off | Start Test | Leave-one-out cross-validation | Start Test |
| k-folds cross-validation | Start Test | Multi-layer perceptron | Start Test |
| Feed-forward neural network | Start Test | Unsupervised Learning – Introduction | Start Test |
| k-means clustering | Start Test | k-medoid clustering | Start Test |
| Hierarchical clustering | Start Test | Top-down clustering | Start Test |
| Bottom-up clustering | Start Test | Single-linkage | Start Test |
| Multiple-linkage | Start Test | Dimensionality reduction | Start Test |
| Principal component analysis | Start Test |
GATE 2026 DA Section 7: AI Test Series Online
GATE 2026 DA Test Series for AI covers Search (informed, uninformed, adversarial), Logic (propositional, predicate), and Reasoning under uncertainty (conditional independence representation, exact inference through variable elimination, approximate inference through sampling).
| Sub-Topic | Topic Test Link | Sub-Topic | Topic Test Link |
|---|---|---|---|
| Informed search | Start Test | Uninformed search | Start Test |
| Adversarial search | Start Test | Propositional logic | Start Test |
| Predicate logic | Start Test | Conditional independence representation | Start Test |
| Exact inference through variable elimination | Start Test | Approximate inference through sampling | Start Test |
Regular online mock tests help candidates improve analytical thinking, programming skills, and numerical accuracy while becoming familiar with the computer-based examination format. By reviewing performance after every test, aspirants can identify weak topics, revise important concepts, and build the confidence needed to perform well in GATE 2027.
Free GATE Data Science & Artificial Intelligence (DA) Test Series 2027 Overview
| Particular | Details |
|---|---|
| Examination | GATE 2027 |
| Paper Code | DA |
| Subject | Data Science & Artificial Intelligence |
| Test Mode | Online |
| Test Types | Chapter-wise, Topic-wise, Subject-wise & Full-Length Mock Tests |
| Cost | Free |
| Question Pattern | Based on the Latest GATE Examination Pattern |
| Suitable For | GATE DA Aspirants |
Why Practice the GATE DA Online Test Series?
The Data Science & Artificial Intelligence paper includes conceptual, analytical, programming, and numerical questions that require both theoretical understanding and practical problem-solving skills. Solving online mock tests regularly helps candidates improve speed, accuracy, and logical thinking while evaluating their preparation level before the examination.
Key benefits include:
- Free online mock tests
- Chapter-wise practice
- Topic-wise quizzes
- Subject-wise mock tests
- Full-length online examinations
- Better speed and time management
- Detailed performance analysis
- Complete revision support
Features of the Free GATE DA Mock Tests
The mock tests are designed to provide an examination-like experience while covering the complete Data Science & Artificial Intelligence syllabus. Regular practice enables candidates to strengthen conceptual understanding, improve coding and analytical skills, and monitor their preparation throughout the learning process.
The test series offers:
- Chapter-wise practice tests
- Topic-wise question banks
- Subject-wise mock examinations
- Full-length online tests
- Instant score and performance analysis
- Latest GATE examination pattern
- Unlimited online practice
- Mobile and desktop compatibility
How to Use the GATE DA Test Series for Better Preparation
Begin your preparation by studying one Data Science & Artificial Intelligence subject at a time, such as Engineering Mathematics, Probability and Statistics, Linear Algebra, Programming, Data Structures, Algorithms, Database Management, Machine Learning, Artificial Intelligence, Data Analytics, and Optimization. After completing each topic, attempt the corresponding chapter-wise mock tests to evaluate your conceptual understanding and identify mistakes.
Once several subjects are covered, solve subject-wise mock tests to strengthen your preparation across broader sections of the syllabus. After completing the entire DA syllabus, regularly attempt full-length mock examinations under timed conditions to simulate the actual GATE examination. Carefully analyze every incorrect answer, revise important algorithms, mathematical concepts, and programming techniques, and continue practicing consistently to improve your speed, accuracy, confidence, and overall GATE DA score.
Conclusion
Success in the Data Science & Artificial Intelligence paper depends on conceptual clarity, programming proficiency, analytical thinking, and regular mock test practice. Solving free online mock tests throughout your preparation will strengthen your problem-solving abilities, improve examination readiness, and help you maximize your score in GATE 2027.