GATE DA 2026 TEST SERIES
GATE Data Science & Artificial Intelligence 2026 Test Series
Welcome to the most comprehensive and strategic GATE Data Science & Artificial Intelligence 2026 Test Series offered by Mindspan Education …
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MindSpan Education
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This Test Series Includes:
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1+ comprehensive tests
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Detailed performance analytics
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Extended access period
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Access on mobile and desktop
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Detailed solutions
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Bookmark questions
About This Test Series
GATE Data Science & Artificial Intelligence 2026 Test Series
Welcome to the most comprehensive and strategic GATE Data Science & Artificial Intelligence 2026 Test Series offered by Mindspan Education. Designed to simulate the real exam environment and boost your preparation through step-by-step testing, our test series ensures you cover every topic, every concept, and every type of question that GATE DA demands.
Key Features of Mindspan's GATE DA Test Series
- 1400+ High-Quality Questions: Curated to match GATE standards with deep concept coverage
- 60+ Full-Length and Topicwise Tests: Well-planned across topicwise, subjectwise, and mock formats
- 8 Full Mock Tests: Simulate the actual GATE DA exam with exact 3-hour interface and difficulty level
- Detailed Solutions & Explanations: Every question comes with step-by-step reasoning
- Exact GATE Exam Interface: Familiarize yourself with the official GATE exam pattern and UI
- Bookmark Questions: Save important or tricky questions to revisit anytime
- Practice Mode: Re-attempt questions in practice mode after submission for stronger retention
Complete Test Structure
1. Topicwise Tests (Tests 1-23)
Master individual topics with focused 15-question tests covering all GATE DA subjects:
| Test No. | Subject & Topic | Questions | Marks | Time |
|---|---|---|---|---|
| 1 | Probability & Statistics - Counting, permutations, combinations, probability axioms, sample space, events, Bayes Theorem | 15 | 20 | 35 mins |
| 2 | Probability & Statistics - Discrete random variables, PMFs, Bernoulli, uniform, binomial, marginal, joint, conditional probability, conditional expectation and variance, standard deviation | 15 | 20 | 35 mins |
| 3 | Probability & Statistics - Continuous random variables, PDFs, CDFs, uniform exponential, Poisson, normal, t, chi-squared, conditional PDF | 15 | 20 | 35 mins |
| 4 | Probability & Statistics - Mean, median, mode, covariance, correlation, CLT, confidence intervals, z-test, t-test, chi-squared test | 15 | 20 | 35 mins |
| 5 | Linear Algebra - Systems of linear equations, Gaussian elimination, LU decomposition, matrices, linear dependence and independence of vectors | 15 | 20 | 35 mins |
| 6 | Linear Algebra - Vector space, subspace, eigenvalues, eigenvectors, determinant, rank, nullity | 15 | 20 | 35 mins |
| 7 | Linear Algebra - Projections, projection matrix, orthogonal matrix, idempotent and partition matrices | 15 | 20 | 35 mins |
| 8 | Linear Algebra - Quadratic forms, SVD | 15 | 20 | 35 mins |
| 9 | Calculus & Optimization - Functions of single variable, limits, continuity, differentiability | 15 | 20 | 35 mins |
| 10 | Calculus & Optimization - Taylor series, maxima and minima, optimization involving a single variable | 15 | 20 | 35 mins |
| 11 | Python & DSA - Programming in Python | 15 | 20 | 35 mins |
| 12 | Python & DSA - Stacks, queues, linked lists, trees, hash tables | 15 | 20 | 35 mins |
| 13 | Python & DSA - Search algorithms: linear search and binary search, basic sorting algorithms: selection sort, bubble sort and insertion sort; divide and conquer: mergesort, quicksort | 15 | 20 | 35 mins |
| 14 | Python & DSA - Introduction to graph theory; basic graph algorithms: traversals and shortest | 15 | 20 | 35 mins |
| 15 | DBMS & Warehousing - ER model, relational algebra, SQL, tuple calculus | 15 | 20 | 35 mins |
| 16 | DBMS & Warehousing - Integrity constraints, normal form, file organization, indexing, data types, data transformation such as normalization, discretization, sampling, compression | 15 | 20 | 35 mins |
| 17 | DBMS & Warehousing - Data warehouse modelling: schema for multidimensional data models, concept hierarchies, measures: categorization and computations | 15 | 20 | 35 mins |
| 18 | Machine Learning - 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 | 15 | 20 | 35 mins |
| 19 | Machine Learning - 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 | 15 | 20 | 35 mins |
| 20 | Machine Learning - Clustering algorithms, k-means/k-medoid, hierarchical clustering, top-down, bottom-up: single-linkage, multiple-linkage, dimensionality reduction, principal component analysis | 15 | 20 | 35 mins |
| 21 | Artificial Intelligence - Informed/uninformed/adversarial search | 15 | 20 | 35 mins |
| 22 | Artificial Intelligence - Logic, propositional, predicate | 15 | 20 | 35 mins |
| 23 | Artificial Intelligence - Reasoning under uncertainty topics — conditional independence representation, exact inference through variable elimination, and approximate inference through sampling | 15 | 20 | 35 mins |
2. Subject-wise Tests (Tests 24-52)
Comprehensive subject-level practice with multiple tests per subject:
| Test No. | Subject | Questions | Marks | Time |
|---|---|---|---|---|
| 24-25 | Aptitude (2 Tests) - Numerical reasoning, data interpretation, verbal ability, logical reasoning | 20 | 30 | 55 mins |
| 26-29 | Linear Algebra (4 Tests) - Complete subject coverage | 20 | 30 | 55 mins |
| 30-33 | Python & DSA (4 Tests) - Complete subject coverage | 20 | 30 | 55 mins |
| 34-37 | Probability & Statistics (4 Tests) - Complete subject coverage | 20 | 30 | 55 mins |
| 38-40 | Calculus & Optimization (3 Tests) - Complete subject coverage | 20 | 30 | 55 mins |
| 41-44 | DBMS & Warehousing (4 Tests) - Complete subject coverage | 20 | 30 | 55 mins |
| 45-48 | Machine Learning (4 Tests) - Complete subject coverage | 20 | 30 | 55 mins |
| 49-52 | Artificial Intelligence (4 Tests) - Complete subject coverage | 20 | 30 | 55 mins |
3. Full Syllabus Tests (Tests 53-60)
Complete GATE DA simulation with full syllabus coverage:
| Test No. | Test Type | Questions | Marks | Time |
|---|---|---|---|---|
| 53-57 | Full Syllabus Tests (5 Tests) | 65 | 100 | 180 mins |
| 58-60 | All India Mock Tests 2026 (3 Tests) | 65 | 100 | 180+ mins |
What's Included in the Test Plan?
- Topicwise Tests: Practice 15 questions per topic across all GATE DA subjects like Probability, Statistics, ML, AI, Python, DBMS, and Linear Algebra
- Subjectwise Tests: Test your depth with multiple focused tests per subject for comprehensive coverage
- Full-Length Mock Tests: 8 complete tests with 65 questions and 100 marks each – exactly like GATE DA exam format
Click here to view the complete test plan and activation schedule
Click here to view the test series announcement
Who Is This For?
This test series is designed for serious aspirants aiming for top ranks in GATE DA 2026. Whether you're a final-year student, a working professional, or a repeat aspirant — this is your one-stop practice destination.
Why Choose Mindspan Education?
At Mindspan Education, we believe in smart, structured, and targeted preparation. Our test series is built not just to test, but to teach — by helping you learn from mistakes, sharpen concepts, and improve with every attempt.
Get ready to crack GATE DA 2026 with confidence. Enroll in the Mindspan Test Series today and take the first step toward your dream score.
What You'll Achieve
- Evaluate your knowledge across all competitive exam topics
- Master time management under real exam conditions
- Identify knowledge gaps through detailed performance analytics
- Get familiar with actual competitive exam patterns and formats
Requirements
- Basic understanding of your target exam subjects
- Access to a computer or mobile device with internet connection
- Regular practice commitment and dedication to improvement
Key Features
Real Exam Simulation
Designed to exactly match competitive exam patterns and difficulty levels
Performance Analytics
Comprehensive performance reports with detailed insights and improvement tips
Detailed Solutions
Step-by-step explanations with shortcuts and time-saving techniques
Available Tests
- No tests available in this section
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Linear Algebra ST 01
- No tests available in this section
Expert Team
Competitive Exam Specialists
Our expert team comprises experienced educators who specialize in competitive exam preparation. With years of experience in training successful candidates across various government job examinations, our instructors bring deep understanding of exam patterns, effective teaching methodologies, and proven strategies for success.