GATE DA 2027 : Data Science & Artificial Intelligence Study Guide | Includes 2026-2024 Latest Solved Papers | 2600+ MCQs, NTQs & MSQs with Detailed ... Guide 2600+ MCQs Solved Papers 2024-2026

GATE Data Science & Artificial Intelligence (DA / DS-AI) 2027 Study Guide by GKP The world of engineering is evolving, and so is the GATE exam. GATE DA 2027: Data Science & Artificial Intelligence Study Guide by GKP is a meticulously engineered, all-in-one resource designed for the new-age aspirant. Whether you are aiming for a specialized M.Tech in AI/ML at a premier IIT or high-impact data science roles in modern PSUs, this guide provides the exact mathematical and algorithmic depth required to excel in the GATE DA paper. Combining concise subject-wise theory, a massive bank of 2,600+ practice questions, chapter-wise drills, and fully solved official papers (2024–2026), this guide serves as your complete study companion from foundational mathematics to advanced machine learning algorithms. Key Features of This Book: 2,600+ Exam-Calibrated Practice Questions: A diverse question bank covering Multiple Choice Questions (MCQs), Numerical Answer Types (NATs/NTQs), and Multiple Select Questions (MSQs)—strictly aligned to the latest GATE DA syllabus. Fully Solved Inaugural Papers (2024–2026): Analyze actual test benchmarks with fully solved papers from the official paper cycles, featuring complete, step-by-step logic. Digital Access to Archives: Exclusive online access to foundational solved practice papers and data-driven problem sets (access details provided inside). Structured Technical Theory: Complex AI and Data Science concepts—such as Gradient Descent, Neural Networks, and Matrix Decompositions—are broken down into easy-to-digest modules. Explanation-First Pedagogy: Solutions are designed to build your algorithmic intuition, helping you master complex mathematical proofs and solve "unseen" coding and data logic problems. Exhaustive GATE DA / DS-AI Syllabus Coverage: Probability & Statistics: Random Variables, Probability Distributions, Joint Distributions, Mean, Variance, Correlation, and Hypothesis Testing. Linear Algebra: Vector Spaces, Matrices, Eigenvalues, Eigenvectors, LU Decomposition, and SVD. Calculus & Optimization: Single and Multivariable Calculus, Maxima/Minima, Gradient Descent, and Constrained Optimization. Programming, Data Structures & Algorithms: Python Logic, Stacks, Queues, Trees, Searching, Sorting, and Graph Algorithms. Database Management & Warehousing: ER-Models, Relational Algebra, SQL, Normalization, and NoSQL Basics. Machine Learning: Supervised Learning (Regression, Decision Trees, SVMs), Unsupervised Learning (K-Means, PCA), and Neural Networks. Artificial Intelligence: Search Strategies (A*, Heuristics), Propositional & First-Order Logic, and Reasoning under Uncertainty. General Aptitude: Complete practice modules to secure the high-weightage 15-mark foundational section. Who Is This Book For? GATE DA 2027 Aspirants: Computer Science, Electrical, Electronics, and Allied Branch students transitioning into Data Science & AI. IIT M.Tech & Research Candidates: Students aiming for top percentiles to secure M.Tech / M.S. admissions in AI, Machine Learning, and Data Engineering at IITs, IISc, and NITs. Data & Tech Job Seekers: Candidates preparing for analytical roles in PSUs, research laboratories, and tech firms. Read more