GATE DA 2026 Coaching | Best Data Science & AI Preparation – Supremum Classes
Ace GATE Data Science & Artificial Intelligence (DA) 2026 with Supremum Classes – India's leading coaching institute for GATE DA preparation. Designed by IIT experts, our comprehensive course ensures you cover every topic with conceptual clarity, structured learning, and hands-on practice.
Course Highlights:
- Expert Faculty from IITs & Top Institutions
- Structured Syllabus Coverage – Machine Learning, AI, Statistics, Python & More
- Live Interactive Classes + Recorded Sessions for Revision
- Exclusive Study Material, Formula Sheets & Topic-Wise Notes
- Weekly Practice Tests & Full-Length Mock Exams
- Doubt-Solving Sessions & 1-on-1 Mentorship
- Scholarship Opportunities & Affordable Fee Structure
Course Inclusions:
- Online live lectures of full course
- Full HD recorded video of full course
- Regular test on Topic basis
- Live discussion class of each test
- Questions practice set regularly
- Live discussion class of practice set
- One-to-one mentorship | 24 x 7 WhatsApp Support
- Test series and discussion available
- Live Previous year discussion
Why Supremum Classes is the Best Choice for GATE DA 2026?
- Proven Track Record of Success – Our students consistently score top ranks.
- Exam-Oriented Approach – Focused preparation with mock tests & analysis.
- Flexible Learning – Online + Offline hybrid model with recorded sessions.
- Doubt-Clearing Support – 1-on-1 mentorship for personalized attention.
- Structured Study Plan – Covers entire syllabus with expert guidance.
GATE DA 2026 Syllabus – What You’ll Learn
- Probability & Statistics
- Counting (Permutations & Combinations), Probability Axioms
- Bayes Theorem, Random Variables & Distributions (Binomial, Poisson, Normal)
- Confidence Intervals, Hypothesis Testing (z-test, t-test, chi-squared test)
- Linear Algebra
- Vector Spaces, Matrices, Eigenvalues & Eigenvectors
- LU Decomposition, Singular Value Decomposition (SVD)
- Systems of Linear Equations & Gaussian Elimination
- Calculus & Optimization
- Limits, Continuity, Differentiability & Taylor Series
- Optimization Techniques (Gradient Descent, Lagrange Multipliers)
- Programming, Data Structures & Algorithms
- Python Programming, Data Structures (Stacks, Queues, Trees, Hash Tables)
- Sorting & Searching Algorithms (Binary Search, Quick Sort, Merge Sort)
- Graph Algorithms – Traversals & Shortest Paths
- Database Management & Warehousing
- SQL, Relational Algebra, Integrity Constraints & Normalization
- Data Warehousing, Schema Design, Indexing & Data Transformation
- Machine Learning (Supervised/Unsupervised Learning)
- Regression (Linear, Multiple, Ridge), Classification (SVM, Decision Trees)
- Naïve Bayes Classifier, KNN, Neural Networks & Bias-Variance Trade-off
- Clustering (K-Means, Hierarchical Clustering)
- Dimensionality Reduction (Principal Component Analysis - PCA)
- Artificial Intelligence (AI)
- Search Algorithms (Informed, Uninformed, Adversarial)
- Logic & Reasoning (Propositional, Predicate Logic)
- Probabilistic Reasoning (Bayesian Inference, Sampling Methods)
