Cracking the GATE exam for Data Science & Artificial Intelligence (DA) or Computer Science & Information Technology (CS&IT) is within your reach. GeeksforGeeks offers complete GATE courses designed to transform you from unsure to GATE exam-ready.
Our GATE faculty are experts who make tough concepts easy to understand. You’ll also get plenty of practice problems to sharpen your skills and build confidence. With GeeksforGeeks, you’ll have all the guidance and resources you need to crack the GATE exam and achieve your goals.
Key Features of Online Classes
- 1000+ hours recorded and 1000+ hours live classes for CS&IT.
- 600+ hours recorded, and 600+ hours live for DA
- 5000+ Questions and their video solutions as Daily Practice Problems (DPPs)
- 24/7 One-on-One AI Doubt Assistance
- Guidance from Expert GATE Mentors
- Comprehensive GATE Syllabus Coverage
- Supporting Notes & Documentation for every lecture
- Previous Year Question (PYQ) Solutions (last 15 years in the form of Quiz)
- Skill Assessment Contests (for CS&IT)
- Weekly Study Schedules & Strategy
- Problem-Solving Sessions
- Exclusive Access to E-Study Materials
- Test Series for DA and CSE&IT
GATE Courses Offered By GeeksforGeeks
Course Name | What You Will Learn | Explore |
|---|---|---|
GATE Computer Science & Information Technology | 1. Discrete Mathematics: Logic, sets, relations, functions, graphs, combinatorics 2. Digital Logic: Number System, Minimization, Combinational Circuit and Sequential Circuits. 3. Computer Organization & Architecture: Machine instructions, ALU, pipelining, memory hierarchy, I/O interfaces. 4. C Programming Data types and operators, control flow, functions, arrays, pointers, structures. 5. Data Structures Linked lists, stacks, queues, trees, graphs, and hashing. 6. Algorithms Searching, sorting, asymptotic worst-case time and space complexity, algorithm techniques, and graph algorithms. 7. Theory of Computation Regular expression and Finite Automata, Context-free grammars and push-down automata Regular and contex-free languages, pumping lemma, Turing machines and undecidability. 8. Compiler Design Lexical analysis, parsing, syntax-directed translation, Runtime environments, Intermediate code generation, Local optimisation, Data flow analyses. 9. Operating Systems Processes, threads, synchronization, deadlock, scheduling, memory, and file management. 10. Database Management ER model, relational algebra, SQL, normalization, indexing, transactions. 11. Computer Networks OSI/TCP/IP, switching, data link, and network protocols, routing, IPv4, transport, and application protocols. | |
GATE Data Science and Artificial Intelligence | 1. Probability & Statistics Counting principles, probability axioms, independent/mutually exclusive events, Bayes’ theorem, expectation & variance, descriptive statistics, correlation & covariance, random variables, major distributions (Bernoulli, Binomial, Poisson, Normal, Exponential, t, Chi-Square), CLT, CDF, confidence intervals, and hypothesis tests (z-Test, t-Test, Chi-Square). 2. Linear Algebra Vector spaces, subspaces, linear dependence, matrices (projection, orthogonal, idempotent, partition), quadratic forms, Gaussian elimination, eigenvalues/eigenvectors, determinant, rank, nullity, projections, LU & SVD decomposition. 3. Calculus & Optimization Functions, limits, continuity, differentiability, Taylor series, maxima & minima, single-variable optimization. 4. Data Structures & Algorithms (Python) Python programming basics, stacks, queues, linked lists, trees, hash tables, searching (linear, binary), sorting (selection, bubble, insertion, merge, quicksort), graph algorithms (traversals, shortest path). 5. Databases (DBMS) ER & relational models, relational algebra & SQL, constraints, normalization, indexing (B, B+ trees), transactions, concurrency control. 6. Data Warehousing Multidimensional schemas, concept hierarchies, and measures. 7. Machine Learning Supervised: regression (linear, multiple, ridge, logistic), classification (k-NN, Naive Bayes, LDA, SVM, decision trees), neural networks (perceptron, feed-forward). Model Evaluation: bias-variance tradeoff, cross-validation (k-fold, LOO). Unsupervised: clustering (k-means, k-medoids, hierarchical), dimensionality reduction (PCA). 8. Artificial Intelligence Search strategies (uninformed, informed, adversarial), propositional & predicate logic, reasoning under uncertainty (conditional independence, exact & approximate inference). |