GATE DA: most asked topics and weightage

Most asked topics in official papers

1. 1.Supervised learning22 questions · 34 marks
2. 2.Random variables and distributions18 questions · 30 marks
3. 3.Quantitative Aptitude14 questions · 21 marks
4. 4.ER model, relational model: relational algebra, tuple calculus, SQL10 questions · 16 marks
5. 5.Programming in Python10 questions · 18 marks
6. 6.Probability basics10 questions · 14 marks
7. 7.Functions of a single variable: limit, continuity, differentiability8 questions · 11 marks
8. 8.Verbal Aptitude8 questions · 11 marks
9. 9.Stacks, queues, linked lists, trees, hash tables8 questions · 10 marks
10. 10.Logic: propositional, predicate7 questions · 10 marks
11. 11.Search: informed, uninformed, adversarial7 questions · 10 marks
12. 12.Integrity constraints, normal form6 questions · 9 marks
13. 13.Unsupervised learning6 questions · 9 marks
14. 14.Maxima and minima; single-variable optimization5 questions · 8 marks
15. 15.Spatial Aptitude5 questions · 8 marks

Subjects ranked by share of marks

Share of the exam's marks that the pattern gives to each subject, and the topics it covers in the official syllabus.

1. 1.Probability and Statistics

   18% of marks

   Probability basics · Descriptive statistics · Random variables and distributions · Statistical inference

2. 2.Programming, Data Structures and Algorithms

   16% of marks

   Programming in Python · Stacks, queues, linked lists, trees, hash tables · Linear search and binary search · Selection, bubble, insertion sort; mergesort and quicksort (divide and conquer) · Introduction to graph theory; traversals and shortest path

3. 3.General Aptitude

   15% of marks

   Verbal Aptitude · Quantitative Aptitude · Analytical Aptitude · Spatial Aptitude

4. 4.Machine Learning

   14% of marks

   Supervised learning · Unsupervised learning

5. 5.Database Management and Warehousing

   12% of marks

   ER model, relational model: relational algebra, tuple calculus, SQL · Integrity constraints, normal form · File organization, indexing, data types · Data transformation: normalization, discretization, sampling, compression · Data warehouse modelling: multidimensional schema, concept hierarchies, measures

6. 6.Linear Algebra

   10% of marks

   Vector space, subspaces, linear dependence and independence · Matrices: projection, orthogonal, idempotent, partition matrices and properties · Quadratic forms · Systems of linear equations; Gaussian elimination · Eigenvalues, eigenvectors, determinant, rank, nullity, projections · LU decomposition, singular value decomposition

7. 7.Artificial Intelligence

   8% of marks

   Search: informed, uninformed, adversarial · Logic: propositional, predicate · Reasoning under uncertainty: conditional independence, variable elimination, approximate inference by sampling

8. 8.Calculus and Optimization

   6.30% of marks

   Functions of a single variable: limit, continuity, differentiability · Taylor series · Maxima and minima; single-variable optimization

Section-wise questions and marks