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The complete, exam-focused guide to PDSA for GATE DA — Python programming, stacks, queues, linked lists, trees, hash tables, searching, sorting, mergesort, quicksort, and graph algorithms. Full syllabus, topic weightage, PYQ patterns, and the best course to score every mark.
Programming, Data Structures and Algorithms typically carries 8–12 marks in GATE DA — and it's Python-based, not C. The syllabus covers programming in Python, basic data structures (stacks, queues, linked lists, trees, hash tables), search algorithms (linear & binary search), basic sorting (selection, bubble, insertion), divide and conquer (mergesort, quicksort), introduction to graph theory, and basic graph algorithms (BFS/DFS traversals, shortest path). Highest-yield: Python output prediction, binary search, mergesort/quicksort tracing, tree traversals, and BFS/DFS order. The syllabus is deliberately limited — no dynamic programming, no AVL/heaps — so focused preparation pays off fast. Piyush Wairale's GATE DA PDSA course covers all of it with GATE-pattern practice and a test series.
PDSA is where GATE DA tests whether you can think like a programmer — and it does so entirely in Python. Every year the paper includes Python output-prediction questions (lists, slicing, mutability, recursion), algorithm-tracing questions (what does the array look like after 3 passes of bubble sort?), and traversal-order questions (BFS from vertex A, alphabetical tie-breaking). These are mechanical once practiced — pure scoring territory.
The GATE DA PDSA syllabus is also deliberately smaller than GATE CS: no dynamic programming, no greedy theory, no AVL trees or heaps as named topics. This means a focused candidate can achieve complete coverage — a rare opportunity in a GATE paper. Python fluency built here also pays off in the Machine Learning and data-handling portions of your Data Science journey.
Every topic below is officially part of the GATE Data Science & AI syllabus. Our course covers each one with theory, Python code examples, and GATE-pattern questions.
Based on GATE DA previous-year patterns, here is how the topics rank in exam importance. Prioritize the high-importance areas first.
| Topic | Exam Importance | Typical Question Style |
|---|---|---|
| Python Output Prediction | Very High | Code tracing — lists, slicing, recursion, functions |
| Binary Search | Very High | Comparison counting, mid-index sequence |
| Mergesort & Quicksort | High | Partition/merge tracing, complexity analysis |
| Tree Traversals (BST) | High | Inorder/preorder/postorder reconstruction |
| Graph Traversals (BFS/DFS) | High | Traversal order with tie-breaking rules |
| Stacks & Queues | High | Operation sequences, output after push/pop series |
| Basic Sorts (Selection/Bubble/Insertion) | Medium-High | Array state after k passes, swap counts |
| Shortest Path | Medium | Distance computation on small weighted graphs |
| Hash Tables | Medium | Collision resolution, final table state |
| Linked Lists | Medium | Pointer manipulation, operation results |
Follow this sequence to build concepts logically and avoid gaps:
A dedicated, syllabus-mapped course covering Python programming, data structures, searching, sorting, and graph algorithms for GATE Data Science & AI — with concept lectures, code-tracing practice, solved GATE-pattern problems, and topic-wise tests. Built by an IIT Madras M.Tech who has taught the IIT Madras BS Degree Programme.
Former instructor, IIT Madras BS Degree Programme • Microsoft Learn, AWS Academy & NPTEL Educator • 20,000+ students & 44,000+ YouTube subscribers.
The syllabus covers programming in Python, basic data structures (stacks, queues, linked lists, trees, hash tables), search algorithms (linear search and binary search), basic sorting algorithms (selection sort, bubble sort, insertion sort), divide and conquer (mergesort, quicksort), introduction to graph theory, and basic graph algorithms (traversals and shortest path).
Python is the official programming language for GATE DA. Unlike GATE CS which uses C, all GATE DA code-based questions — output prediction, code completion, recursion tracing — are in Python. Get fluent with Python syntax, list/dictionary operations, slicing, mutability, and recursion.
PDSA typically carries 8–12 marks in GATE DA. Python output-prediction questions, sorting algorithm tracing, binary search comparison counting, and BFS/DFS traversal order questions are highly pattern-based, making PDSA a section where consistent practice converts directly into marks.
The most frequently tested topics are Python code output prediction (lists, slicing, recursion), binary search (comparison counts), mergesort and quicksort (partition tracing, complexity), stack/queue operation sequences, tree traversals (inorder, preorder, postorder), and BFS/DFS traversal order. Python output questions appear every year.
The GATE DA Programming, Data Structures and Algorithms course by Piyush Wairale (M.Tech, IIT Madras) is designed specifically for the Python-based GATE Data Science & AI syllabus. It covers Python programming through graph algorithms with GATE-pattern PYQs, code-tracing practice, and a full test series at piyushwairale.com.
No. The GATE DA syllabus is limited to basic data structures, linear/binary search, elementary sorting, divide and conquer (mergesort, quicksort), and basic graph algorithms (traversals and shortest path). Dynamic programming, greedy algorithm theory, AVL trees, and heaps as separate topics are part of GATE CS, not GATE DA — keep your preparation focused.
Get the full Python-based syllabus, concept lectures, code-tracing practice, GATE-pattern questions, and test series — built for GATE Data Science & AI aspirants.