UNIQUE SERIES AKTU Syllabus
ACS201

Essentials of Data Structures

L-T-P2-1-0 Credits3 Session2026-27 TypeTheory
Appears in 39 semesters · 39 branches

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Syllabus

5 units · weightage in %
Unit1

Unit 1: Introduction and Arrays

20%
  • Data structure — definition, need, classification (linear / non-linear, static / dynamic)
  • Algorithm efficiency — Big O notation: O(1), O(n), O(log n), O(n²) — concept only
  • Array — insert, delete, search, traverse in C/Python
  • 2D array — matrix addition and transpose Real connect:NumPy array (AI/ML), image as 2D matrix, sensor data log as array, spreadsheet rows.
Unit2

Unit 2: Linked List

20%
  • Node — data + pointer; why linked list over array
  • Singly linked list — insert (head, tail, position), delete, traverse
  • Doubly linked list — concept and structure only
  • Circular linked list — concept only Real connect: Browser back/forward = doubly linked list. Music playlist = circular. Python deque = doubly linked list (used in BFS, LRU cache).
Unit3

Unit 3: Stack and Queue

20%
  • Stack — LIFO; push, pop, peek; implementation using array in C/Python
  • Application: balanced parentheses check; function call stack (concept)
  • Queue — FIFO; enqueue, dequeue; implementation using array in C/ Python
  • Application: print spooler; task scheduling queue, Polish Notation Real connect: Ctrl+Z = Stack. WhatsApp delivery = Queue. Python error traceback IS a call stack. CPU scheduling = queue.
Unit4

Unit 4: Searching and Sorting

20%
  • Linear search, binary search, comparison
  • Bubble sort and insertion sort, Quick Sort and Selection Sort — trace + C/ Python
  • Merge sort — concept and trace only
  • Python built-in sort() and sorted() Real connect: Binary search in database index. Python sort() uses Timsort. E-commerce ranking, leaderboard, ML data preprocessing = sorting.
Unit5

Unit 5

20%
  • Trace + Python demo Trees and Graphs (Concept Level)
  • Binary tree — terminology (root, leaf, height, level); properties
  • Binary Search Tree — insert, search; inorder/preorder traversal trace
  • Graph — vertex, edge, directed/undirected; adjacency list representation, insertion
  • BFS and DFS — trace on a small graph (5– 6 nodes) Real connect: File system = tree. BST in database indexing. Google Maps = weighted graph. Social network = graph. Decision Tree in ML = binary tree. Suggested Assignment / Practice Problems (Unit-wise) but not limited to No Tutorial hour in ACS201. The following problems are assigned as class assignments and self-practice. 2–3 per unit recommended for internal assessment. Unit Trace on Paper Code (C/Python) I Arrays
  • Trace insertion at index 2 in [10,20,30,40] 2. Trace deletion of element 30 from [10,20,30,40] 3. What is O(log n)? Give one example
  • Input N numbers; find max, min, average 2. Count even and odd in array 3. Remove duplicates from array II Linked List
  • Draw linked list after inserting 10, 20, 30 at head 2. Trace deletion of node 20 from [10→20→30→NULL] 3. Difference between singly and doubly linked list
  • Create linked list; insert at head; print 2. Insert node at tail
  • Delete node by value III Stack & Queue
  • Trace push(5), push(10), pop(), push(15) — what is top?
  • Trace enqueue(A,B), dequeue(), enqueue(C) — what is front? 3. Check '({[]})' balanced — show stack trace
  • Stack using array — push and pop 5 elements 2. Queue using array — enqueue and dequeue 3. Balanced parentheses checker IV Search& Sort
  • Binary search for 35 in [10,20,30,35,40,50] — show steps 2. Bubble sort trace on [64,34,25,12] 3. Insertion sort trace on [5,3,8,1]
  • Linear search and binary search — compare 2. Bubble sort — count swaps 3. Python: sort a list ascending and descending V Tree & Graph
  • Draw BST after inserting 50,30,70,20,40 2. Write inorder and preorder of above BST 3.
  • BST insert and inorder in python 2. Graph as adjacency list in Python dict 3. BFS using queue in Python (10–15 lines) BFS from A on: A-B, A-C, B-D, C-E
Course Objectives5 points
  • Understand data structures and why choosing the right one affects program performance
  • Implement linked list and perform basic insert, delete, and traverse operations
  • Implement stack and queue and apply them to simple real-world problems
  • Apply searching and sorting algorithms and compare their efficiency
  • Understand trees and graphs at a conceptual level with real-world connections
Course Outcomes (COs)5 outcomes

After completion of the course student will be able to:

  1. CO1Explain data structure types and Big O; implement array insert, delete, and search in C/PythonK3
  2. CO2Implement singly linked list operations and relate to real-world applicationsK3
  3. CO3Implement stack and queue using arrays and apply them to solve simple problemsK3
  4. CO4Apply linear and binary search; trace and implement bubble and insertion sortK3
  5. CO5Trace BST insert and traversal; trace BFS/DFS on a small graphK2

Suggested Textbooks / Reference Books

  1. Data Structures Using C – ReemaThareja (Oxford University Press)
  2. Data Structures and Algorithms in Python – Goodrich, Tamassia, Goldwasser(Wiley)
  3. Problem Solving with Algorithms and Data Structures using Python – Brad Miller (free: runestone.academy)

Online Resources

  1. VisuAlgo – visualgo.net (free animations of every DS and algorithm — use in every class)
  2. HackerRank – Data Structures track, hackerrank.com (C++/Python, free, auto-graded)
  3. Runestone Academy – runestone.academy (free interactive Python DSA book)
  4. LeetCode Easy – leetcode.com (start after this course; industry-standard placement prep)

Official AKTU syllabus

This subject is on page 34, 35, 36, 37, 38 of the original AKTU PDF.

Official AKTU syllabus PDF ↗