AKTU Essentials of Data Structures Syllabus 2026-27 (ACS201) — Full Unit-Wise Breakdown

If you're moving into Semester II wondering why Data Structures feels harder than Programming Languages did, here's the honest reason: this is the subject where "I can write code that runs" turns into "I can write code that runs well." It's a mindset shift, not just a syllabus jump, and it trips up a lot of otherwise strong first-year students.

Essentials of Data Structures is a Semester II Engineering Science Course under the AKTU B.Tech First Year CSE Stream syllabus 2026-27, carrying course code ACS201, with ACS101 – Programming Languages listed as an official prerequisite. That prerequisite isn't a formality — the whole course assumes you're already comfortable writing basic C and Python.

Quick Facts: AKTU Data Structures Syllabus 2026-27

Unit-Wise Course Content

Unit I: Introduction and Arrays (20% weightage)

You'll start with what a data structure actually is, the classification into linear/non-linear and static/dynamic types, and — crucially — Big O notation at a concept level: O(1), O(n), O(log n), and O(n²). This is where you learn to reason about efficiency, not just correctness. The unit also covers array insert, delete, search, and traverse operations in both C and Python, plus 2D array operations like matrix addition and transpose. The syllabus draws a direct line here to NumPy arrays used in AI/ML, images represented as 2D matrices, and sensor data logs.

Unit II: Linked List (20% weightage)

This unit introduces the node concept (data + pointer) and why a linked list beats an array in certain situations. You'll implement singly linked list insert (at head, tail, or position), delete, and traversal, plus study doubly and circular linked lists at a conceptual level. A genuinely helpful real-world connection: your browser's back/forward buttons work like a doubly linked list, and Python's deque (used in BFS and LRU cache implementations) is essentially a doubly linked list under the hood.

Unit III: Stack and Queue (20% weightage)

Here you'll implement Stack (LIFO — push, pop, peek) and Queue (FIFO — enqueue, dequeue) using arrays in both C and Python, with applications like balanced parentheses checking and understanding the function call stack. The real-world hooks are memorable: Ctrl+Z is literally a stack operation, a Python error traceback IS a call stack, and CPU task scheduling runs on queues.

Unit IV: Searching and Sorting (20% weightage)

This unit covers linear search and binary search with comparison, bubble sort, insertion sort, quick sort, and selection sort with tracing exercises, and merge sort at a conceptual/trace level. You'll also look at Python's built-in sort() and sorted() functions. Binary search underpins database indexing, Python's sort() actually uses Timsort internally, and sorting itself powers everything from e-commerce ranking to ML data preprocessing — worth remembering when the trace exercises feel repetitive.

Unit V: Trees and Graphs — Concept Level (20% weightage)

The final unit introduces binary tree terminology (root, leaf, height, level), Binary Search Tree insert and search with inorder/preorder traversal tracing, and graphs — vertices, edges, directed vs. undirected, adjacency list representation. You'll trace BFS and DFS on small graphs of 5-6 nodes. This unit closes the loop nicely: file systems are trees, BSTs power database indexing, Google Maps is a weighted graph, and decision trees in ML are literally binary trees.

How You'll Actually Be Assessed

There's no tutorial hour in ACS201 — instead, 2-3 practice problems per unit are recommended for internal assessment, split between "trace on paper" exercises (e.g., tracing insertion, deletion, or a stack push/pop sequence step by step) and actual coding tasks in C or Python. The teaching approach explicitly follows a "draw first, code later" philosophy — every structure is shown visually on the board before any code appears, with VisuAlgo animations used in class for sorting and tree/graph traversal.

Evaluation Pattern

Recommended Books and Free Resources

For a C-focused approach, Data Structures Using C by Reema Thareja is the standard AKTU recommendation. For a Python-first approach, Data Structures and Algorithms in Python by Goodrich, Tamassia, and Goldwasser is excellent, and Problem Solving with Algorithms and Data Structures using Python by Brad Miller is available free at runestone.academy. For practice, VisuAlgo.net is genuinely the best free tool for visualizing every algorithm in this syllabus, and HackerRank's Data Structures track offers free, auto-graded practice before you move on to LeetCode.

My Honest Take

Trace problems feel tedious compared to writing code, but they're actually the more important skill in this course — most placement interviews and even the AKTU exam pattern itself reward students who can trace an algorithm's execution by hand, not just students who can copy-paste a working implementation. If Unit V's graph traversals feel abstract right now, draw them out physically on paper multiple times before touching code. That's genuinely the fastest way through this unit.

Frequently Asked Questions

What is the course code for AKTU Essentials of Data Structures?

The course code is ACS201, a 2-credit Engineering Science Course in Semester II with ACS101 (Programming Languages) as a prerequisite.

Is there a lab or tutorial for Data Structures in AKTU's scheme?

No, ACS201 doesn't have a dedicated tutorial hour. Instead, 2-3 unit-wise practice problems (both trace-based and coding-based) are recommended for internal assessment.

Which topics carry the most weightage in this course?

All five units — Arrays/Introduction, Linked Lists, Stack/Queue, Searching/Sorting, and Trees/Graphs — carry equal 20% weightage each.

Do I need to know Python for this course, or is C enough?

Both C and Python are used throughout, with Python often used for quick, clear 5-line demonstrations. You should be comfortable with the basics of both, since ACS101 (which covers both) is a prerequisite.

How is Data Structures evaluated at AKTU?

30% internal assessment through class tests and assignments/quizzes, and 70% end-semester examination covering theory, trace problems, and short code-writing questions.

Are BFS and DFS covered in this syllabus?

Yes, both are covered at a conceptual and trace level in Unit V, applied to small graphs of 5-6 nodes rather than large-scale implementation.

Check out the AKTU Programming Languages (ACS101) syllabus, the prerequisite for this course, or go back to the complete AKTU B.Tech 1st Year syllabus overview.