Data Structures, Algorithms & Computational Thinking provides a comprehensive study of fundamental data structures, algorithm analysis, and systematic computational problem-solving. The course examines how data can be organized efficiently, how algorithms can be designed and evaluated, and how computational performance can be improved through suitable structural and procedural choices. Topics include asymptotic analysis, arrays, linked lists, stacks, queues, recursion, divide-and-conquer, trees, binary search trees, heaps, hashing, sorting, graphs, graph traversals, and shortest path algorithms. Laboratory work emphasizes implementation, testing, complexity analysis, and performance comparison across alternative solutions.