Students learn important data structures in computer science and acquire fundamental algorithm design techniques to get the efficient solutions to several computing problems from various disciplines. Topics include the analysis of algorithm efficiency, hash, heap, graph, tree, sorting and searching, brute force, divide-and-conquer, decrease-and-conquer, transform-and-conquer, dynamic programming, and greedy programming.
In this course, I've delved into the essential realm of computer science algorithms and data structures, learning how to efficiently implement them. I gained insights into algorithm analysis, understanding how to measure their efficiency and express it using asymptotic notations. Various algorithm design techniques, including brute-force, divide-and-conquer, decrease-and-conquer, transform-and-conquer, dynamic programming, and greedy programming, have become familiar tools. I explored data structures such as hash, heap, tree, and graph, and got hands-on experience with sorting techniques like heapsort, merge sort, and quicksort. Tree and graph-based algorithms, computational complexity, and concepts of P & NP were also covered. This comprehensive journey equipped me with the skills to create efficient software systems while considering factors like space-time tradeoffs.
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