This week’s lessons strengthened my understanding of how algorithm efficiency is measured using Big-O, Big-Theta, and Big-Omega notations. The lecture notes emphasized identifying the basic operation and using the dominant term to classify an algorithm’s growth order. Through quizzes, I learned why Big-Theta can only be used when an algorithm has the same time complexity in all cases, while Big-O represents an upper bound. The homework project applied these ideas in practice by showing how sorting often dominates overall runtime, leading to (n log n) complexity. Analyzing recursive algorithms using recurrence relations and backward substitution also helped clarify how time complexity evolves across recursive calls.
My educational goal in the Computer Science program is to become a skilled software engineer who can design and build innovative, efficient software solutions using both strong programming skills and AI tools responsibly. I want to complete my degree with a solid understanding of core computer science principles, such as algorithms, data structures, and system design, so I can solve complex problems without relying only on AI. After graduation, I aim to work on projects that improve people’s lives by combining creativity and technology, constantly learning new skills to stay ahead in the fast-changing tech world. This goal excites me because I see myself growing as a confident, adaptable developer who uses both traditional knowledge and AI to create a meaningful impact. My career goal is to be a software engineer who makes intelligent and helpful technology for people. I want to work at a company that uses AI and new tools carefully and responsibly. I want to grow as a leader who helps...
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