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WK1: CST438 - Software Engineering

WK01: Weekly Reflection Before starting this course, I expected Software Engineering to focus mainly on the process of developing software, including project planning, software design, testing, and teamwork. I thought we would learn about development methodologies such as Agile, how to organize projects, and how to write clean and maintainable code. I also expected some practical programming assignments, but I assumed the course would focus more on the organizational aspects of software development than on coding itself. After completing the first week, my understanding of software engineering has become broader. The first chapter of Software Engineering at Google helped me better understand the difference between programming and software engineering. Programming is about writing code to solve a particular problem, while software engineering also considers how software will be maintained and modified over time. I found the discussion about time, scale, and trade-offs particularly inter...
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Week 66: CST370 - Design and Analysis of Algorithms

WK07: Weekly Reflection This week's module focused on dynamic programming, Warshall's algorithm, Floyd's algorithm, greedy techniques, and Prim's algorithm. These topics helped me understand ways to solve optimization and graph problems. One topic that stood out was dynamic programming. I learned that it works by breaking a problem into smaller overlapping subproblems and reusing previously calculated results instead of solving the same problem again. The Coin-Row problem helped me understand this idea because the goal is to choose coins with the greatest total value without selecting adjacent coins. I also practiced the Coin-Collecting problem, where values are calculated step by step across a grid to find the maximum number of coins that can be collected. Another important topic was Warshall's algorithm. I learned that it is used to find the transitive closure of a directed graph. It shows whether a path exists from one vertex to another, even when there is no di...
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Week 65: CST370 - Design and Analysis of Algorithms

 WK06: Weekly Reflection This week's module focused on AVL trees, 2-3 trees, heaps and Heapsort, and hashing. These topics helped me understand how different data structures maintain organization and efficiency when storing, searching, inserting, and removing data. I also learned how balancing, restructuring, and choosing an appropriate storage method can improve the performance of algorithms. One topic that stood out to me was the AVL tree. I learned that an AVL tree is a self-balancing binary search tree in which the heights of the left and right subtrees must remain balanced. The balance factor of each node is calculated by comparing the heights of its subtrees. When the balance factor becomes too large or too small, rotations are used to restore the tree’s balance. The exercises helped me practice identifying whether a binary search tree was also an AVL tree. The AVL tree operations were especially useful because they showed how rotations maintain efficient performance. I learn...
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Week 64: CST370 - Design and Analysis of Algorithms

  WK05: Weekly Reflection This week's module focused on Quick Sort, binary tree traversal and height calculation, decrease-and-conquer algorithms, topological sorting, and transform-and-conquer through pre-sorting. These topics helped me understand how different algorithm design techniques can simplify problems and improve efficiency. One topic that stood out to me was Quick Sort. I learned that Quick Sort is based on the divide-and-conquer technique. It selects a pivot, partitions the remaining elements into smaller and larger groups, and then recursively sorts those groups. The median-of-three partitioning method was also useful because it showed how choosing a better pivot can improve the performance of the algorithm. The exercises helped me understand how partitioning works step by step and why pivot selection can affect efficiency. The binary tree materials were also helpful. I learned about different traversal methods, including preorder, inorder, and postorder traversal. Eac...
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Week 63: CST370 - Design and Analysis of Algorithms

  WK04: Weekly Reflection This week's module focused on merge sort and preparation for the midterm exam. Since there is only one lecture this week, most of my time is being spent reviewing past topics and practicing problems for the upcoming test. The merge sort lecture helped me better understand the divide and conquer strategy. I learned that merge sort works by dividing a list into smaller parts, sorting those parts, and then merging them back together in order. This made recursion easier to understand because I could see how smaller problems combine to solve a larger problem. One thing that stood out to me was the merging step. Splitting the list is important, but the algorithm depends on carefully combining the sorted parts. This showed me that divide and conquer is not just about breaking a problem apart, but also about having a clear method for putting the solution back together. The rest of the week is focused on midterm review. I am going back over topics such as brute for...
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Week 62: CST370 - Design and Analysis of Algorithms

WK03: Weekly Reflection This week's module focused on brute force string matching, exhaustive search, depth-first search, breadth-first search, and the introduction to divide and conquer algorithm design. These topics helped me understand different ways algorithms solve problems, from simple direct methods to more organized search and problem-solving strategies. One thing that stood out to me was brute force string matching. I learned that brute force approaches are often easy to understand because they try possible solutions directly, but they may not always be efficient for large inputs. The examples with string matching helped me see how an algorithm can compare characters step by step and still become expensive as the input grows. The exhaustive search topics were also useful because they showed how some problems can be solved by checking every possible option. The lectures on the traveling salesperson problem, knapsack problem, and assignment problem helped me understand why e...
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Week 61: CST370 - Design and Analysis of Algorithms

  WK02: Weekly Reflection This week's module focused on asymptotic notation and the analysis of algorithms, which helped me understand how computer scientists compare solutions beyond just whether they work. I learned that asymptotic notations such as Big O, Big Omega, and Big Theta describe how an algorithm's running time grows as input size increases. This gave me a clearer way to talk about efficiency when comparing algorithms that solve the same problem. One thing that went well this week was learning the difference between informal and formal definitions of time efficiency. The examples made me realize that Big O is not just about counting every single step, but about understanding the overall growth pattern. I also found the exercises helpful because they gave me practice recognizing whether an algorithm grows linearly, quadratically, logarithmically, or differently.  I learned how to analyze loops, nested loops, and basic operations to estimate running time. The Big The...
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