My CS Degree Journey

 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...

  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...

  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...

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...

  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...

  WK01: Weekly Reflection This week's module introduced important foundations in algorithms and data structures. I learned what an algorithm is, how algorithms solve problems step by step, and how pseudocode can be used to plan a solution before writing actual code. The videos on Euclid's algorithm for GCD calculation helped me understand how a simple repeated process can solve a mathematical problem efficiently. One thing that went well this week was learning how to break problems into smaller steps. The pseudocode examples made it easier to think logically about a problem without worrying too much about programming syntax. I also learned about important problem types such as sorting, searching, graph problems, and the Traveling Salesperson Problem. These topics showed me that many computer science problems can be grouped into common categories. The data structures materials were also useful, especially the introduction to graphs and trees. I learned that graphs can represent ...