Skip to main content

Learning Merge Sort and Graph Traversals

This week’s module focused on algorithm design and analysis, with a strong emphasis on divide-and-conquer techniques, especially Merge Sort. From the lecture video captions, I learned how Merge Sort works by repeatedly dividing an array into two halves until each subarray contains only one element. Since a single element is already sorted, the algorithm then focuses on the merge step, where two sorted arrays are combined by comparing elements one at a time. This helped me better understand why Merge Sort is efficient and reliable.

I also learned how to analyze Merge Sort using a recurrence relation and apply the Master Theorem to determine its time complexity. By identifying the values of a, b, and f(n), I was able to see why Merge Sort runs in Θ(n log n). In addition, the homework and quiz strengthened my understanding of DFS and BFS traversals, including how traversal order and marking rules affect the final result. Topics like the Traveling Salesman Problem, Knapsack Problem, and worst-case analysis helped me connect algorithm theory to practical problem-solving. Overall, this module improved my ability to analyze algorithms step by step and understand both their logic and efficiency.

Comments

Post a Comment

Popular posts from this blog

CST383: Learning Probability Distributions and Data Visualization in Python

This week, I learned more about probability distributions, density plots, histograms, and how to visualize data using Python libraries such as Pandas, Matplotlib, Seaborn, and SciPy. I practiced creating density plots, box plots, cumulative density plots, and histograms using real datasets. I also learned how changing things like bin width, bandwidth, transparency, and sample size can affect the appearance and interpretation of graphs. Another important topic was understanding skewness and how transformations such as log10 can help make heavily skewed data easier to analyze. One thing I found interesting was how probability density functions (PDFs) and histograms can represent the same data differently. Before this week, I thought graphs mostly showed the same information in different styles, but now I understand that each type of plot has a different purpose and can make patterns easier or harder to notice. I also learned that larger sample sizes tend to reflect the true distribution...
Post a Comment
Read more

Choosing Between MongoDB and MySQL

This week I learned more about how MongoDB and MySQL are both powerful tools for managing data, but they serve different purposes. MySQL is a relational database that organizes data into tables with rows and columns. It uses SQL (Structured Query Language) to define and manage data, which makes it very structured and reliable. MongoDB, on the other hand, is a NoSQL database that stores data as documents in a flexible JSON-like format . It does not require a fixed schema, so it is easier to change or add new data types as needed. Both databases are similar because they can handle large amounts of data, support indexing for faster searches, and allow users to perform queries to get specific information. They are also widely used in modern applications and can be connected to programming languages like Java, Python, or C++. However, the key difference is how they store and organize data. MySQL is best when data has clear relationships, such as in school systems, banking, or employee ...
Post a Comment
Read more

CST383 Week 1: Python for Data Science

This week, I learned the basics of Python for data science and how tools like NumPy are used. I already have programming experience from my computer science classes, but Python feels different from languages like Java or C++. It is easier to write and more flexible because it does not require strict data types. This makes coding faster, but I also need to be careful to avoid mistakes. We also learned about the Python data science ecosystem, such as NumPy, Pandas, and tools like Google Colab and Jupyter Notebook. I liked using Google Colab because it is simple and runs in the browser, so I don’t need to install anything. However, I am curious when it is better to use local tools like Spyder or Jupyter instead of Colab. The most important concept for me this week was NumPy. I learned that NumPy arrays are much faster than Python lists because they store data in a continuous block of memory and use the same data type. This connects to what I learned in my algorithms class, where performan...
Post a Comment
Read more