CST438: Sprint Planning & Team Collaboration Reflection

This week, I learned the importance of clear communication and teamwork during sprint planning. Our team successfully reviewed feedback, confirmed that all tasks and user stories for Iteration 2 are well understood, and ensured that responsibilities are evenly distributed through the GitHub project board. I also learned how regular check-ins, early scheduling, and consistent updates can help keep everyone aligned and prevent delays as we move forward with the project.

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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...

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...

CST462S - From Learning to Impact: My Service Learning Journey

What went well during my service learning experience was my ability to contribute meaningfully to the ASCENDtials web team. I was able to complete several tasks such as updating website pages, working on LifterLMS courses, and improving user experience through better layouts and navigation. I also communicated effectively with my team, asked questions when needed, and stayed consistent with meeting deadlines. Over time, I became more confident using tools like WordPress, WPForms, and course-building platforms. If I could improve something, it would be my time management and planning. There were moments when tasks felt overwhelming, especially when balancing schoolwork and service hours. I would also improve my confidence in decision-making, particularly when working independently on design or technical issues. Taking more initiative earlier and asking for feedback sooner would have made my work even stronger. The most impactful part of this experience was seeing how my work directly co...

My CS Online Journey

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