Skip to main content

CST438: Top 5 Key Lessons Learned from the Course

One of the most important things I learned in this course is how to build full-stack applications using Spring Boot and React. Working on projects with REST APIs, DTOs, and React components helped me understand how the frontend and backend communicate, which is essential for modern software development.

Another important concept I learned is testing, especially unit testing and system testing. Writing tests using tools like JUnit and Selenium showed me how to verify that my code works correctly and how to catch bugs early, which improves software quality.

I also learned how to use Git and GitHub effectively. Version control helped me manage my code, collaborate with others, and safely make changes without losing previous work. This is a critical skill in real-world development environments.

Understanding software design principles like low coupling and high cohesion was also important. These concepts helped me write cleaner, more maintainable code and made it easier to modify and extend my projects.

Finally, I learned about Agile development and how software is built incrementally. Concepts like iterations, user stories, and continuous improvement helped me understand how real teams manage projects and adapt to changing requirements.

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