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...
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 ex...