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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 performance and efficiency are very important. It made me think about how programming at a lower level can improve speed.

One area I found confusing is indexing, especially with 2D arrays and fancy indexing. Sometimes I am not sure if the result will be a 1D array or a 2D array. I also want to practice Boolean masking more because it seems very useful for filtering data without loops.

Another important idea is vectorized operations. Instead of using loops, NumPy can apply operations to the whole array at once. This is faster and cleaner, but it is still new to me, so I want to practice more.

Overall, this week helped me understand that data science is not only about coding, but also about using the right tools efficiently. Moving forward, I want to improve my skills in NumPy and become more comfortable working with arrays and data.

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