Week 1: CST383 Introduction to Data Science

    It has been a while since I worked with Python, so I was really excited this week to get back to it. We learned how to create 1D and 2D arrays, slicing, and fancy indexing. I especially liked working with boolean masks, because they make filtering data so much easier than using loops in regular Python. For example, I could quickly get tuition values below a certain amount just by applying a mask.

    I got confused between arange and linspace at first. The third parameter in linspace actually sets the number of points. For example, linspace(1, 4, num=6) gives six numbers evenly spaced between 1 and 4.

    I was also surprised by how fast NumPy arrays are compared to normal Python lists!!! They store all elements in the same type and in a continuous block of memory, which makes operations like addition, multiplication, and filtering more efficient.

    Vectorized operations were incredibly useful in the labs. Tasks like calculating acceptance ratios, finding below-average full-time students, and averaging tuition values all became simple one-line commands! Without them, I would have needed loops, which would have taken way longer and made the code harder to read. I did struggle a bit with indexing multi-dimensional arrays at first though, especially when combining slicing with fancy indexing, but watching the class recordings and reviewing the NumPy quickstart helped me understand it better.

    Through this class, I also learned about Google NotebookLM and Google Colab, which are really cool. I honestly feel like I’ve been living under a rock! Heard of them but never really used them until now, NotebookLM seems amazing for interactive coding and experimenting with data, while Colab allows me to write and run Python in the cloud without worrying about setup or installation. I’m excited to explore these tools more and see how they can help me with future projects!

Looking forward to learn more!