CST383 - Module 1

What did I learn in the first week of CST383?

This week introduced NumPy, Python, and machine learning concepts. Starting with Python, the slides introduced how it is an object-oriented language that is structured through whitespace, while having the variables dynamically typed. The slides also illustrated an assortment of different functions, which showcased the difference between expressions and statements. As for NumPy, it was really interesting given the assortment of different operations that can be done through this library, as NumPy is capable of storing all elements of the same type within a contiguous block in comparison to basic Python.

One of the areas that took me some time to understand was the difference in how the question was being asked within the programming labs, as one stated 'type of values in x' and the other was 'type in x'. I eventually realized that it was stating type() and dtype. Another concept that I struggled on was boolean masking, given that it dealt with filtered array's using conditions without writing a loop. This raises two questions, are there other commonly used NumPy properties beyond dtype, and what is the difference between a pre-stored mask versus writing the condition inline directly?

In the end, outside of this course, having priorly worked with Python through Codecademy, I honestly didn't realize there were specialized libraries like NumPy, as I had a pre-conceived understanding that it was all just Python. However, seeing how NumPy is used through the labs so far has helped me to understand just how much more efficient NumPy is in comparison to Python alone. Therefore, this makes me curious on how other Python libraries improve efficiency, data, and optimization, which I hope we can explore later on in this course.