Week 4 - BALT 4363 - CH 4

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Chapter four of Data Toolkit: Python + Hands-On Math teaches us how machine learning and AI are able to sort through large datasets as efficiently as possible, and that is linear algebra. The hardest class I have taken in college was a finite mathematics class, so when I saw that a whole section in this chapter was dedicated to matrices, I began to sweat. While I am mostly kidding, it took a lot of studying to understand matrices, so I am glad all those hours went toward something with practical applications. If there is one thing I will take away from these data AI classes, it is that people who can analyze and use data to their advantage will always be in demand. With that, tools make this data work much easier, and the people who can program the tools are in even more demand. 

Learning this would be useful in practical situations, especially in my field, accounting. For example, I am in the middle of audit fieldwork working with large sets of financial data, and matrix operations could help analyze trends, perform check and invoice exams way faster than I could, or automate calculations across thousands of rows of financial data. Understanding even the basics of linear algebra would make data feel less overwhelming, and I want to get to the point where I can implement some of these tools to make my work faster, more accurate, and more analytical.



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