This week, I rehashed all the basics of Python. Since I haven’t studied it at all in ten years, this was a very useful refresher. (Basically, it seems to me that Python is essentially Java structure with something like JavaScript syntax. This is a huge oversimplification, but hey, it’s an extremely high-level language that I’m using it in an object-oriented way for this purpose. There are demonstrable similarities.)

The course I’m currently using doesn’t go over Python in any great detail, so if you’d like to supplement the Python they teach you, or you’d like to add to your knowledge of the language (since this course teaches only a very limited scope of Python), I highly recommend Learn Python The Hard Way. Python was my first programming language ever, and this was the course I used. It gives you a solid grasp of not just Python but how programming works in general.

In addition to the general Python refresher, I learned about all the libraries that I’ll need to use it to do data science: namely, NumPy, Pandas, SciPy, StatsModels API, MatPlotLib, Seaborn, and SciKitLearn. In combination, these libraries add methods that can import data from a variety of sources including Excel spreadsheets, conveniently calculate and tabulate relevant statistical data, do a variety of regressions and cluster analyses, and display elegant and understandable graphs.

This week, I learned how to do a simple linear regression (least squares). Next week, I’ll learn how to do multiple regressions and cluster analyses! And after that, the real fun begins with deep learning and AI. I’m looking forward to it!

In the future, expect me to start creating some little projects. I can’t do much with what I’ve learned this week, but by next week, I’ll absolutely have something at least moderately interesting, and I’ll absolutely do a nice write-up for it.

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