The first couple chapters of the ISLR deal with vocabulary and underlying concepts. I’ve chosen to summarize them in a series of hopefully more engaging and descriptive posts that break the information down topically. If you come from a STEM background, all of this should be at least passingly familiar, if not outright dull. If you don’t have a STEM background, then bear with me, cause the topic material (pretty much from here on out) is going to be dry and sometimes full of strange symbols that will frighten and enrage and confuse you at first. Rest assured, they are not sinister – they are in fact mostly chosen arbitrarily or aesthetically. They are merely tools, and you are here to learn how to make them do your bidding.
I’ll include links that explain various things in more detail if you’re like me and consistently find yourself having anywhere from 30 to 100 browser tabs open at one time that you never really fully read through. So if you’re ready to add to your tab count, or otherwise just want a brief overview of some important basic statistics concepts and vocabulary, then by all means, jump down the rabbit hole!
Continue reading “01.1 – Let’s talk about how to talk about statistics! Part one”
This will be the first of hopefully a long series of posts of me learning the R programming language by working through the examples in “An Introduction to Statistical Learning“, which is free in PDF form. Before we get too deep into code and math, keep reading if you want a high level overview of Python (which I am mostly comfortable with) and R (which I know very little of).
Continue reading “Teaching Myself R via the ISLR”
I’ve recently made the decision to focus this blog on my journey in data science. With that, I wanted to give a brief explanation of the name change, what I’ve been up to recently, and what I plan on doing moving forwards.
Continue reading “What’s with the new name?”
So those of you who may know me personally may be aware that I recently completed General Assembly’s Data Science Intensive Fellowship. I’ve had a few months to internalize the experience, figure out what other skills may help me get employed, and get a taste of the current job market. What follows is my deconstruction of the program:
Continue reading “What I learned from a Data Science Bootcamp”
It’s been the better part of two years since I last wrote a post for this site. The reason for this travesty is that I took a job for an abusive employer. I was working 80 hours a week, often including weekends. The breaking point was when I caught my employer stealing leave.
As my experiences withing my chosen field of chemistry have been mostly negative, and since there is little opportunity for chemists in Austin, TX anyways, I have chosen to pursue a career in Data Science. After all, I already have a good handle on the math and the concepts. Might as well learn the code. And since most of the data science world works in Python, I can use that to sharpen my skills in TouchDesigner.
Continue reading “It’s Been a While. . . .”