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Using Data Science to Help Kids Graduate

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Photo by Siora Photography on Unsplash High school graduation rates are the final metric of school achievement. School districts can ensure their students feel safe and connected, but the ultimate measure of success is still whether the kid gets across that mortarboard-littered finish line. Timely graduation is associated with all sorts of positive outcomes, so this metric is a truly valuable one. Because graduation is so crucial, school districts are understandably very interested in taking steps to ensure as many students as possible graduate on time. This often means that districts are trying to identify at-risk students early in order to help them before an on-time graduation becomes impossible. Finding these students was the task taken on by Lakkaraju, et al, in their 2015 paper "A Machine Learning Framework to Identify Students at Risk of Adverse Academic Outcomes." The researchers used standard machine learning algorithms to predict which students were most at risk, ...
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Back to Binary with OneHotEncoder

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Photo by Pang Yuhao on Unsplash I found this data set about college students that had a bunch of demographic info and also details about whether they completed their degree. I'm very interested in the conditions that make it more or less likely for a person to succeed in school, so I wanted to dig in and see how life outside of school possibly affects how a student does in school. There's been a bunch of research showing how socioeconomic status and parental education levels affect student (and those two variables are ripe for issues of multicollinearity), so I didn't want to reinvent the wheel. Something that intrigued me about this data was a column about marital status. For obvious reasons, that's not something you see a lot in analysis of K-12 education. A student's family of origin often plays a massive part in their success, so I was very curious to see what effect a chosen family member might have. Photo by  Hannah Busing  on  Unsplash So I had a list of ov...
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Python Lambda Functions Hurt My Head

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Learn to code, they said. It's important. Learn to code, they said. You're good at logic. Learn to code, they said. It's just like algebra. Sure. Code is important and usually logical and follows a lot of math rules. But there's a few things that keep tripping me up because they seem to break some of the conventions of algebra. Lambda Functions in Python are one of those tricky things that I really struggle to consistently wrap my head around. They should be so easy! They're literally disposable! And they look straightforward, since they are usually quite short. But for some reason I am always getting stuck writing them, because I feel like they have one too many undefined variables. I feel like I'm trying to solve a system of equations with two variables, but somebody only gave me one of the equations. Somehow I balk at this every time and think I haven't possibly provided enough information for the function to properly run. I'm racking my brain adding ...
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How I Learned to Stop Worrying and Love to Code

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  “Could I learn how to code?” My husband and I were running an errand and he’d been telling me about a coworker at his tech job who’d majored in poetry. This anecdote led me to wonder if I, a veteran high school teacher, could also be successful in a tech field. Ben gave me a funny look and replied, “OF COURSE you could learn to code.”  He spoke as if this were the most obvious statement in the universe. Let’s be clear here: It was NOT obvious to me.  Don't believe everything you think I’ve always thought I was pretty smart, but I’ve been on a fairly recent journey of realizing some limiting beliefs I’ve held about myself. For example, I thought I wasn’t good at math because I couldn’t do arithmetic in my head. Despite acing my calculus class, I still didn’t think I had math chops because I wasn’t fast at it. I left high school saying I’d never take another math class if I could get away with it, even though I thought math was interesting and often elegant. Similar mis...
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