As I hurdle towards the inevitable – my college graduation in May 2017 – I’ve been thinking a lot about the purpose of my education, its place in the greater context of my life, and the way it intersects with my place in the world.
I came to Cooper Union essentially by luck, driven by the same things as most high-achieving students proficient in math and science my age – grades, class rank, the perception that those above me in the hierarchies of family and scholarship knew what was best, that the ultimate purpose was to be the Best, do the Best, whatever the hell that even meant. So I ended up applying to engineering school because it fit the persona I felt I was “supposed” to be (smart, useful, wealthy) and thought I wanted to be. Cooper was free, and falling in line with my tendency towards risk-aversion (failure means weakness, and weakness is unacceptable!), that too seemed like the Right thing to do. So I went to Cooper Union.
Like many of my classmates, I was intoxicated by the idea that STEM was a superior set of fields – we were “smart”, we got good grades (the ultimate validation), we enlightened beings understood that the only correct way to look at the world was through the eyes of logic, reason, and rationality. We were – ironically enough – objective zealots.
Except it isn’t in the slightest. Engineering in the way that it’s been presented to me by many of my professors and peers – an overwhelming series of theory-dense courses that reward rote memorization and the ability to perform well under arbitrary pressure, is anything but superior. Like the education of my early and formative years did, it shapes directionless students into 4.0-hungry followers and suppresses recalcitrance and stifles original thought. Of course it does – most of them(us?) have been raised with similar value systems that we swallowed without question – most of them don’t seem to have thoughts outside their field of study or quest for some nebulous sense of ‘success.’
The only time I’ve found myself to be truly happy/thoughtful in my time at Cooper – and I’m not talking about the spikes of adrenaline that accompany the feeling of checking my semester grades – is when I struggle to make sense of something, only to come to the realization that my original perception of the concept or idea in question is missing something. Some examples I can pinpoint: figuring out how cell towers work or experiencing critiques in the class that utterly upended my life.
Last semester I took an art class that challenged the way I saw myself in the classroom setting and totally altered my perceptions of what it means to have “a successful education.” For the first time, I was surrounded by people (artists) who all seemed to have passions and practices that drove their educations, instead of vice versa. There was no ‘right’ answer to find in the solutions manual; the point was not to smile and speak up in class and do the assignments so that the professor would like me and give me an A or a glowing recommendation so that I could get a job and make lots of money and retire in a house with a garage and some dogs. As someone incredibly comforted by following the rules and the paths of other people to avoid discomfort and failure, this class was a shock to my system.
For the first time in my life, I was forced to think for myself. “Bullshitting” a project, as my engineering peers call the execution of an assignment with the minimal amount of work and receiving a stellar final grade, was not a badge of honor anymore. Because making art isn’t about the grade you get when you present a finished work at critique. It’s about how a thought process is explored and questioned and expressed and critiqued, but it’s also about the fact that nothing is ever finished or answered. I assert these things about art, but to be completely honest, my experiences and perceptions of it are constantly changing but will never reach a deterministic truth. It’s exhilarating.
To be continued…
kylie_jenner = pandas.DataFrame(['KUWTK', 'Kylie Kosmetics', 'Tyga4ever', 'why is there a python in this picture?'])
Kylie Jenner said that 2016 was the year of realizing things, but I’d bet Cooper Union’s sticker price (TOO SOON) that she wasn’t referring to the illuminating experience of learning Python for fun over winter break. Yes, I realize that Kylie has lip kits and white Ferraris to focus on, but girl should check out pandas dataframes if she really wants to live.
In an attempt to get over myself and the resulting self-doubt and stubbornness that made me think I wasn’t capable of programming and therefore terrified of failing at it, I spent the last three weeks crash-coursing myself in Python and all of its very awesomely intuitive data science packages.
EdX is great – check out some of their ‘Python for Data Science’ courses if you’re trying to teach yourself to code and have some solid self-discipline to keep you going.
Now that I’m proficient in numpy, pandas, matplotlib, and scikit-learn, I’ve seen the light that is data manipulation/machine learning with Python and have all the regrets that I tried to do all my Statistical Learning assignments last semester in MATLAB. *shudders*
This is cool. Now I should make some cool things that attempt to answer some cool questions.
So if you read my aptly-titled ‘Brain Barf’ post, you know that I have all the feels about doing projects that fulfill my arbitrary standard of what is valuable and useful. Are those feels (and that post) just my thinly-veiled insecurities about never being good enough? Probably. Like I said, working on getting over myself.
I’ve come to terms with the fact that right now it is most valuable for me to practice my skills on projects that challenge the way that I think; doing significant things that change the world will come later when my skill level and mental elasticity get there.
So right now, I’m planning on doing projects related to some questions that I’ve jotted down in my notes recently:
What will happen if Congress defunds Planned Parenthood?
Yes I realize that this is a massive question, but I’m curious about the relationships between maternal death rates, infant and fetal mortality, and crime rates, among other things, and how they’ve changed since Planned Parenthood started offering abortion services in 1970. I also wonder if there’s a significant difference in the trends of graduation rates, suicide rates, and quality of life over that period of time between the biological sexes (namely the male and female sexes, as intersex data is largely unavailable).
The hardest part of this project will probably be the data collection. Some of the features that I’m interested in analyzing are readily available in nice clean datasets, but many (including some of the features I have yet to think of), are not.
What type of brown ale should we brew next?
For those of you following along at home, some important context:
- I’m a senior studying electrical engineering at Cooper Union (More About Me!)
- I helped start an interdisciplinary independent study in beer brewing last semester.
- We brewed some delicious stouts (milk and imperial), an IPA (session), a blonde ale, and a brown ale.
This semester, we’re continuing to brew for fun even though there aren’t credits involved, and we’re trying to refine our process and clone our favorite beers.
BeerAdvocate, a noted beer review website that we use for reference, has reviews for 2677 different brown ales alone. As tempting as it may be, it’s not feasible for our class of 5 people to try 2677 different brown ales before deciding which one to clone.
Enter data science! My brewing professor found a gigabyte worth of scraped beer reviews (YASSSSS I don’t have to deal with scraping!!!) that I can do some text analysis on. The preliminary plan is to look for themes in the reviews and determine the ones that match with our class’s verbal description of the type of brown ale we’d like to brew.
In my last post, I talked about a project in which a friend and I scraped popular Twitter hashtags related to sexual violence and performed LDA document clustering and topic modeling on them to see if any interesting patterns emerged. To be honest, it was pretty cool to see an algorithm differentiate users/tweets that supported victims of sexual violence from those who were less than empathetic.
The project has led me to question the utility of data science as it relates to social justice and digital activism – data scraping is cool, machine learning algorithms are fascinating, and meaningful outputs are interesting to talk about, but how does one take the knowledge derived from doing all this data sciencey stuff and actually do something USEFUL with it?
And by useful, I don’t mean the kind of data science that lets me tell HR how many people to fire to improve the bottom line or the kind that my professor gives me an A for because I made my data visualization look pretty. I’m talking about the “using this knowledge to make the world a better place” kind of data science.
So that leads me to the actual question that’s been bothering me for a while:
Does massaging Twitter content into categories do anything beneficial for survivors?
If not in its present state, could it?
This begs some really important questions about how I approach my future projects.
Is it responsible for me to collect data and apply algorithms without a clear direction or intention in mind? What if that direction is a really vague, “I wonder what will happen if…?” Is that approach a responsible way of avoiding self-fulfilling prophecy bias? Is bias in data science necessarily a bad thing?
Data science as a tool does not live inside a bubble. It is inherently outward-facing. According to every book, article, and MOOC out there, the point of it is to answer data-driven questions.
But what if that question isn’t useful?