Admiration to Ambition: My Journey to Data

Note: I drafted this prompt and initial paragraph of this post on August 2024.

I have always been obsessed with innumerable subjects. From history, to science, art and beyond, I have always seen myself as being a multi-faceted in the span of my interests. I could never just “stick” to one thing, and I can never force myself to act as if doing so would actually leave me fulfilled. However, what I can do is allow myself to experiment, to explore, and to continue to create.

Now, from where I left off

As of August 2024, I had been using Coursera for over six months, on and off, and the courses that kept catching my attention were related to data analysis and data science. In fact, I also subscribed to DataCamp, Codecademy, and Brilliant.org. Though these are a lot of subscriptions (and I will not be renewing my memberships to many of them), I have found them to be immeasurably valuable resources for learning and small-scale implementations of what I have already learned on Coursera and, subsequently, in my MS in Data Science program.

I have always loved data, synthesizing it, visualizing it, and analyzing it. In fact, I often find myself realizing how much time I have spent on reading academic journal articles, news articles, and pouring through datasets – all to ensure that I have the proper context to be able to understand what’s going on with a data set, and why said data appears the way that it does.

My curiosity inherently predisposes me to want to understand the root causes of any issue that interests me. It is insatiable and has led me to take up numerous interests and has resulted in why my perspective happens to be the way that it is.

This may be surprising to some as my background was originally in art, design, and political science (I literally minored in Art, Business, and Political Science for undergrad), but data science is a surprisingly useful, and versatile, field with ramifications in each of these fields that I explored during my years as an undergraduate student.

Why Data?

And this is exactly why I admire data scientists and have begun my journey towards becoming one.

Data gives us the ability to better understand the world around us, it is the foundation for all information, so understanding how to put it towards the best use cases is an imperative in this century. Every field, from art, to politics, and business, has become data-driven.

But that is also an issue, right?

Everything revolving around data means less emphasis on what makes each of these fields as special as they are. Being data-driven has become the goal, rather than the means towards the end goal of advancing your field.

Data is being utilized as the means by which industries die, whether it be through AI-driven automation or a decreasing emphasis on what appears to be the “least profitable” portions of a business until downsizing inevitably leaves a company a shell of its former self.

Why I want to learn data science.

This is another reason for why I wanted to study data science, because I know who I am as a person, as a human being.

I am not a person who would stand idly by and oversee the automation of something that can be better, and more professionally done, by a human being with the proper skills, background and knowledge. There are reasons for why automation doesn’t work for everything, and those are the fact that automation must account for the fact that efficiency isn’t always what’s best.

The most efficient code may not lead to the most readable and usable solutions for a website’s functionality.

The most efficient use of color is not always the most visually appealing, nor is it always the best way to convey an artist’s perspective.

Efficiency and data have their place, but they must also be used hand-in-hand with domain knowledge and make room for creativity, the type of creativity and imagination that have allowed engineers, artists, scientists, and the like to innovate in the ways that we have over the past decade, alone. So much of what’s normal to us today was unimaginable yesterday.

If we simply resort to data as a crutch for our imagination, we inevitably kill it.

But, if we use data as a way to reference past ideas and unite them with today’s sensibilities and possibilities, we can enable imagination, creation, and innovation.

So, simply put, though I admire data scientists and data analysts, I want to be a creative data scientist, a creative data analyst.

I want to function as someone who can, not only simply analyze and synthesize data, I also want to be someone who can unite data with creativity to help in the goal of building a truly better world that accounts, includes, and benefits all of us.

Data should not be a tool used by the wealthiest to further exploit the masses and promote elitism, it is an imperative that data (within reason) become knowledge available to the masses for the sake of transparency.