4 Valuable Lessons I Learned as a Data Science Student
Charlie Custer is a marketer and marketing analyst with experience from everything from After Effect
I never really wanted to learn data science.
When I got hired at a startup that teaches data science, it was mostly for my writing skills. And having tried and failed to learn Python several times before, I had pretty much written off anything involving programming as not for me.
But what started as a brief foray onto our platform to understand the student experience quickly turned into a student experience of my own. Data science, it turned out, was more interesting, more accessible, and more useful to me than I ever would have thought.
Here are a few lessons I’ve learned along the way.
1) Learning a programming language is a bit like learning a language
Early into my college study of Mandarin, I remember asking a professor how long it had taken him to learn the language. He gave me a bemused look and said: “Well, it’s been about 40 years so far.” What he meant was that there’s really no endpoint when it comes to studying a language. There’s always more to learn, and spoken languages evolve over time.
The same, I’ve come to learn, is true of programming languages. There’s always more to learn. It’s unlikely I’ll ever reach a point where I can sit down and write any project I can conceive in Python without having to consult some documentation and learn something new.
Even if such an achievement were possible, programming languages evolve just like spoken languages. In ten years, it’s quite possible that the way I’ve learned to approach data work in Python now will be totally outdated.
To some, that might make learning a programming language seem daunting, but to me, the realization was actually quite freeing. The fact that there’s no real endpoint to reach means that I’m free to learn at my own pace, focusing on the things that are actually useful to me rather than trying to understand the entire body of knowledge and practice that is Python programming.
2) A little code can go a long way
Although I only started learning data science to get an understanding of how students experience our platform, what kept me learning beyond that was the fact that my rudimentary data skills were already yielding some practical utility.
For example, when I needed to pick a contest winner from a long list of emails, I was able to quickly write a couple of lines of Python to ensure the results were truly random. When I needed to filter down a survey with hundreds of respondents and dozens of questions to just a very specific subset of responses based on several different answers, I was able to do that quickly with a little basic Python and pandas.
In fact, although my studies have been focused primarily on working with data, I learned enough Python that I was also able to hack together a quick script that reminds me to stand up and do some quick exercises every hour — or at any other interval I choose — while I’m sitting at my desk for work.
Being able to build useful things for myself — that’s something I had assumed would take months or years of study, and require some kind of qualification like a certificate
or degree, but I realized I didn’t even need them. It turns out that even a little study of the basic fundamentals was enough for me to make my work a little easier and faster.
3) Building your own projects is critical
Finding a good platform for learning is really valuable, but one thing I learned pretty quickly is that it’s also important to strike out on your own.
There are two big reasons for this. The first is that working on your own projects helps you stay motivated. My first totally independent data project was an investigation of child trafficking in China based on a data set that I found and learned how to scrape and analyze. Building it was a struggle, but the fact that I was genuinely interested in the results helped me see it through to the end.
The second is that building a project on your own forces you to learn all sorts of new things. I spent hours Googling error messages and browsing Stack Overflow answers and documentation to try to figure out what I needed to do next, and how to make my code work. It wasn’t always fun, but I learned a ton from the process of building my first project, and the same has been true of every subsequent project I’ve undertaken.
4) Data science is for everyone
By this, I mean that everyone can benefit from learning some data science skills, not that everyone should try to become a data scientist.
I certainly have no interest in becoming a data scientist myself. But even as a marketer working on primarily creative endeavors, I have benefited quite a bit already from what I’ve learned in the field of data science.
As I’ve mentioned, my data skills have helped with the data-related aspects of my work. But my new data knowledge has also helped me to better understand the world. I certainly don’t know enough to work in AI, but since I started studying data science, I’ve developed a much better understanding of what “AI” is out there and how it really works. I have an easier time reading charts, and spotting the ones that are misleading. When I read a news article and get curious, I can sometimes dig up a data set and do some investigation of my own.
I still have tons left to learn, but I’ve learned enough to say confidently that I think most people could benefit from picking up some data science skills — even if, like me, you don’t have much interest in dedicating your life to data.
Big data is playing an increasingly important role in the world, and the more I learn, the better I feel about my own ability to keep up.
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