February 4, 2019

How a Humanities Teacher Learned Data Science with Dataquest

Greg Iannarella is living proof that you don’t need a math, science, or programming background to learn data science.

“My my entire academic background is pretty strictly humanities,” he said. “No math or no science or anything like that. I always had an interest in those things. But you know how school works: if you’re not completely talented at something, sometimes you’re a bit dissuaded from it.”

So Greg focused on humanities in college, and then again in grad school. But while he was there, he came across a professor who was doing interesting linguistics work: counting how frequently particular words appeared in texts “and making arguments about how that seems to inform our larger cultural understandings of how literature works.”

This professor wasn’t doing any data science programming – in fact, Greg said, he was counting words by hand – but the idea of analyzing numerical data to get a better understanding of literature was interesting. It seemed to Greg like that analytical approach could be combined with a broader trend he had been reading about: digital humanities, using digital tools and analysis to address humanities questions.

Still, that alone wasn’t enough to get Greg to dive into data science. What finally pushed him over the edge? A bet.

On a trip with his brother, a computer scientist, the two got into a debate about which course of study was more difficult: Greg’s Masters in English or his brother’s Masters in Computer Science. They decided to resolve the debate with a wager: Greg would learn computer science, and his brother would learn English literature.

Learning with Dataquest

Although he figured learning data science programming made sense, Greg wasn’t sure exactly where to start. “I was just looking for free tools that I could just start accessing,” he said.

“Before I found [Dataquest], I went all the way through the free content of Codecademy,” he said. “I found Dataquest and I breezed through the free sections and free courses, [and I] tried a lot of your other competitors.”

“The thing that brought me back to Dataquest was your shell, working in your shell and having that side-by-side view. It felt very comfortable.”

Learning and applying, side by side on the Dataquest platform.

He also liked that the Dataquest platform explains why in addition to how. “I think that’s one thing that you guys focus on,” Greg said, “Here’s why we do this, and here’s how we take this next step. That helped. That helped a lot.”

“Not every service does that,” he added. “Sometimes they really throw you in the deep end. Then there you are. Swimming around.”

Greg started by working his way down our Python for Data Science path. But he didn’t stop there. He was also taking courses on other sites, and after taking a Bioinformatics class on EdX, he has started to dive into learning R with Dataquest, too.

Data Science for Everyone

While he was learning, Greg was also thinking about how he could apply his new data science knowledge to his work: teaching English and literature to freshmen at Seton Hall University.

“One thing that I was recognizing was that […] sometimes it’s about changing how you think about asking questions,” he said. “How do I then take that and apply it to teaching people how to write essays?”

What he came up with was a plan to incorporate more “digital humanities” into Seton Hall’s freshman English program.

He wasn’t asking freshmen English students to learn programming, of course, but he gave them access to a data analysis tool that allowed them to map and view data like citizens’ healthcare spending or education levels in a given location. Using the tool, the students could do their own data analysis and then write essays reflecting on what they found.

The project was a “blaring success”, Greg said, and digital humanities is catching on. He’ll be presenting his results this spring to try to encourage more similar work. He’s also found a group of other humanities academics interested in working more with data. “It’s guys from the English department, Religion, and History, and we’re all learning R and talking about how we can use R to engage with our study. It’s cool,” he said. “I pushed a lot of people towards Dataquest as a way to get their feet wet.”

This synthesis of programming, science, math, and humanities feels natural to Greg. “I always felt this kind of weird feeling that humanities and science were split to begin with. They seem like things that compliment each other and complete each other. Watson and Crick’s essays and writings on the double helix are all incredibly poetic. And the Romantics and the Victorians in England, they all explore science.”

He also sees embracing data and technology as the next obvious step forward in the study of the humanities. “I think a lot of the work in the humanities that can be done without technology has been done already,” he said. “I think that the future is looking at ways in which we can use technology.”

Finding success

Studying data science isn’t always an easy road. One of Greg’s biggest struggles was getting things set up locally so that he could write code on his own computer. “I could do things in the shell on Dataquest but I couldn’t get my computer to do the stuff. I was ready to throw my computer out the window at that point, just give it all up entirely. But you have to take a deep breath and recognize that the answers are out there somewhere. You just have to be willing to research and be patient.”

You also need to recognize that it’s going to be a long journey. “When did I go on that trip with my brother? It must’ve been two years ago. It’ll take you a really long time.”

“You just have to stay consistent and not get discouraged,” he said.

If you want to make fast progress, Greg recommends focusing on exposure and immersing yourself in what you’re learning. “One thing that helped was when I wasn’t able to be in front of my computer I would download free PDF books on my phone,” Greg said. “Just try and get fluent in the language and continue to expose yourself to it.”

And as for that bet about computer science versus English?

“[My brother] still reads interesting and good pieces of literature, but I’m certainly not anywhere near a Masters in computer science or data science,” Greg said. “But I keep working towards it.”

“So yeah. I guess we’re probably about even.”

You can learn data science too, no matter what your academic background is. Click here to get started for free in Python or R.

Charlie Custer

About the author

Charlie Custer

Charlie is a student of data science, and also a content marketer at Dataquest. In his free time, he's learning to mountain bike and making videos about it.

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