More data is available now compared to any other time in human history, and data touches nearly every aspect of our society. IBM estimates that people create 2.5 million terabytes of data each day – that would fill more than 135,000 of the largest hard drives available – and only a fraction of that data is analyzed and used.

The College of Human Ecology is working to address this knowledge gap with its Data Science and Programming Curriculum Initiative, an effort launched in 2019 to teach Human Ecology students how to use data and technology in their respective disciplines. To date, the initiative includes three classes that teach programming and data application in apparel design, molecular nutrition and public policy.

“Data science and programming are tools that are critical to many fields,” said Rachel Dunifon, the Rebecca Q. and James C. Morgan Dean of the College of Human Ecology. “My goal was to give students the chance to develop skills in data science and programming, and see how they can be brought to bear in our multidisciplinary, applied, problem-focused curriculum in Human Ecology.”

While the three courses are vastly different in their focus and scope, they share a common element: they teach programming and data analysis to students who don’t typically learn to apply these skills in their chosen fields.

“My class addresses a problem that I found in my own education,” explained Nathaniel Vacanti, an assistant professor of nutrition who teaches Proteins, Transcripts and Metabolism: Big Data in Molecular Nutrition. “Those classically trained in molecular nutrition don’t know how to access these data sets. And even if they can access them, they don’t know how to use them. We’re trying to answer the question, how do you get past the noise of all of this data to find something you can use?”

Huiju Park, associate professor of fiber science and apparel design, experienced similar struggles when trying to apply technology in his field. To create his new class, Smart Clothing: Design and Programming, he worked independently to learn the electrical engineering and programming language he taught the students.

“There is a huge gap between what is possible and what students have been able to create,” he said. “Engineers know the technical components, but they don't know how to incorporate technical components into soft material covering the body. I wanted to create a bridge to help students incorporate technology in their designs because to commercialize these ideas and make the ideas work in real life, the designer needs to understand both sides. This new initiative makes that possible.”

“A crash course” in how data can inform policy decisions

The three classes that are part of the initiative are truly introductory classes; students don’t need any background in math, statistics or computer science before enrolling.

Matt Hall, associate professor of policy analysis and management, co-teaches the course Big Data for Big Policy Problems with PAM Professor Maria Fitzpatrick.

“We teach them how to manipulate and explain data, how to analyze relationships and how to communicate what they find,” he said. “It’s a crash course in the economic and sociological approaches to public policy and the innovative computational tools being used to evaluate it.”

“The class is ambitious in that we are trying to cover substantive policy topics on pressing social issues – income inequality, crime and policing, and the pandemic – and use them as way to teach students how to use some of the basic tools of data science and big data.”

Students in the class are learning R, a programming language and free software environment for statistical computing and graphics. The class is taught virtually with an interactive framework that allows students to work at their own pace, but also participate in discussion groups with other students and teachers. “It allows students to use these tools on their own time, from anywhere in the world.”

This spring, the class was also offered virtually to high school students across the nation as part of the National Education Equity Lab, a non-profit that offers online college courses to high school students in underserved communities.

Merging technology and programming

Park’s class – Smart Clothing: Design and Programming – also provides an introduction, this time to electrical engineering and programming.

Students spend the first half of the class learning the technical skills to build technology into clothing and the second half designing their own piece of wearable technology. Final projects from the class included a winter hat with a pompom that changes color based on the temperature, a “portable piano” glove that plays musical notes when pressing on each finger and a figure skating dress that lights up to the sound of music.

The key, Park said, is to teach students to consider human factors when creating wearable technology.

“Students must learn how to include these technology components as part of the flexible material covering our bodies, so the garment is comfortable,” he said. “Material choice is important. You want to use a soft conductive material and soft switches to control the unit.”

The first session of the class, taught in the fall of 2020, provided students a mix of virtual and in-person learning. Students learned programming and design fundamentals virtually, then were able to spend time in the lab, while following social distancing protocols, to learn the hands-on aspects of constructing the garments with electrical components.

“I’m optimistic about the future of this course,” Park said. “There is tremendous opportunity to provide students with the skills they need to make innovative strides in apparel design.”

Using big data to understand the human body

The third class in the initiative – Proteins, Transcripts and Metabolism: Big Data in Molecular Nutrition – teaches students to use programming languages and techniques to analyze large data sets about gene expression.

“The idea is to give students who have not had prior programming experience an entryway to look at these big molecular data sets and address their own research questions,” said Nathaniel Vacanti, assistant professor of nutritional sciences, who teaches the class.

Vacanti started studying these large data sets in his own research as a postdoctoral fellow at the Karolinska Institute in Stockholm, Sweden. Even with an engineering background, he said he struggled to overcome the barriers to analyze big data sets.

“That experience really gave me the inspiration for this kind of class,” he said. “I was trained as an engineer and had strong quantitative skills. If I was having difficulty, I couldn’t imagine what it would be like for people who haven’t been trained in these disciplines. My goal is to make the entryway to performing these analyses easier.”

A major priority for Vacanti is teaching the students how to access and analyze these data sets on their own personal computers. “I want them to understand that they own a machine that is plenty powerful to perform these analyses,” he said. “They can download the data sets and begin addressing research questions on their own.”

For all three classes, the broader concept is to use the reams of data available to apply their knowledge in a broader way. “These classes really speak to the merging of fields,” Vacanti said. “A lot of people are recognizing that modern researchers are applying similar analytical tools across disciplines.”

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