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Zia Qi

  • Pathway:
    Program Evaluation and Applied Research
  • Field Placement:
    U-M School of Social Work, Child and Adolescent Data Lab

MSW student Zia Qi has taken a deep dive into the world of data visualization. “She is the first student I know of to take this exam and be certified as a Tableau Desktop specialist,” says Professor Brian Perron of Qi. Tableau Desktop is today’s most popular data visualization software. 

“Data literacy is very important for social workers,” says Perron. “We generate so much data, we must be able to make value of it. You want to make a product that you can publish and that end users can interact with. Even if a social worker is running a therapy practice, that involves a lot of data.”

As an undergraduate at the Central Academy of Fine Arts in Beijing, where she grew up, Qi majored in art history so she was used to thinking visually. She also was drawn to social justice work and helping others.

“I myself had a traumatic childhood,” Qi recalls. “Then, at the Central Academy, I learned about what in China we call ‘left-behind children.’” Parents seek work in big cities while their children remain behind in rural areas, often cared for by extended family members. Complex policies prevent these children from registering in public schools. They attend poor schools instead, without even basic equipment.

When Qi learned about the left-behind children, she saw a way to integrate the art she was studying with another interest: social justice. She founded a project to provide children with free art education. She won a pitch competition for funds to launch the project at a school in the Tongzhou District of Beijing.

This program lasted almost a year before COVID intervened. At that point, much of the world jumped right on Zoom, but not Qi’s program. “These children typically don't have computers or cell phones,” she says. “We could not go online, so everything shut down.” Qi was now due to graduate, and she knew that the next step for her would be a graduate degree in social work. “The social welfare system in China is not good,” she says. “I didn’t know what I could learn there, so I came to the United States—to the top social work school!” Qi arrived in Ann Arbor in August 2021.

“I was interested in social justice,” she says, “but I had not yet found the best fit for my skills and interests. Among other things, I thought I would take a data course.” In the data visualization course taught by Perron, Qi found her talent.

In the first class Qi and her classmates were asked to make a collection of meaningful items from their everyday lives. This helped to emphasize that data describes real-world objects and occurrences. Students included the size and weight of each object, again connecting numbers to things meaningful to them. “Once I did that exercise,” Qi says, “I started to look at things differently, intentionally focusing on their quantitative aspects.”

Qi began using Tableau because of its power and its popularity. “I had ideas about how to tell data stories, to display storylines that were driven by data,” she says. “Tableau let me do that.” Currently, Qi is developing a dashboard to help the Michigan court system use data to understand the state’s child welfare system. “I want my work to inform the court in making their decisions,” she says, “and help the general public understand the Michigan foster care system.”

“Data visualization is about creating value from data,” Perron says. “The state has all this data, but we need to draw insights from it. Visualization tools, such as interactive dashboards, help the end user find what is most important in the data. Qi has pursued this very seriously: converting data into meaningful, actionable insights.”

What inspired Qi to take the extra step to be certified in Tableau? “Honestly, I thought it would help me find a better job!” she says. “And I like taking exams. I like challenging myself and testing my skills. That interests me. Not everybody likes tests, but I do!”

For the near future, Qi sees herself staying in the United States. “I would like to continue to work with messy data,” she says, referring to large data sets that may contain heterogeneous values, missing entries, and other errors. “You have a file with tons of numbers. It will have errors. You cannot just draw conclusions from it. It would not necessarily tell a story. You must clean it to create insights and values from it.” Qi also adds that, “because I plan to apply to a PhD program, working with a research team would be ideal. I joined the Child and Adolescent Data Lab and am doing my field placement there because they have a lot of researchers. I joined some research studies and I helped them analyze data, clean messy data sets and draw conclusions.”

Zia Qi sees the practical side of data analysis, but she seems driven by emotion as well. Perhaps she is remembering the left-behind children when she concludes, “I would like to make my work really meaningful and contribute to real world changes.”

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