Earlier in the semester, the ASU Libraries Data Science and Analytics Unit promoted Kerri Rittschof to Director. She plans to improve the unit and make data science more accessible to everyone at ASU, regardless of what they learn.
Rittschof joined the unit as a program manager to facilitate student engagement. Now, as a director, that remains her primary goal.
“(I’m) really focused on making sure, engaging, and watching my students’ light bulbs go off … having a-ha moments with my students that they can use data science in any field. I have it,” she said.
Data science refers to various tools and techniques for analyzing large amounts of data, usually using coding, statistics, or mathematics. This is a tool that can be used by all researchers, even those new to computer science.
“[Data science]is here. Employers really care about it. Looking ahead, having that knowledge will be important,” says Rittschof. “I would like to emphasize that you don’t have to be a data scientist to use data science in your business. It’s about what story you want to tell. I have. What do you want to know from it?”
One of our current programs is the self-paced Canvas course.
“I recommend the Canvas course. Anyone can take this course because no programming is required,” says Rittschof.
Debra Riley-Huff, associate librarian for Engagement and Learning Services, agrees with the idea of making data science accessible.
According to Riley-Huff, the library will provide physical access to the data science and analytics unit, with an eye toward future careers as well as current academics.
“We do everything we can to help bridge the gap between traditional classrooms and online classrooms,” Riley Huff said.
While many of the unit’s resources are offered virtually, Hayden Library has a collaboration area on the third floor.
“We share this suite with our Makerspace students. It’s a really comfortable environment where we try not to scare the data,” says Riley-Huff.
One of the unit’s main programs is an open lab. Despite its name, it is not a lab, but a series of webinars and exercises offered each semester for all students to participate.
The final Open Lab webinar of the semester is titled “It’s Complicated – Using Machine Learning Techniques” by Shawn Walker and Allan Colbern, Assistant Professors in the Department of Social and Behavioral Sciences.
“The purpose of this talk is application (to show). Many talks are methodological or technology oriented. Do you?” said Walker.
Walker and Colburn have been collaborating on a machine learning project since 2021 that focuses on different aspects of how the news is discussing immigration and immigration.
“[Machine learning]makes us much more efficient and allows us to structure the qualitative work that we do … for example, we can look at news articles spanning 30 or 40 years. We can,” said Colbert.
According to Colburn, the duo has six working papers, one of which is about how the word “crisis” is used in news articles. Machine learning allowed them to look at about 100,000 news articles to understand the “crisis” and how the term is used to label immigrants and immigrants.
“The kinds of questions we can bring to the field will not only help organizations communicate with reporters, but they will also help change the narrative about immigration and immigration,” Colburn said.
Those interested in this research and how data science can be applied to the social sciences can join Walker and Colbern’s webinar on April 5th.
Associated with the unit, Walker also produced a podcast with former Data Science and Analytics Director Michael Simeone called Misinfo Weekly.
After the next talk, the lab is in transition and Litshoff is building a new team to further the unit’s goals.
“We are not only data scientists, but also digital humanities analysts, which allows us to intertwine our social science backgrounds in all areas,” Richchov said. I also want to get their ideas and their passions when I’m in. This is the organizational psychologist in me.What is our own ‘why’ and what are we I want to know what you want to do. “
Edited by Annie Graziano, Piper Hansen and Caera Learmonth.
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