Mathematics Q&A

With Data Science in Demand, This State Is Infusing Instruction With It

By Lauraine Langreo — September 19, 2023 4 min read
Teacher giving a lecture in a data classroom to a group of students
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Data science skills are becoming increasingly relevant and important for all jobs, according to experts.

To get students ready for those careers, more states are adding data science programs to the K-12 curriculum, according to the national initiative Data Science for Everyone.

Virginia is one of those states. In April 2022, its Board of Education approved the Data Science Standards for Learning for a high school data science course. In August 2023, Virginia also updated its mathematics standards of learning to infuse more data analysis lessons throughout the K-12 curriculum.

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Deb Crawford, the mathematics supervisor for Frederick County Public Schools in Virginia and one of the leads for the state’s data science course development and pilot team, talked to Education Week about the importance of data science education and how the state is putting the data science standards to work.

The interview was edited for brevity and clarity.

What will students learn in the data science course?

Deb Crawford

This course lives at the intersection of mathematics and statistics, computer science, and then the domain of the dataset, so business and industry. This aligns really well with our Blueprint Virginia [a business-led initiative that provides direction and long-term economic development planning for the state] in terms of preparing students for in-demand careers.

The entire course is organized around the data cycle, where kids are doing exploratory data analysis of a big dataset, and they formulate their own questions. They have to clean the data, they have to make decisions about the formatting of the data. Students will represent the data with data visualizations, model, and do data analytics on the dataset in terms of forming predictions, and then communicate their findings and their predictions for decisionmaking.

What are some challenges that the pilot schools are facing in making the curriculum work?

The first one is with the ed-tech tools. The problem is many of those tools are blocked on school networks, and it’s been arduous to try to make sure that kids have the tools that they need in data science that are the same tools that are used in the workplace. Tableau, for example, is the No. 1 data visualization tool in the workplace for data science, and we can’t access it due to data privacy concerns, which are definitely important. But can we not develop a sandbox for students to be able to use those tools used in business and industry in a safe environment?

A secondary challenge is that data science typically comes after Algebra II as a fourth math credit. Post-Algebra II, there are lots of courses that students can choose, and often they may not have the time in their schedules to take all of them.

What professional development opportunities did educators receive for this course?

We have a three-day data science professional learning event, where teachers meet, and they stay overnight so they can work during the day and network in the evenings in order to create relationships to be in a cohort. They’re immersed in learning about the standards of learning for data science: What is the content? What are some of the tools that you can use to teach this content?

After that, cohorts meet over the summer to continue professional learning based on their needs—our teachers are telling us what they want to learn more about.

We also have master teachers from year one sharing how they design their units and sharing some of the projects. Teachers were able to immerse themselves in the work of the students and see exactly where the students were in the beginning of data science in unit one, and then where they were with their culminating projects and see that growth.

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How did the state partner with higher education institutions and businesses?

We had six different universities represented on our course development team. We had different universities look at our standards of learning before they were published and give us feedback. We even had a mini-summit that spring when our standards were up for approval, and it brought together higher ed, business and industry all from the commonwealth and K-12 administrators and supervisors who wanted to learn more about data science. The business and industry partners actually gave commitments to us in terms of how they would support our data science teachers.

What’s your advice to other states or districts that want to do this?

At the state level, my advice is really simple. Make sure you have a diverse committee writing the standards. I would include parents, as well. We did not and I think that the one thing I would have changed is inviting parents to be a part of it. We did later in the process in terms of public feedback, but I would do it right from the start.

[Make sure you’re also] involving business and industry right from the start to align the skill set of the standards to the skill set required of the workforce, and we think we’ve done that. But being really intentional in the beginning to do that would be good advice.

In terms of schools implementing data science, a team approach would work. So if the math department wants to run data science, there might be just one teacher who’s interested in it—that’s not as effective as if the whole school gets behind it. Having a team at the school look at what data science is and what it offers students and then advertise it heavily as a team—not just marketing it as yet another math course—I think is important because kids need to know what’s out there.

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