The future of data science: Thoughts from Datapalooza 2023
What is data science?
Data science is a hot topic right now, but what does it mean and why should K-12 educators be paying attention to it? I went to Datapalooza 2023: The Future conference hosted by the UVA School of Data Science to learn more.Â
As a K-12 computer science (CS) educator, I have considered data science to be part of CS, but it has also emerged as its own discipline. The UVA School of Data Science defines data science as the study of data and the methods used to learn from data. They also focus heavily on the interdisciplinary nature of the discipline that crosses boundaries between CS, math, and business with applications across other disciplines such as humanities, social and behavioral sciences, medicine, and architecture.
Several presenters described data science as having the potential to bring disciplines together for a purpose. What purpose is that? To gain insights, make predictions, and support data-based decision making in almost any field you can imagine.
There is a lot of attention on data science in large part because of the release of the latest round of generative AI models. In fact, ChatGPT had this to share when asked about the relationship between data science and AI - "data science and AI are interconnected disciplines where data science provides the foundational tools and techniques for working with data, and AI leverages these capabilities to build intelligent systems capable of learning and making decisions."Â
The advancements in AI and data science are powerful and can have impacts, both positive and negative, on a massive scale. Mozilla has been considering the challenges of AI like how it exhibits bias, invades privacy, and reinforces existing power hierarchies and what trustworthy AI could look like, if it is designed to center privacy, transparency, and human well-being.
Why should K-12 educators be paying attention to data science?
I teach CS because I want students to become creators, and not just consumers of technology. The fascinating thing about the field of data science is that we are all participating in it as we generate vast amounts of data through social media use, fitness tracking, online learning tools, internet browsing, communication apps, gaming, device usage, content creation, location tracking and more. How much data are we generating? For a little perspective, in 2007 the digital universe was estimated to be a total of 281 billion gigabytes and the amount of digital information created had for the first time exceeded available capacity to permanently store it (Gantz et al., 2008). In 2023, it is estimated that 328.77 billion gigabytes of data are created every day (Statista).Â
Because we are living in this the age of data, the message from Datapalooza is that data literacy needs to be as foundational as math and science in K-12 schools. In Virginia, K-12 educators can find data science standards as part of the K-12 computer science strand, Data and Analysis. In addition, Virginia adopted secondary mathematics data science standards in 2022. At the conference I also met a high school math teacher who is part of the pilot of a data science course along with 75 teachers across Virginia.
One thing I really appreciated about the conference is how passionately folks from the UVA School of Data Science talk about data science education. A few key takeaways that resonated with me:
Teaching calculus is not going to change much from year to year, but technologies are changing so fast that the focus of data science education needs to be on teaching students how to teach themselves.Â
There can be a wide variability in student backgrounds related to data science, so it is important to create a healthy environment where students don’t feel like they need to compete with each other.
Leading with a project based on a real-world problem can provide the motivation students need to do what it takes to learn what they need to learn.
What is the future of data science?
I am hopeful for the future of data science education because it is so new. That has its own challenges for sure, but it also offers the opportunity to do things differently. There are persistent, pervasive, and problematic gender and racial gaps in CS education and technology fields (Cerf & Johnson, 2016; DuBow, W. & Gonzalez, 2020; Grover & Pea, 2013; Margolis et al., 2012). As the founder of Tech-Girls it often feels like we are applying bandages to a broken system. I’m excited that the vibe at Datapalooza was to acknowledge the challenges being faced and to lean into integration and the interdisciplinary nature of data science. You can learn more about the future of data science education in the UVA School of Data Science with Brian Wright, Director of the Undergraduate Program on the Once Upon a Tech podcast.
More resources:
Data Science: Telling a Story with Your Data (VPM/VDOE). This lesson extension provides a discussion guide, a hands-on activity about telling a story with your data, learning resources, and career resources to use in conjunction with the Computer Science Careers Across Virginia: Data Science video.
Data science vs. Computer science: What's the difference? (Rice University). A great resource to understand the college and career pathways in CS and data science.
Making Meaningful Connections: A K-8 CS Integration Guide (VDOE). A free, online Virtual Virginia course that includes a module on integration strategies and a module on the Data and Analysis from the computer science strand.
Internet Health Report 2022 (Mozilla). This report focuses on AI and shares findings through a series of podcasts and stories.