Now, before we dive deeper, let’s see how data science thinking already connects to different subjects. Each subject area offers natural entry points for developing data skills and thinking habits.
This fits best in English Language Arts because students are analyzing literature and writing techniques. While math skills are involved in counting and comparing, the primary focus is on understanding how different authors use language. This shows how data science enhances rather than replaces traditional ELA instruction.
A class wants to use census data to understand how their community has changed over the past 20 years. What subject area provides the best context for this investigation?
Social Studies provides the best context because this investigation is fundamentally about understanding society and how communities change over time. While students will use math skills to analyze the data and might write about their findings, the core learning objectives align with social studies standards about demographics, community development, and historical change.
Data science thinking appears across all subject areas because it’s about enhancing what you’re already teaching with intentional attention to curiosity (asking questions that can be explored with information), critical thinking (questioning sources, recognizing bias, and evaluating claims), problem-solving (using data to understand and address real-world issues), and communication (sharing findings clearly and persuasively). The video below from the Alan Turing Institute features Melissa Terras where she discusses how humanities and data science can work together to address societal issues using examples from her work on handwriting recognition, 3D scanning, and multispectral imaging. While this is certainly beyond the scope of anyone just beginning their data science journey, it certainly highlights how data science can engage students to analyze and solve real world problems.
