This workshop empowers science educators to embed essential data literacy skills into their instruction. Recognizing that students often struggle with analyzing and interpreting data, the session addresses how to explicitly teach these skills through scientific practices. Participants explore how to help students move from basic data recognition to making meaningful scientific inferences using diverse data sets. The workshop emphasizes real-world application, interdisciplinary integration, and alignment with NGSS standards to ensure students are equipped for academic success and informed citizenship.

• Students’ difficulty in making sense of data and identifying trends or patterns.
  
  
• Inability to connect data to the broader lesson, unit, or real-world applications.
  
  
• Lack of foundational data skills such as understanding graphs, using tables, and differentiating correlation from causation.
  

1. Foundational Data Literacy and Visualization Skills
   Participants will learn how to teach students to understand the components of data displays, and how to select the appropriate graph types. Educators will also discover how to structure engaging activities like "Data Talks" to help students practice analyzing and interpreting visual data.
   
   
2. Strategies for Scaffolding Inference and Claim-Making
   Teachers will gain strategies for guiding students in making accurate scientific claims based on evidence. This includes differentiating between observations and inferences, understanding “inference space,” and extending claims into structured arguments using evidence and reasoning.
   
   
3. Integrating Data Collection and Interdisciplinary Applications
   Educators will explore how to support students in collecting their own data and interpreting professional data sets. Additionally, they will see how data literacy bridges science with disciplines like math and social studies.