Section Introduction to the Data Science Learning Progressions
If you’re a math teacher, you’re probably already familiar with how the Common Core State Standards describe mathematical progressions—showing how students develop understanding of concepts like number sense or algebraic thinking over multiple grade levels. Science teachers know this approach through the Next Generation Science Standards (NGSS), which map how scientific thinking develops from kindergarten through high school. Social studies educators have seen similar frameworks in the C3 Framework, which outlines how civic reasoning and inquiry skills build over time.
The data science learning progressions work exactly the same way, but with one powerful difference: they’re designed to span across all subject areas. Just as the Common Core shows how mathematical reasoning develops, or NGSS shows how scientific thinking evolves, these data science progressions map out how students develop the ability to ask questions about data, think critically about evidence, and communicate findings—skills that are valuable whether students are analyzing historical census data, conducting science experiments, interpreting literature, or solving mathematical problems.
Learning progressions are research-based frameworks that map out how students typically develop understanding and skills in a particular area over time. Unlike traditional scope-and-sequence documents that simply list what to teach when, learning progressions describe how student thinking actually evolves. They capture the messy, non-linear reality of how children learn—showing that students don’t simply move from "not knowing" to "knowing," but rather develop increasingly sophisticated ways of thinking about concepts over months and years.
What makes the data science progressions particularly powerful for your implementation is that they focus on thinking skills rather than specific tools or procedures. Instead of prescribing that students must use particular software or memorize statistical formulas, they describe the reasoning patterns that make someone an effective data scientist: asking good questions, thinking critically about evidence, recognizing bias, communicating findings clearly, and using data responsibly. A second-grader sorting objects by color and a tenth-grader analyzing climate data are both developing the same fundamental data science concepts—just at different levels of complexity.
As you begin implementing data science education, these progressions serve as both a planning tool and an assessment guide—just like the standards frameworks you already use. They help you understand what data science thinking looks like at your grade level, identify natural connections to your existing curriculum, and recognize growth in your students even when it doesn’t look like traditional academic progress. Most importantly, they give you confidence that you’re building genuine data science capabilities in your students, not just teaching them to follow procedures or use specific tools.
The progressions also provide a common language for talking about data science education with colleagues, administrators, and parents. When someone asks what students are actually learning in data science, you can point to specific thinking skills and show how they connect to other academic areas and real-world applications—just as you might reference Common Core mathematical practices or NGSS science and engineering practices.
Remember, these progressions are meant to support your professional judgment, not replace it. They provide a framework for understanding student development, but you know your students best and can adapt the progressions to meet their specific needs and interests.
The data science learning progressions you’ll be working with are organized into five interconnected strands. You can explore the complete progressions at
the learning progressions website.
1. Data Dispositions and Responsibilities: Developing curiosity, ethics, and critical thinking about data
2. Creation and Curation: Collecting, cleaning, and organizing data for analysis
3. Analysis and Modeling: Making sense of data through various techniques and tools
4. Interpreting Problems and Results: Drawing conclusions and making claims based on evidence
5. Visualization and Communication: Sharing data stories effectively with different audiences
These strands spiral through grade levels, building sophistication over time. A kindergartener sorting objects by color is developing the same fundamental thinking as a high school student analyzing complex datasets—just at different levels of complexity.
Checkpoint 4.
What’s the most important thing to understand about how the five data science strands work together?
Hint.
Think about whether these strands should be taught separately or together, and how they build over time.
Solution.
The five strands work together in every data science activity. When students do a simple class survey, they’re practicing curiosity and ethics (Strand 1), collecting and organizing information (Strand 2), making sense of patterns (Strand 3), drawing conclusions (Strand 4), and sharing results (Strand 5). The complexity increases over time, but even kindergarteners can engage with all five strands in age-appropriate ways.
This resource adapts to where you are in your teaching journey. Consider which description best fits your primary teaching context:
Elementary (K-5): I work with younger students who learn best through hands-on activities, concrete examples, and visual representations.
Secondary (6-12): I work with older students who can handle more abstract concepts and sophisticated tools like spreadsheets or coding platforms.
Mixed/Other: I work across grade levels, in specialized settings, or want to see examples from multiple levels.
Checkpoint 6.
Based on your teaching context, what type of data science activities do you think would work best with your students? Consider their developmental level, attention span, and interests.
Throughout this resource, you’ll see examples tailored to different contexts. Elementary teachers will see examples using manipulatives, simple surveys, and picture graphs. Secondary teachers will see examples with spreadsheet tools, data visualization software, and introductory coding. You can always explore examples from other grade bands—data science thinking is remarkably adaptable!
Let’s end this chapter with something you can try in your classroom this week. Here’s a simple 15-minute activity that works in any subject and any grade level:
Exploration 1. The Quick Class Survey.
Materials: Paper/whiteboard or digital tool of your choice
1. Ask your students a question related to your current topic. Examples: Math: “How many pets do you have?” Science: “What’s your favorite weather?” Social Studies: “How do you get to school?” ELA: “What’s your favorite book genre?”
2. Collect responses quickly (show of hands, exit ticket, etc.)
3. Create a simple visual representation together (tally marks, bar graph, etc.)
4. Ask: “What do you notice? What surprises you? What questions does this raise?”
5. Connect back to your lesson: “How might this information be useful for...?”
Why this works: You’ve just engaged all five data science strands in 15 minutes! Students practiced questioning (Data Dispositions), collecting information (Creation and Curation), organizing it (Analysis), interpreting results (Interpreting Problems and Results), and communicating findings (Visualization and Communication).
Watch this 4-minute example of a teacher implementing a quick class survey with 4th graders.
Checkpoint 7.
What do you think is the most important part of the Quick Class Survey for developing data science thinking?
Hint.
Consider which step helps students move beyond just collecting data to actually thinking about what it means.
Solution.
The follow-up questions like “What do you notice?” and “What surprises you?” are crucial because they develop investigative thinking skills—the heart of data science. While technology and sample size can be important, this activity is about developing thinking habits. The questions help students move from passive data collection to active investigation and analysis.
