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Educator Professional Development Onboarding to the Data Science Learning Progressions

Hannah Kurzweil

Section Building Your Confidence: You’re Already a Data Science Teacher

Many teachers feel intimidated by data science because they think it requires advanced mathematical or technical knowledge. The truth is, if you’re helping students think critically about information, ask questions, and communicate their findings, you’re already teaching data science and now you’re just here to hone that skill and be more purposeful with your intent.
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Checkpoint 9. Recognizing Data Science in Your Teaching.

Let’s identify the data science thinking that’s already happening in common classroom activities.
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(a)

Consider this activity: “Students sort objects by color, size, or shape.” What data science skills does this develop?
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Answer.
Categorizing and organizing information - foundational data skills
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(b)

What about: “Students create a bar graph showing class favorite foods?”
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Answer.
Data visualization and communication skills
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(c)

And: “Students analyze different perspectives on a historical event using multiple sources?”
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Answer.
Critical evaluation of evidence and bias recognition
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Each of these activities involves data science thinking! The progression shows how concepts naturally build from basic categorization to sophisticated analysis.
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Let’s address some concerns teachers often have about data science education:
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Checkpoint 10.

A teacher says: “I don’t have time to add another subject to my curriculum.” How would you respond to this concern?
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Solution.
Data science is a way of thinking that makes existing curriculum richer and more engaging. Instead of adding content, it adds depth to current lessons. A math lesson about graphing becomes more meaningful when students graph data they collected about something they care about. A social studies lesson about community becomes more engaging when students analyze real data about their own community.
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Checkpoint 11.

Another teacher worries: “I’m not strong in math, so I can’t teach data science.” What’s the most accurate response?
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Solution.
While math becomes a larger player at higher levels, the foundation of data science is about thinking clearly about information. The most important data science skills are actually curiosity, critical thinking, and communication. Many crucial data science concepts don’t require advanced math: questioning data sources, recognizing patterns, organizing information, and communicating findings clearly. These are skills that teachers use every day. If you are hoping to tackle a larger data science project in the upper grades, consider partnering with other subjects to make it even more robust and meaningful for your students!
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The key insight: Data science education is about developing ways of thinking that transcend any single subject or mathematical skill level. You’re preparing students to be thoughtful consumers and creators of information—a skill they’ll need regardless of their career path.
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