Five basic concepts for teachers new to data science

Looking at comprehensive data science standards can feel overwhelming. Pages of concepts, technical terms, and grade-level progressions might make you think you need to completely revolutionize your teaching. But here's the secret: the core of data science is asking good questions and thinking critically about information—skills you're already fostering in your students every day.

Let's break down five essential data science concepts that form the foundation of everything else. Even better? We'll show you how you're likely already incorporating these into your classroom and how a little more focus can help you equip students to thrive in today’s data-driven world.

1. Ask Questions That Data Can Answer

What it means: Help students move from "I wonder..." to "I wonder, and here's how we could find out..."

You're already doing this when you:

• Guide students to turn observations into testable questions during science experiments
• Help them research topics by asking "What would we need to know to answer that?"
• Encourage them to be specific: instead of "Are dogs better pets?" ask "Do dogs or cats require more daily care time?"

Simple next step: When students ask questions, add one follow-up: "What kind of information would help us answer that?" You've just introduced the concept of identifying data needs.

2. Organize and Describe What You See

What it means: Taking messy, real-world information and organizing it so patterns become visible.

You're already doing this when you:

• Create class graphs of favorite pizza toppings or eye colors
• Help students organize research findings into charts or lists
• Use tally marks to count anything (raised hands, books read, rainy days)
• Sort objects by attributes in math or science

Simple next step: After organizing information, ask "What do you notice?" and "What surprises you?" Congratulations—you're now facilitating data analysis!

3. Use "Uncertainty Language" Naturally

What it means: Teaching students that conclusions from data aren't absolute—they're about likelihood, trends, and "most of the time" rather than "always."

You're already doing this when you:

• Use words like "most," "many," "few," "rarely," "often," "sometimes"
• Discuss weather predictions ("probably sunny," "might rain")
• Talk about survey results ("most students prefer...")
• Help students avoid absolute statements like "all" or "never"

Simple next step: When students make claims, ask them to add a "confidence word." Instead of "Boys are taller," try "Boys are often taller" or "In our class, boys tend to be taller."

4. Notice Patterns and Make Fair Comparisons

What it means: Looking for relationships, trends, and differences while being thoughtful about what makes a fair comparison.

You're already doing this when you:

• Help students compare characters across different books
• Discuss changes over time (growth charts, reading levels, historical timelines)
• Point out patterns in math (even/odd numbers, multiplication tables)
• Compare different groups fairly ("Let's make sure we're comparing classes of the same size")

Simple next step: When students notice differences, ask "Is this a fair comparison?" and "What else might explain this difference?" You're building critical thinking about correlation vs.causation.

5. Tell Stories That Matter

What it means: Using data to communicate clearly and persuade others, while being honest about limitations.

You're already doing this when you:

• Help students support arguments with evidence in writing
• Create presentations about research projects
• Discuss why graphs need titles and labels
• Talk about considering your audience when sharing information

Simple next step: When students present findings, ask "Who would care about this information?" and "What would they need to know to understand it?" You're teaching data communication and audience awareness.

The Beautiful Truth: It's All Connected

Here's what makes these concepts so powerful: they build on each other naturally. When students ask good questions, they need to organize information to answer them. When they organize information, they start noticing patterns. When they see patterns, they want to share them with others. And throughout it all, they're learning to think critically about uncertainty and fairness.

You don't need to overhaul your curriculum or become a statistics expert overnight. Start with one concept that feels most natural to your current teaching, and let student curiosity guide you to the others.

Your Students Are Ready

The best part? Students are naturally curious about data. They want to know which Pokémon is most popular, whether their school has more girls or boys, and if pizza is really everyone's favorite food. Their questions are your starting point. Your job isn't to become a data scientist—it's to help them explore their questions thoughtfully and systematically. Every time you help a student move from "I think..." to "I think, and here's why the evidence suggests..." you're teaching data science. Every time you help them organize their thinking, question their assumptions, or communicate clearly with others, you're building the foundation for every advanced data concept they'll ever learn.

The revolution isn't in your lesson plans—it's in recognizing the data science that's already happening in your classroom every day.

Want to Take This a Step Further? Try These in Your Classroom Today

Ready to make these concepts more explicit in your teaching? Here are simple, grade-appropriate activities you can try this week—no special materials or prep required.

1. Ask Questions That Data Can Answer

• K-2: Have students vote on a class question like "What's our favorite playground activity?" Then ask: "How could we find out if other classes feel the same way?" Help them think about who they'd ask and how.
• 3-5: When students wonder about something (like "Why do some plants grow faster?"), help them turn it into a measurable question: "Do plants with more sunlight grow taller in two weeks than plants with less sunlight?"
• 6-8: Give students a broad topic they care about (video games, sports, music) and challenge them to write three different questions: one they could answer by counting, one by comparing groups, and one that would require collecting information over time.
• 9-10: Present students with a local issue (school lunch satisfaction, parking problems, etc.) and have them identify what data would be needed to understand different perspectives on the issue.
• 11-12: Have students find a news article making a claim and identify what additional data would be needed to verify, challenge, or strengthen that claim.

2. Organize and Describe What You See

• K-2: Use physical objects (blocks, crayons, students themselves) to sort and count by attributes. Create simple pictographs with stickers or drawings where each picture represents one student's choice.
• 3-5: Collect classroom data (birthdays, number of siblings, favorite subjects) and have students organize it into different types of charts—bar graphs, line plots, or simple tables—then compare what each shows.
• 6-8: Give students messy data (like a list of student responses mixing numbers and text) and have them clean and organize it into a proper data table, explaining their decisions about categories.
• 9-10: Have students collect data from multiple sources about the same topic (school website, student survey, observation) and organize it into a format that allows comparison across sources.
• 11-12: Challenge students to take complex, real-world data (census information, economic indicators, research study results) and create organized summaries that highlight key patterns for a specific audience.

3. Use "Uncertainty Language" Naturally

• K-2: When reading stories or discussing observations, practice using words like "usually," "sometimes," "rarely," and "most." Make it a game: "Can you tell me about recess using an 'uncertainty word'?"
• 3-5: After any classroom survey or data collection, have students practice making statements with confidence words: "Most students in our class..." or "It's likely that..." instead of absolute statements.
• 6-8: When students make predictions (in science experiments, historical outcomes, math problem solving), have them rate their confidence on a scale and explain their reasoning.
• 9-10: Introduce basic probability language by having students estimate likelihood in real situations: "What's the chance it rains tomorrow?" connecting to weather forecasts and their uncertainty.
• 11-12: When analyzing any data or research, require students to include uncertainty statements: discussing margins of error, sample limitations, or alternative explanations for their findings.

4. Notice Patterns and Make Fair Comparisons

• K-2: During calendar time or daily observations, help students notice patterns: "What do you notice about the weather this week?" Introduce the idea that we need to compare similar things (comparing apples to apples).
• 3-5: When students notice differences between groups (boys vs. girls, different classes, etc.), ask: "Is this a fair comparison? Do the groups have the same number of people? What else might explain this difference?"
• 6-8: Give students simple data sets and have them identify relationships, then discuss whether correlation means causation: "Ice cream sales and drowning incidents both increase in summer—does ice cream cause drowning?"
• 9-10: Have students analyze claims in media or advertising, identifying what comparisons are being made and whether they're fair and meaningful.
• 11-12: Challenge students to find examples of Simpson's Paradox or misleading statistics in real research or news, then explain why the comparison might be problematic.

5. Tell Stories That Matter

• K-2: After collecting any class data, have students "tell the story" of their graph to a partner: "This shows that most of our class likes..." Practice adding titles and labels that help others understand.
• 3-5: Have students create simple data presentations for younger students or parents, focusing on making their findings clear and interesting to their specific audience.
• 6-8: Challenge students to present the same data findings to two different audiences (peers vs. teachers vs. parents) and explain how they'd change their presentation for each group.
• 9-10: Have students create data-driven arguments about school or community issues, requiring them to acknowledge limitations and potential counterarguments in their presentations.
• 11-12: Ask students to write op-ed style pieces using data to support their arguments, including discussion of their methodology, limitations, and broader implications.

Remember: Start with just one activity that feels most comfortable for your current curriculum. Once students get excited about the questions they're exploring, they'll naturally want to dive deeper into the other concepts.

Ready to dive deeper? Explore our complete learning progressions to see how these foundational concepts develop into sophisticated data science skills across all grade levels.

We’ve put together an onboarding plan for new teachers looking to explore data science. Start learning here: https://hkurzweil.github.io/ds4e-teacher-pd