# > What does a data education entail?

Our lives have become surrounded by data—every second of every day, the world creates enough data to fill 50 new Libraries of Congress. We must work now to ensure that tomorrow’s citizens are prepared to navigate a world awash in data.

Students who are data-literate must be able to read, work with, analyze, and argue with data. They must be competent and comfortable reading, understanding, and synthesizing information displayed in the many forms data may take (graphically, in a data table, written words, etc.).

How do we teach data literacy for all?

Achieving widespread data literacy for all requires data education from the earliest years onward. Early in their math education, children are already dealing with graphs and numbers. Yet more schools ought to be taking the next step, teaching children how this information can actually represent data. From elementary to high school, students should be developing flexible and conceptual understanding of what data is, where it comes from, and what it can be used for.

Everyone, regardless of socioeconomic status, racial background, or local resources, must have access to this type of education. All students will use these skills throughout their lives—having them will make or break a student's success in the modern world. Technological literacy, causal inference, and statistical reasoning will be the new foundations for understanding the processes that comprise our world, and making better predictions about it. These skills serve as toolkits for being watchful consumers, discerning citizens, and productive workers.

What if I don't want to be a data scientist?

Data scientists process, analyze, and draw insight from data. They work across disciplines, spanning computer science, mathematics, statistics, and content-specific domains.

While not all learners will become data scientists, they will all become data users and consumers. These skills are essential to 21st-century living: visualizing, analyzing, and making decisions based on data is already necessary for both work and personal life. By designing and paving the way for data education, we ensure that today’s learners can apply those skills in a wide range of contexts.

# FAQ

# So what about teaching the beauty of math, for math's sake?

> Example:

An interdisciplinary math exploration with data science, statistics, and art history.

Data Visualization: Wikipedia article-links between major artists throughout history

Doron Goldfarb, Max Arends, Josef Froschauer, Dieter Merkl. Art History on Wikipedia, a Macroscopic Observation WebSci 2012, June 22–24, 2012,.

# > Students are unlikely to be inspired

# without modern applications or relevance

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In this example, students could explore several mathematical questions through analyzing data:

> How does geographical distance correlate with artistic influence?

> Does less distance between artists cause artistic influence? Why can't we prove it?

> What does "influence" really mean? What quantitative metrics can we use to define it?

# Ok, but what about lost critical thinking skills?

# > Critical thinking skills are not unique to one or even a particular group of mathematics.

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# > It is better to instill critical thinking through simulating situations students will actually encounter.

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# > Data science, statistics, and modeling are built on logic-based arguments, just like any mathematics.

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# Ok, but doesn't everyone need the existing math?

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# > Many careers don't, but curriculum models can preserve the path-to-calculus for STEM degrees.

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# > Forcing everyone to learn hand-written procedures with little context can be counter-productive.

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# > Increasing access to data science and other technology-focused education is itself an equity goal.

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# Ok, but how is this different from Statistics or C.S.?

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# > Data science combines some basic statistics, some basic code, and a practical ability to analyze data.

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# > Most school subjects are just artificial groupings of content and skills—we want to integrate them.

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# > We advocate for a curriculum with more applied math generally: data, statistics, and modelling.

# Ok, but aren't existing mathematics more "rigorous?"

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# > Data science, statistics, and mathematical modeling can all be just as "challenging."

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# > Definitions of "rigor" are based on false perceptions, rather than any education-science basis.

# > See this report on the evolving definition of rigor in mathematics.

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# Ok, but what do actual math experts think?

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"Data literacy is critical for our student as it will teach them to separate fact from fiction, and train them to be effective 21st century problem-solvers. Data explorations will engage students actively in the classroom. Importantly, a data science pathway has the potential to disrupt inequities in the STEM pipeline."

"Students need to move away from procedural learning and toward problem solving in meaningful contexts. They should make genuine choices about which tools they apply, what approaches they take and how they present their solutions."

- Dr. Rachel Levy, Mathematical Association of America / Harvey Mudd

"We could be teaching the power of all mathematics, including calculus, through inquiry-based problems using rela-world datasets. We need more than just drills in the classroom. "

- Dr. Steven Strogatz, Cornell University

Convinced Yet?