Frequently Asked Questions

Data science is a professional discipline in which people gather, analyze and interpret data sets to solve complex, real-life problems using statistical and computational methods. See the definition of the formal occupation “Data Scientist” from the Bureau of Labor Statistics.

Data science education refers to instruction focused on the systematic processes, analytical techniques, computational methods, and utilization of appropriate technologies to gain knowledge and insight from data. In the context of K-12 education, it equips students with the introductory skills and problem-solving that are necessary to collect or consider, analyze, model, interpret, and communicate data to answer investigative questions. Data science education draws upon tools and methods from mathematics, statistics, computer science, analytics, operations research, and the many disciplines in which they are applied.

For further reading on data science education, see example definitions from industry, from policymakers, and from higher-education.

Data literacy refers to the ability to read, understand, interpret, and communicate about data or claims derived from data. In the context of K-12 education, data literacy involves teaching students how to critically and iteratively question data and representations of data to understand their origins, limitations and potential biases. It also involves teaching students how to effectively communicate about data through visualizations and other means.

All high school students should graduate data literate. Every K-12 student should also have the opportunity to access age-appropriate, introductory data science learning opportunities in mathematics or other subjects.

While not everyone will become a professional data scientist, all people will be affected by data as both users and consumers. Moreover, a large number of current and future careers will require a robust baseline of modern data analysis techniques across sectors. By designing and paving the way for data science education in K-12, we ensure that todays’ learners can apply those skills in a wide range of contexts, and have the skills to leverage and interrogate many emerging data-driven technologies, such as artificial intelligence.

With rapidly increasing access to AI tools, the value and ubiquity of data is now clearer than ever. There are countless initiatives underway across the country to expand introductory access to these new tools, including efforts to offer data science classes, build curricula and training materials to equip educators, statewide initiatives to expand access in rural communities, and organizations creating public classroom-ready datasets to make sure all communities have access to 21st century education.

19+ states have moved to adopt K-12 data science education programs, 330+ organizations have committed to build new resources and learning opportunities, and the DS4E coalition joined with many national math, science, and civics education associations to introduce the Data Science and Literacy Act of 2023 in Congress. 150 prominent national education leaders have also called upon the President to build data science education opportunities across the United States (see the letter here!), and over 3000 education leaders now constitute the DS4E coalition.

Through our Commitments Campaign, we are building a Coalition to call upon industry leaders, schools and universities, policymakers, funders, and individuals to commit to action so that all students have the opportunity to gain data science skills. Learn more about the national commitments campaign here, and consider signing your name to support the movement.

In the construction of Artificial Intelligence (AI), data scientists are the primary trainers of AI models, and work in collaboration with computer scientists, software engineers, user-design professionals, and many others to implement AI models into practice for other users. For example, in the case of a Large Language Model (LLM) such as ChatGPT, data scientists help gather, scrape, organize, and input data into a model for training purposes. Data scientists also help monitor errors or unfavorable outcomes of a tool like ChatGPT, and suggest critical improvements and fixes to prevent them.

In the usage and application of Artificial Intelligence (AI), data literate individuals are able to effectively and appropriately leverage an AI model or tool for a problem at-hand. These skills include deciphering the input and output data of AI models for potential errors, interrogating how the AI tool was designed, and deploying a strong intuition on data-fueled technology to confidently assess future tools we cannot currently predict, including their use-cases, limits, and potential harms.

For example, an individual confident with data would easily recognize that so-called “hallucinations” are in fact by-design in a tool like ChatGPT, because while fueled by large amount of the internet’s text-based data (which itself can often be inaccurate), the tool is designed to give correlation-based predictions that sound realistic as possible, but are not factually-accurate as possible, approximating based on the initial data the model was given. For more, see our virtual Ask-Me-Anything event on “Accessing AI.”

Data science is now fundamental to a broad range of fields, including STEM, social sciences, and even the humanities. As all these fields involve ever increasing amounts of data, it is vital for our students to learn how the data is generated, analyzed, and summarized. Data is also the primary fuel behind many new emerging technologies (e.g. artificial intelligence) that have the potential to fundamentally reshape our economy and our society, and in many cases have already done so. As tasks within any career become increasingly computer-assisted, it is an urgent national priority for any graduate to know how to work with these systems, including both the input, output, and interpretation of their data. Developing a workforce equipped with data science skills is in our immediate economic and security interests. We recommend reviewing our Advocacy Deck and our Medium page for more information.

No. Many believe that data science is a technical skill, only for statisticians or machine learning experts. But the reality is data literacy is a skill for all. Anyone in any role who interprets, visualizes, or communicates data is leveraging data science skills — and the approach goes well beyond math. Data scientists process, analyze, and draw insight from 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.). To see how data science can be applied across disciplines, watch our Ask-Me-Anything Event with NYC Department of Education, where we discussed and answered questions about expanding data science curriculum across disciplines and various formats.

No. While data science was once viewed as a domain exclusive to industry-focused statisticians, mathematicians, and computer scientists, its applications have since evolved to be much more expansive. Today, data science is a universal skill, and data science-related skills are needed by workers in a multitude of fields, including agriculture, manufacturing, healthcare, law, logistics & freight, and many others. Anyone in any role who interprets, visualizes, or communicates data is leveraging data science skills — which centers on, but goes beyond traditional high school math content.

Frequently cited definitions of data science in both industry and K-12 education emphasize the critical importance of domain knowledge. Data science is most productive in context, and a student should learn background knowledge about the data they may be analyzing either ideally before or in conjunction with the analysis process.

To see how data science can be applied across disciplines, watch our Ask-Me-Anything Event with NYC Department of Education, where we discussed and answered questions about expanding data science curricula across disciplines and various formats. (Our full list of prior Ask-Me-Anythings and other events on K-12 data science education can be found here.)

In high school, data science may involve introductory or “light-touch” coding in R, Python, or other scripting languages, including through the use of Jupyter Notebooks or other simplified coding environments. Beginning in earlier grades, classroom-specific tools such as CODAP, Tuva, DataClassroom, and Pyret can be used to scaffold learning for college and career. Spreadsheets can also be used to introduce students to data tables and basics of data analysis. In short, coding is encouraged, but not necessarily required at a student’s first exposure to the discipline.

Looking for classroom resources or professional development (PD) opportunities? Check out our classroom resource hub here. Upcoming PD opportunities are listed here.

At least in K-12 contexts, no one is expecting any student to graduate as a professional data scientist. And while not all learners will become data scientists, they will become data users and consumers. Data literacy and introductory analysis techniques 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.

For students who become inspired to pursue professional data science, there are a variety of educational pathways available to access data-focused careers.

As data science has grown, the field has also specialized: high-paying entry-level careers in data engineering, business intelligence, and data analysis are available to individuals with industry-recognized credentials, associate degrees, or other work-based learning programs.

A full-time professional Data Scientist typically requires at least a Bachelors or Masters degree in data science, Statistics, Computer Science, or a related field. Bachelor's degrees at 4-year institutions in data science are available in every U.S. state. For students who opt for this path, several college-level mathematics courses are required and encouraged while a student is in college.

In other fields, our education system offers many age-appropriate learning opportunities for students from early foundations to chosen specializations. For example, most U.S. K-12 schools require all students to complete courses named “Physics”, while small numbers of students may go on to pursue a Masters or Ph.D. in Physics. And while taught at an introductory level, we do not label these courses “physical literacy,” even if that is the ultimate learning goal for the vast majority of students. Given the increasing everyday usage of data in the 21st century, we strongly believe that data science should be treated as a fundamental part of K-12 instruction, similar to physics or other foundational subjects.

No. Similar to how high school students are able to learn introductory versions of Physics, Statistics, Computer Science, and even Engineering without Calculus, a high school student does not need Calculus to learn the “Level 1” of Data Science. Many data science programs are designed to be “low-floor, high-ceiling” that teach concepts through multi-week problems and capstone projects, wherein increasingly advanced mathematics are introduced over time as a student needs them.

This is not to say a student cannot learn both – and in fact, we believe an advanced “Data Science II” high school course may provide a compelling entrypoint to introduce and apply other rigorous topics in mathematics for many college programs in a 21st century context, including Linear Algebra, Combinatorics, or Calculus.

Statistics education has been part of math standards for decades, but has not been sufficiently emphasized or taught in synthesis with modern tools. Most introductory data science courses cover statistics course content, but with additional techniques and technology tools.

All disciplines are updated to reflect changes in knowledge, culture, and technology. The calculator has replaced the slide rule, Latin is rarely taught, and cursive writing is no longer a required part of the standard literacy curriculum. To teach math as it was taught 50 years ago, before computers were ubiquitous, deprives students of the opportunity to acquire the most relevant quantitative skills and to use the technology humanity has worked hard to build.

Yes, if designed properly to be subject and age-appropriate, as is the case for any course. In 2013, the National Academies of Sciences, Engineering, and Medicine (NASEM) published a consensus study chaired by Thomas E. Everhart (formerly the president of the California Institute of Technology) and authored by the field’s leading mathematicians recommending the field itself should “more thoroughly embrace computing” given it is “central to the future of the mathematical sciences, and to future training in the mathematical sciences” (see Chapter 4). The report also noted that data analysis at scale with technology is “inherently mathematical in nature.”

In many ways, data science programs can demand more of students than a typical math course experience 20 or even 10 years ago. This includes a synthesis of complex problem-solving, mathematical theory, and concrete technology skills concurrently, as well as a requirement for persistence, creativity, and comfort with ambiguity to succeed – 21st century skills that many employers have been repeatedly asking from our country’s education system.

No. We observe students of all race, gender, and ability identities benefiting from data science education programs. Data analysis and data technology skills directly translate to tangible income premiums for anyone who earns them, and we expect these gains to persist and increase over time for a variety of jobs across sectors.

We believe in ensuring that wealth gaps, intergenerational poverty, historic or present discrimination, and geography-based technology gaps (often called the “digital divide”) do not exclude a student from accessing or participating in data science education opportunities.

Any college or university will view a student’s attempt to learn an advanced subject such as data science as favorable, let alone the act of taking an additional math course. (If you find an experience to the contrary, please let us know, as it would be the first to our knowledge.)

The National Association for College Admission Counseling (NACAC) and the NCAA both already recognize high school data science as a rigorous and high-quality mathematics option – meaning high school data science courses are categorically eligible for many 4-year programs and scholarships.

Moreover, 138 college degrees now require a strong foundation in the basics of data science and statistics, as many fields adopt more data-intensive research in-line with broader technology changes.

The National Academies of Science, Engineering, and Medicine (NASEM) published a landmark report in 2023 cataloging the past decade of research and development to create age-appropriate learning experiences in data science foundations. We also recommend reviewing Concord Consortium’s DSE Research Database, the Institute of Education Sciences (IES) Evidence-Based Practice Site and their DSE Implementation Recommendations, and the Journal of Statistics and Data Science Education. Rather than summarize here, we strongly recommend reviewing these extensive resources.

No. As implemented in K-12 math education, data science is either offered as focused standalone courses or as integrated into existing courses to modernize existing content and provide more relevant context. In either case, data science is an addition, not a subtraction, to mathematical learning. Recent national conversations on the high school math sequence focus on restructuring arbitrary course units (such as an Algebra 2 course), that include both useful and less useful content within their typical syllabi. Data Science 4 Everyone advocates for all existing math courses to be modernized with relevant technology and concept focus for the 21st century.

Within the K-12 mathematics trajectory, students may wish to take a one-year high school Data Science course before or in substitution of other math courses (i.e. Algebra 2), following a foundational Algebra course (i.e. Algebra 1). However, higher education institutions have yet to universally allow this type of substitution.

A growing number of education leaders have increasinglyquestionedtherelevance of traditional high school math courses, suggesting a potential over-allocation to procedural manipulation and memorization, and an under-allocation to teaching math with and about modern technology (i.e. computers), context, and applications. A 2013 National Academies report (see Chapter 4) recommended greater emphasis on computing, technology, data analysis, and modern applications for the academic field of mathematical sciences in higher-education. However, others have resisted allowing K-12 mathematics teaching to do the same: specifically in the context of the University of California, concerns have been raised over allowing data science to substitute for Algebra 2 (see guest blog post for historical context). Given existing rigidity of K-12 course definitions and college entrance requirements, many degree programs have yet to more precisely consider how computing and new technology affect what content should be required of all incoming students across disciplines. At this time, DS4E recommends checking with both your state’s graduation requirements and your prospective postsecondary institution(s), as requirements may vary by state.

Absolutely. Data science builds on and supports teaching of foundational math. For example, the “essential concepts” defined by the National Council of Teachers of Mathematics are aligned with data science as well as related areas of advanced math, such as advanced algebra and calculus. Algebra focuses in part on functions, which are central to data science. Both algebra and data science rely on mathematical modeling, and both have the power to express patterns. While algebra focuses on solving for unknowns, data science supports making predictions and quantifying uncertainty. For more on the power of combining these disciplines strategically, see this guest op-ed as one of many approaches.

All high school data science courses impart content that cover a significant number of Common Core Mathematics Standards (CCSS). Additionally, some courses cover content beyond AP Statistics curriculum expectations, and others were adapted from popular college-level programs.

The organizing team includes full-time staff based at the University of Chicago, along with shared capacity from the Center for RISC, The Learning Agency, and the Concord Consortium, with the generous support of Schmidt Futures, Valhalla Foundation, Citadel LLC Founder and CEO Ken Griffin, and the Siegel Family Endowment. For partnership or press opportunities, contact info@datascience4everyone.org.

We encourage you to join our DS4E Slack community to connect with others in the coalition. We will continue to share updates, opportunities, and other resources to build this movement. Additionally, attend one of our upcoming events to connect with coalition members, commitment-makers and others interested in expanding data science education. Or, take a look at our 2022 commitments directory to get an idea of who is involved.

Join our DS4E Slack community to connect with others in the coalition! Use #ds4everyone on social media and follow us on Twitter and LinkedIn! We host ongoing events and opportunities in addition to the annual Commitments Campaign. You can also get updates and stay in touch by subscribing to our mailing list, or contacting our team at info@datascience4everyone.org

Please contact info@datascience4everyone.com to inquire how the coalition can help promote your data science related work and events, facilitate introductions to potential partners and stakeholders, and connect you with upcoming opportunities.

We can't do our work alone - that is why Data Science 4 Everyone is organized as a coalition comprising 100s of educators, policymakers, curriculum designers, parents, students, and other stakeholders. Join our Slack to find volunteer calls, internships, and other opportunities.

Please contact Brian Bock (bbock@uchicago.edu) for information on press related inquiries.

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