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Leaders from the industry, education, and nonprofit spheres gathered to ensure Virginia’s students develop into the innovators of tomorrow. Dr. Michelle DeLoach presenting as part of the K-12 Data Science & Literacy Panel. Rarely does a state build a new high school course from scratch, let alone an entire content area. Virginia was already well on its way to becoming a national leader on Data Science and Literacy. But on December 3, the state’s K-12 leaders decided to take things a step further, collaborating with The Virginia Society for Technology in Education (VSTE) to gather K-12, higher education, and business leaders for a conversation focused on guiding Virginia’s students on their journeys from the classroom to their future careers. The Data Science Career Pathways Summit, hosted by VSTE as a part of their Annual Conference in Roanoke, Virginia brought these leaders from many different spheres of influence together to focus on one shared goal: empowering Virginia’s students with the data literacy skills and data science education they will need to succeed in the 21st-century workforce. New commitments to accelerate K-12 Data Science Over 50 new commitments, suggestions and ideas were gathered at the Summit to advance statewide work in K-12 Data Science education. These commitments ranged from Laurel Ridge Community College agreeing to add Data Science to the teacher CS Generalist Certificate to the expansion of dual-credit partnerships with the community college system. Equipping the future workforce The summit opened with a welcome from Dr. Lisa Coons, Superintendent of Schools, Virginia Department of Education. Rod Carnill, Executive Director of VSTE, connected the purpose of the Summit to prepare the workforce needed now and in the near future to the VA 2030 Blueprint. The workforce and industry panel then tackled the question of which knowledge, skillsets and dispositions related to data are needed for employees’ success. The workforce panel was stacked with panelists from every industry touched by data science, from IBM to NASA. Many speakers focused on the need to teach students data science in order to keep the US workforce competitive: Ted Hallum of IBM shared, “If we talk about keeping the US competitive, for every one machine learning or data scientist the US produces, China produces nine…If we want to remain competitive over the next five, ten, 15, 20 years, we need to radically optimize our pipeline for people to learn these skills.” Henry R. (“Speaker”) Pollard, V. moderates the Business & Industry Workforce Panel. Several panelists pointed out that these skills must be taught long before an employee ever steps into the office, especially with the advent of AI. Angela Rizzi of NASA shared, “We need to understand this doesn’t start in college, and it doesn’t start in high school — it starts much earlier. We need to build capacity and interest amongst our earliest learners.” Joshua Jones, CEO of Quanthub, shared, “[AI] Prompt engineering is a data skill, and not enough folks are talking about this or are afraid to do so. You need to understand data privacy, data fluency, and how the tool works to do that well.” Dr. Padhu Seshaiyer moderates the Higher Education panel. Paving a data science pathway to success, no matter the method This was followed by the higher education panel led by Dr. Padhu Seshaiyer from George Mason University, VA that included faculty from 2-year and 4-year universities (public and private) across the state including University of Virginia, Old Dominion University, Longwood University, Shenandoah University and Laurel Ridge Community College. Dr. Seshaiyer currently serves as the higher education appointee to both the VA STEM Education Advisory Board and the VA Workforce Development Board to the Office of the Governor of Virginia. The panel focused on questions surrounding higher education pathways in data science around degree programs, majors, minors, digital badging, microcredentials and dual-enrollment as well as aligning these programs to the workforce needs. The questions included: What should all students know about data science by the end of high school, regardless of career or postsecondary path? What prerequisite subjects will set students up for success in university data science programs? What are the advantages of getting a formal data science degree? How would you advise a student to choose a particular data science program or pathway? Speakers focused on the overall necessity of data science education, for all students. Dr. Ralph Wojtowicz, Director of the Division of Applied Technology and Associate Professor of Mathematics and Shenandoah University, shared, “Why is data science important to study at the undergraduate level? Without data, there is no science. Without data, there is no business.” Dr. Brian Wright, Associate Professor and Director of Undergraduate Programs with UVA’s School of Data Science, shared, “We need to abandon the idea that Data Science is fundamentally derivative of other disciplines. Other disciplines do not caveat their study as being footnotes in other fields.” Empowering tomorrow’s leaders today Dr. Deb Crawford moderates the K-12 Data Science & Literacy Panel. The summit closed with a panel on K-12 Data Science and Literacy led by Dr. Deb Crawford, VDOE Data Science Course Development & Implementation Lead. Panelists from school districts across the state emphasized the need to begin teaching students data literacy skills early and often, highlighting the fact that though many students might not go into STEM, all would use data. A graph used during the K-12 Data Science & Literacy Panel highlighting the need for data literacy Panelists also noted that data literacy and data science education are not easily taught in one singular course and need to be infused across subjects in order to reach all students at the beginning of their educational journeys. This may require changing the image most students conjure when they think of data science. As IBM’s Ted Hallum shared, many young students count themselves out of math early on because they’re intimidated by the language of math and fail to see themselves in the unapproachable formulas and hypothetical questions. However, once they can see themselves and their lives reflected in the math, in the data, they begin to see its power: “I listened to the lecture, I read the textbook, the concept was totally alien to me… and then when we did an applied exercise and wrote it in R, I finally understood what the formula in the textbook was trying to express….I hope you’ll entertain the idea of taking the math you’re familiar with, embracing a coding-language, and integrating those two things together — because out in the workforce, that’s how it’s going to play out in the real world. The sooner [students] can gain experiences with that, the better.” Chris Dovi, current Virginia STEM Advisory Board Appointee and Co-Founder of CodeVA, shared during a lunch talk, “As a former journalist, I can tell you that data science education protects democracy. We need a data literate population that understands how to interpret statistics they see in the news and on social media. We need people to ask the right questions and to understand how our changing world works.” As Executive Director of CodeVA, Chris Dovi is now helping amplify the work created by the VDOE Data Science Course Development & Implementation Taskforce and making it accessible to other states, with samples of content from Virginia’s OER portal modeled from resources created by the VDOE Data Science taskforce. Statewide progress in Virginia outpaces the country Few states have done as much as Virginia to ensure students are prepared for the data-driven 21st century. A new high school course developed from scratch; statewide educator training cohorts; modernized content standards in K-12; and students placing in Ivy-league institutions with projects submitted from their new coursework. The list of achievements is long and continues to grow as Virginia K-12 Data Science sets out to scale to additional schools. How did Virginia manage to accomplish so much so quickly? In 2020, the Virginia Department of Education (VDOE) Mathematics leadership organized a Data Science course development team to write the Virginia Standards of Learning for a high school Data Science course. This Phase 1 team of K-12 and higher education representatives began organizing conversations to imagine a new type of high school mathematics experience for students, one which would center data analysis, technology, computing, and core mathematics learning together in one joint program — reflecting how these disciplines are utilized in the modern workforce. “In creating the course, we have been intentional in employing project-based learning combined with an instructional philosophy that moves beyond the traditional approach of ‘here is the mathematics, statistics or computing, go solve the problem’ to ‘here is the societal problem or dataset, let us find innovative data science approaches motivated by mathematical foundations, statistical thinking or computational thinking to solve it’” — Dr. Padhu Seshaiyer, Professor of Mathematics and Director at George Mason University “Students rarely have an opportunity to explore problems relevant to them or their communities. We are making that the norm in data science courses.” — Dr. Deborah Crawford, Mathematics Supervisor, Frederick County Public Schools The Virginia Data Science State Standards and content unit resources were developed entirely from scratch by a diverse team, including K-12 district leaders and teachers, practicing data scientists from industry, six higher education institutions, CTE reps, and the Virginia Department of Education mathematics team. The team developed four curriculum modules in 2021 which were field tested in the Year 1 pilot in 2022–2023, following the approval of the DS state standards in April of 2022. The Data Science Leads team revised the unit resources in 2023, based on feedback from the Year 1 pilot teachers through VDOE surveys and data science teacher focus groups. Four curricular modules, including over 310 vetted resources, video guides produced in-house, and locally sourced datasets and data talks and project guidelines and rubric for student choice in problem statements were created and shared with teachers on a Data Science Hub in a Canvas LMS through Virtual VA. Teachers drove much of this content development, working late hours and countless weekends — far beyond their normal job descriptions. “The Data Science Standards of Learning in Virginia were developed by a diverse group consisting of Virginia educators from higher education and K-12 along with input from representatives from business and industry across the Commonwealth. The varied perspectives brought to the table by each member of this group resulted in a rich set of standards that address the future needs of students as they leave K-12 education and enter college or the workforce. The skills needed to be successful in today’s data-driven environment are different than the skills needed just ten years ago. Students must be flexible learners who can represent data and analyze information in order to recognize patterns and solve problems.” — Tina Mazzcane, fmr. Virginia Department of Education, Mathematics Coordinator The Data Science Leads team designed two VDOE Data Science 3-day institutes in 2022 and 2023 to provide professional learning for new Data Science teachers and key insights into the data literacy skillset. Partners providing training included NASA, Amazon: AWS, CodeVA, and NC State InSTEP. Teachers from the first pilot year are now mentoring the Year 2 pilot teachers, and this network of data science high school educators is fueling the acceleration of resource creation and coaching statewide. The Data Science Career Pathways Summit connected district leaders, teachers, community college and university professors with community partners from business and industry. The Year 2 pilot teachers are forming community partnerships where students work with a partner on a problem statement from a real data set provided by the partner. One example comes from Norfolk Airport Authority who asked students to use their data to create a cost-effective model that allocates every incoming flight at Norfolk International Airport to a specific gate. Data science students themselves are reaching out to potential community partners based on their interests. eMediaVA Video Series on Virginia K-12 Data Science Pilot The VA Data Science course kicked off their pilot year in the 2022–2023 school year, with nineteen high schools piloting the program statewide — and it went better than expected. Some students entered their projects in regional and state-wide science and engineering fairs with great success. Student teams from the pilot took first place in Engineering, first place in Mathematics, and third place in Computer Science at the JMU Science & Engineering Fair. Other students at Frederick County Public Schools leveraged data science and computing projects developed in the state’s pre-pilot as supplement materials on college applications, landing them coveted spots at the nation’s top universities including John Hopkins University, Dartmouth University, and the University of Virginia. The data science pilot has since expanded to about 50 teachers from 23 school divisions and independent schools this school year and is on track to reach 75 teachers in the 2024–2025 school year. Last August, the state capitalized on this momentum by infusing data literacy throughout their updated mathematics standards of learning for K-12 curriculum, building upon the course state standards released in April 2022. Now, students as early as K-5 will begin learning more robust foundations of data intuition and literacy — guaranteeing high school isn’t the first time these students encounter data learning. Virginia’s work has been nothing short of heroic and has generated national interest in the subject. Many in the state were working extra hours or on volunteer basis to make 21st century mathematics opportunities that prepare students for today’s realities of career and daily life. But more action is needed nationally to ensure data literacy can be the norm, rather than the exception. Are you an educator in Virginia eager to start a data science program in your school? Contact Keisha Tennessee, VDOE CS Coordinator, keisha.tennessee@doe.virginia.gov, or Dr. Deb Crawford crawford@fcpsk12.net for more information.

White House commemorates #CSEdWeek, and calls for AI education across the country. DS4E's Director Zarek Drozda joins an assembly of educators and advocates at the White House for CSEdWeek. On December 5, Data Science 4 Everyone joined a White House convening commemorating #CSEdWeek and calling for education on the fundamentals of AI for students everywhere. This year marks the tenth anniversary of the first Presidential call for Computer Science education in K-12, following a viral video from Code.org in 2013 entitled “What Most Schools Don’t Teach,” featuring President Obama alongside many tech founders, from Jack Dorsey to Bill Gates, and even celebrities like Will.I.Am of the Black Eyed Peas. That momentum eventually led to the 2016 launch of CS for All, built upon even earlier work for Computer Science Education Week beginning in 2009 through the joint efforts of ACM, CSTA, the National Science Foundation, and bi-partisan Congressional support. CSEdWeek has since grown year-over-year, with hundreds of organizations joining in and served as the incubator for the now global “Hour of Code.” The week also coincides with the birthday of the late Grace Hopper — one of the first computer programmers and inventor of the first compiler who coined the term “bug.” You can read more about the rich and ongoing history of CSEdWeek here. Now enter generative AI. While AI technology such as neural networks have existed since the 1960s, these systems suddenly become powerful when powered by massive amounts of training data; data that spans the internet and nearly all digitally recorded human interactions. Overnight, tools like ChatGPT galvanize the public, make technology hidden behind the scenes incredibly visible, and growing the public user-base 250X faster than Netflix. In the best-case scenario world, AI has the potential to transform economies, replace work that is unsafe or unpleasant for humans, and rapidly advance scientific and empirical research. In the worst-case scenario, AI has the potential to widen the digital divide, disrupt our economic fabric, and further entrench systemic harms through biased data and algorithms. Ami Fields Meyer, Senior Policy Advisor to Vice President Kamala Harris, stated this perfectly: “There can be extraordinary outcomes [from AI] that people in this room know, which comes from the ability to solve problems at scale. Those exact same outcomes can be done negatively, at scale.” In any scenario, education about AI (or lack thereof) will determine the outcome — including who gets to participate, who it benefits, and how it is applied. Charity Freeman, Chair of the CSTA Board of Directors If we continue marching toward an AI-powered world, all students will need to be equipped with at least some foundations of AI and the many other emerging digital technologies. Whether pursuing entry-level careers in healthcare or manufacturing, engaging in advanced research, or building the AI systems of tomorrow, today’s students are more likely to affect the outcome, both for our country and others. Karen Marrongelle, Chief Operating Office of the National Science Foundation, shared, “We need an AI savvy workforce, and we need to start today, to support 21st century workforce development, ensure our national competitiveness, and our national security.” Students need to know the limits of AI tools, how they operate, and when (and why) they make mistakes. We will build a better future if we can all harness this technology with confidence rather than simply respond to it. Data is critical to that mission. Chirag Parikh, Executive Secretary of the National Space Council and Deputy Assistant to the President, shared that, “we included both the Secretary of Education and Secretary of Labor in the National Space Council, because this is about the future space workforce… there are huge amounts of data coming down from our space equipment and observations — petabytes of data coming down daily — whether used to explore space or fight the climate crisis. How do we make that data useful? It’s not just computer scientists, it’s everyone that needs to be prepared. We need a diverse workforce to make this a reality: scientists, software engineers, machine learning experts — AI is a broad umbrella that needs a lot of work, from efficient data conditioning to data management, and [with diverse applications] from crew transfers to autonomous equipment to more efficient transfer of data back to earth.” Teachers are showing what’s possible. Nora Burkhauser, a Computer Science Teacher in Montgomery Blair High School, shared that “high schools are already learning about AI in my classroom. It is possible.” Students are “excited about learning AI and very excited to do AI projects — they collect the data, train the model, and then I frequently remind them “what problem is this solving?” Today, 57% of high schools nationally now offer computer science programs, yet student enrollment numbers are still unfortunately hovering in the single-digits (5.8% as of 2023). During the same period, the power of “big data,” internet of things, artificial intelligence, and other emerging technologies were concurrently transforming both industry and everyday life outside of the classroom. Jake Baskin, Executive Director of CSTA Technology is quickly accelerating, just as our education system is struggling the most. Daaiyah Bilal-Threats, of the National Education Association, reminded the group that “there are school districts right now that are 100% staffed with emergency substitute teachers,” due to teacher shortage and retention issues in many communities acorss the country. How do we create opportunities for students to learn how AI works, within a K-12 system that is facing record learning loss in existing school subjects, teacher shortage and retention challenges, and an engagement gap of only 5% of students in computing courses? This systematic challenge cannot fall on computer science teachers alone. We need to continue building the momentum to grow CS access and participation nationally — and invest like we say that it matters — but we also need to quickly broaden the focus, and leverage the teachers and capacity we already have in the other five or seven school subjects. Nor should we expect our existing CS educators to cover every emerging technology under-the-sun each year. The data is proving we need to engage students in these subjects through more avenues. If we truly want to diversify technology users and builders, we need to meet students where they are at: with diverse subjects and interests, whether in biology, history, or economics. Krystal Chatman, Instructional Technology Specialist for Jackson Public Schools and CSTA Equity Fellow, emphasized the importance of “the cross-curricular conversation, it can happen in math or social studies. Understanding the limitations and how things work, how things can be harmful, that is a job for many disciplines.” It must be accessible, regardless of a students’ identity or interest. Gina Fugate, Maryland School for the Blind, shared “My students love AI, and it’s because they feel included. I have students who aren’t allowed to take Computer Science courses. It’s not because the disability stopped them. It’s because the disability made others think they weren’t able to participate. How can we make sure that my students can jump back in through AI?” Margaret Martorsi, Associate Director, National Science Foundation Directorate for CISE (Computer and Information Science and Engineering), shared that “AI is deeply rooted in CS, but also needs to be broader. Neither ignoring CS foundations nor ignoring its broader disciplines would make any sense. This is big; and in order to benefit students around the country; it will take a lot of people and resources.” Tom Berry of Amazon noted that “NGSS has data science in it. Students are excited to learn data and navigate projects that include computing. We can be a value-add; and students can find a window into the future and a mirror of themselves back in many areas.” AI education should not be exclusively about developing more AI tools and technologies; that will happen. AI education should also arm the public with the skills to navigate an increasingly algorithmic landscape that will govern major aspects of their lives (with or without their knowledge), both in their career and in daily life. That begins with knowing the input and output of these systems, and questioning that data rigorously. We spent the past decade building a 21st-century school subject. Now we need to go back to the rest of the school curriculum, and identify ways to modernize them, with specific interventions aligned to the discipline — whether math, biology, history, or computer science itself — to capture every student and ensure they are ready. That way, everyone will be able to truly participate in the AI future. We thank Dr. Thema Monroe White and Sean Sukol for informing the perspectives in this summary article.

North Carolina State University, a pioneer in the data science education field, served as the setting for the state’s first-ever data science education summit. It takes a village to educate a student — especially when the landscape is changing. On September 28th, NC State’s Data Science Academy partnered with Data Science 4 Everyone, the Friday Institute for Educational Innovation, The Science House, and the NC School of Science and Mathematics to host the first statewide NC Data Science Education Summit. The convening brought together key stakeholders in the state from the higher education, K-12 education, industry, and nonprofit sectors focused on creating a common vision for data-empowered teaching and learning in K-12 schools across North Carolina. Ray Levy, Professor of Mathematics at NC State University and Executive Director of the Data Science Academy, summarized the focus of this cross-sector effort: “We need to bring data science education to all 100 counties… and make North Carolina a model for the country, if not the world.” Along with several local institutions, the NC Department of Public Instruction sponsored the event and gathered content experts from both Career and Technical Education and Mathematics education to support these conversations, engage with community participants, and think forward about North Carolina’s future in data science education. “The NC Department of Public Instruction is excited to be able to collaborate with stakeholders from across the state on this project, as we recognize that our graduates will need to have ample experience with using and analyzing large amounts of data when they enter the workforce. As NCDPI continues to develop learning pathways for students, the inclusion of data science as a means for applying math standards in rigorous and relevant ways will become more and more important to their success. The NC Data Science Education Summit was a great chance to start that collaboration, and we look forward to this continued partnership and the work ahead.” — Charles Aiken (Section Chief, Mathematics, Science and STEM, NC Department of Public Instruction) “Data Science is critical, in every industry, to help push innovation, creativity, and lead the way for the future. Students need to be exposed to data, and be comfortable with interpreting, manipulating, and creating visuals to tell the stories of today and the future. The NC Data Science Education Summit was a magnificent opportunity for individuals to come together to discuss actionable paths forward for promoting Data Science. This was the first day of what I think will be bright days ahead for education in North Carolina to lead the way on Data Science.” — Eli Hamrick (Secondary Computer Science, IT, and Technology Education Consultant, NC Department of Public Instruction) North Carolina State University (NCSU) has made a name for itself by adopting a teaching model that incorporates data science across all 10 of its colleges, and many speakers from the K-12 and higher education fields focused on the ways in which North Carolina’s students can benefit from this kind of interdisciplinary approach to data science education: “When I talk to people outside of tech, I let them know that you don’t have to be a computer scientist or a data scientist. You do need to become data-driven and data-literate to function in a world with the tech developments of the last 20 years. We need to think of data as a natural resource like air or water or oil. And we need to get that thinking in place as early as possible.” — Timothy Humphrey (IBM Chief Analytics Officer) Incorporating data education foundations early was a significant focus of the conversation. Both for a wide variety of careers, but even more so for everyday life functions like consuming the news or navigating personal finances. Data is becoming an increasing part of the modern daily fabric: “Everyday life, the tools we interact with, and what is required of the workforce is now filled with data. Even our news is full of data… and yet students do not get the opportunity to interact with it in the classrooms… we’ve got to change that, and change that quickly.” — Hollylynne Lee (Distinguished Professor of Mathematics and Statistics Education, NC State University; Director of the Hub for Innovation and Research in Statistics Education at the Friday Institute for Educational Innovation) “The modern world is rich in data and poor in data literacy. Data science education arms students with the skills they need to become critical thinkers and responsible citizens in an information-laden world. For traditionally underserved students in particular, data science also provides a means to empowerment. In addition to job market appeal, learning data skills can give students new windows into exploring and contextualizing real issues that affect their lives.” — Dashiel (Dash) Young-Saver (High School Math Teacher, San Antonio, Texas; Founder and Executive Director, Skew the Script) “Today’s lawyers, judges, and legislators need a basic level of familiarity with data science, given its growing relevance to legal issues.” — Terry A. Maroney (Professor of Law, Vanderbilt University Law School) Yet, the adoption of data science curriculum in classrooms across the country, including in North Carolina, hasn’t yet caught up to NC State University’s bold vision for the subject. Most students don’t encounter data science until they’ve reached upper-level high school courses like AP Statistics, which means it can be difficult for students to build the comfort and confidence they need to explore the subject fully in college and beyond. The call for a change came from educators and students directly: “[Data science] creates more ownership and agency over what they’re doing in the class. Students also envision themselves as mathematicians or scientists more so — they are doing the work, rather than repeating it, and it allows them to experience real skills in the workforce that a mathematician or scientist will use in their careers and daily lives.” — Emily Shy (Teacher of Mathematics and Science at J.F. Webb High School in Granville County) “I took a risk to take a non-Pre-Calc course. Because of that opportunity, I was able to get into NCSSM, and do a variety of research experiences that I would not otherwise have known existed. The risk paid off, but it should not be a risk for anyone. I truly believe at the bottom of my heart that data science should be for everyone.” — Kenzo Hubert (NC State University Student) According to North Carolina’s Department of Commerce, computer and mathematical occupations that rely on the skills learned in data science courses are projected to become one of the fastest growing occupational groups in the state, with an estimated 20,000 jobs in the sector to be added before 2026. Increasingly, success in the job market, regardless of industry, relies on one’s ability to both produce and interpret data. “The world is now data-infused, and we have very large datasets that we can use in many different ways for many different applications — our teachers need to know how to use it, and support their students in doing so… they will live in a world full of data, and we need to help them prepare for that.” — Paola Sztajn (Dean of NC State’s College of Education) “Data science education equips students with analytical skills that enable them to make informed decisions and solve real-world problems in an increasingly data-driven society. When we introduce these concepts early, students can explore more diverse career opportunities, creating a skilled workforce and ultimately contributing to North Carolina’s technological and economic growth.” — Talithia Williams (PH.D. PBS Nova Wonders, Harvey Mudd College) A recent analysis of job posting data conducted by the McKinsey Global Institute illustrates this growing emphasis on advanced quantitative and computational skills in the workforce, including the use of artificial intelligence. Though there is some controversy over how artificial intelligence should be used in the classroom, many speakers at the summit highlighted how this new technology should be treated as a new tool for students to learn, rather than a shortcut. Teachers are not waiting. During the afternoon, convening attendees had an opportunity to engage in a gallery walk of data science and data literacy lesson plans developed by K-12 educators from across North Carolina — including many graduate students at NCSU’s Friday Institute. The topics of content varied widely: predicting cell phone battery life over time, finding outlier weather patterns, analyzing traffic patterns in Durham, collecting 3-point rates in basketball, even counting and categorizing ladybugs to build data intuition. Yet they all shared a few key characteristics: real datasets, robust analytical techniques, and a shared commitment to empowering students in an increasingly data-fueled world. K-12 educators from around North Carolina share their classroom lesson plans. The experience developing and teaching data science “has allowed me to see how mathematics is connected to real life.” — Kelley Anderson (Mathematics Teacher, J.F. Webb High School, Granville County) At the close of the summit, participants shared commitment statements focused on identifying how they intend to support data-empowered teaching and learning in North Carolina over the next five to ten years, ensuring that this first-annual event will have a long-lasting effect on the state. North Carolina is uniquely positioned to lead the data science revolution across the nation, and this is just the beginning of the state’s journey into the future of education. Most exciting, state leaders announced the launch of the North Carolina Data Science + AI Education Network, which will carry forward the work of the convening into a statewide working group. This network will serve as just the start of the data science revolution sweeping the Tar Heel State. “In our data-driven world, we are preparing students not only to navigate, but also to innovate. By integrating data science across our high school curriculum, we’re fostering data-literate youth equipped to lead North Carolina forward in an ever-evolving digital age.” — Kevin Baxter (Vice Chancellor and Chief Campus Officer, North Carolina School of Science and Mathematics, Morganton) The call to action is already making a national impact and was recognized by national representatives in attendance: “Thank you for your interest as a state, for North Carolina to take the bold step to recognize data science as vital and critical for our students’ future as it is” — Kevin Dykema (President of the National Council of Teachers of Mathematics) Data science and AI education is just beginning. North Carolina has an opportunity to become a national if not international leader in these subjects. The ingredients are already in place. *Attributed quotes were either submitted prior to the summit or spoken during live presentations. For questions on the summit, the newly announced North Carolina Data Science and AI Education Network, or other, please contact: datascienceacademy@ncsu.edu

By: Steven Levitt In a recent Fortune Magazine op-ed, Freakonomics author Steven D. Levitt strips away the noise surrounding the debate over data science education and zeros in on what matters most: "The data revolution is here and our kids are not prepared for it." Levitt explains that without leadership from policymakers and educators, "this revolution will still happen, but the benefits will go disproportionately to the students who are already advantaged." Read the full article in Fortune Magazine here.

As the U.S. continues to wrestle with myriad concerns over the benefits and risks of artificial intelligence (AI), China has already emerged as an AI superpower with a clear focus on the use of data and analytics to achieve global dominance. This poses significant short and long-term economic and national security implications for all Americans, from every walk of life and across every corner of our nation. Read more of Laura Albert's Op-Ed in The Hill here.

Industry, education, policy leaders convene to ensure education meets the future of work Not often do leaders from across so many sectors come together for a shared, singular goal. On September 12th, representatives from Utah industry, higher education, policy, K-12 education, and nonprofits came together alongside teachers, parents, and even students in American Fork, UT to discuss future priorities for K-12 data science education. Organized through a collaboration between the Utah-based organizations Women Tech Council and Utah Tech Leads and our team at Data Science 4 Everyone, and generously hosted by Utah technology company Domo, the summit brought together leaders and practitioners from around the state, with the goal to ensure K-12 is aligning itself to the future of work. Hosted at Domo’s new headquarters in American Fork, UT, “Scaling Data Science in Utah” brought together nearly 100 industry, policy, K-12 and higher-education leaders to discuss the future of data science, math, CS, and technology education. Utah has been leading the country in both workforce training and academic achievement by a number of measures. The fast-growing “Silicon Slopes” technology sector has been drawing jobs and economic development locally for years, spearheaded through a number of entrepreneurial support initiatives like Talent Ready and The Point. The state’s new Profile of a Graduate highlights several 21st-century durable skills that are critical for the modern workforce. In math education, Utah was the only state nationally not to observe significant achievement declines from pandemic-related disruptions between 2019 and 2022. As a community, Utah is deciding not to stop there, but to push further. “We can’t sit back and stop. Our state is doing amazing things, our school board is doing amazing things, our staff is doing amazing things. Yet if we don’t get out in front of data science now, we’re going to miss it. Data science is everywhere. Every industry is looking at data science and AI, and if we don’t embrace it, we won’t be able to compete.” — Representative Jefferson Moss (R-51). Utah state leaders convened to ensure the state remains ahead on the digital frontier and the future of work with data science education. Several representatives from Utah industry emphasized that data science will be critical for the future of work, and that it goes far beyond the technology sector: “I am not a data scientist, I am a marketer. I need data literacy. In order to do any job well today, you need to be able to work with data. You need data literacy no matter what industry you are in.” — Wendy Steinle (CMO, Domo). “Data is the very first thing we go with, every time… When we acquire a company, we put a data dashboard on their operations, with a common language we can all agree to… We manage risks, make tradeoffs, and solve hard problems.” — Cyndi Tetro (CEO, Brandless) “Technology is changing so quickly, and I have never seen this in my career — all these tools that were once theoretical are now coming to fruition. The volume is so much that if you can’t communicate the vast amount of data to be consumable by decision-makers, there is no impact.” — Joanna Fankhauser (SVP, Instructure) “We’re going to see more and more data professionals, but those who don’t have a data background will struggle in any role in business. It really stands out if you’re not sure what to do with data. A really important conversation in education is how everyone can get a bit of exposure, to ensure their future career success” — Amy Heinrich (VP of Data, Pluralsight) Left to right: Mark Maughan (Chief Analytics Officer, Domo), Anna Bell (University of Utah), Amy Heinrich (VP of Data, Pluralsight), and Joanna Fankhauser (SVP, Instructure) speak from industry and academia on the need for data education in K-12. This translated to a clear call to action for education: “All educators should help their students in problem-solving and explaining [data]. I also think students need to know which variables to create, which models to select, and practice the creativity of how to solve novel problems — to create new insights with data” — Amy Heinrich (VP of Data, Pluralsight) “Ethics will be critical… especially with access to ChatGPT, people who can help shape policy, but also serve as consultants to governments and business, will represent a really big change over the next 10 years.” — Anna Bell (University of Utah) “We want to build the skills in our students, so they can build the workforce of the future themselves.” — Wendy Steinle (CMO, Domo). In a world of changing tools, the conversation also centered on the deep interconnection between AI, computer science, and data — and how to make these many fast-changing disciplines feasible for K-12 schools and districts to take on. What will change, and what will grow or remain more permanently? Mark Maughan (Chief Analytics Officer, Domo), who moderated an industry panel, shared that “a lot of organizations are trying to build AI into their products… In the recent conversations with ChatGPT, there is deep connective tissue between data and AI — obviously AI is driven by data, and is the foundational piece of it… Without that data, we won’t get anywhere.” Joanna Fankhauser specifically noted that “people get fearful when they tie their career to a particular tool or technology (e.g. Python, SQL, etc.), especially if something is automated. But if you say, ‘I am going to use data to solve problems,’ there are so many different avenues you can leverage. There will always be that opportunity.” A number of speakers and attendees share their personal experiences with data — and how formal education opportunities would have been transformational if they began earlier. Anna Bell, a graduate student in Data Science at the University of Utah, shared that “if I were in high school now, any of my prior interests could have been important in some type of data science project… I would have been more interested in math and science. I had a lot of interests, but they weren’t relevant in anything in the standard classroom subjects,” emphasizing that earlier exposure could help students understand their interests and make better career choices. Patty Norman, Deputy Superintendent of Public Instruction for the Utah State Board of Education, shared that, “I was a great follower… a quiet student who could memorize well, and who loved the stability of Step 1, Step 2, Step 3. Those types of students were rewarded with Straight As. We’re in a world today where students need to think for themselves, and think about the information they encounter.” Former Utah graduates — including a data scientist at NASA and a mathematics instructor at Harvard — shared what they see as emerging in this field, including projects from detecting exoplanets beyond our solar system to making air-traffic control and U.S. airspace safer for passengers: “Data science wasn’t even on my radar in college, much less high school… yet whether you plan on having a career in DS, or a career in something else, it’s important to know about — how models are trained, how much data goes into the models, and the ethics around it all.” — Daniel Weckler (Associate Data Scientist, KBR, NASA AMES Research Center) “I think we need to get more rigorous in our mathematics thinking. Rigor is about better understanding their world — few students see the math class as rigorous in that way… our current curriculum fails to give them the tools they need to engage with problems that matter.” — Brendan Kelly (Director of Introductory Mathematics, Harvard University) Brendan Kelly, Director of Introductory Mathematics at Harvard University, spoke on the need to re-align definitions of mathematical rigor to the 21st century. Utah schools and districts have already begun early efforts to bring these opportunities to students statewide, building upon earlier work to create computer science education at-scale. They focus on infusing data science and AI into existing school subjects, like mathematics. For the 2023–2024 school year, the State Board of Education launched an opt-in pilot program for K-12 data science high school courses — meant to serve as an additional 3rd or 4th year mathematics option and a bridge to college. 18 schools are participating in the inaugural cohort. Lindsey Henderson, Secondary Mathematics Specialist for the Utah State Board of Education, moderated a discussion with the school leaders, teachers, and students who began these programs this year. They collectively shared what they hope their classes will learn, and why they chose to invest the time and energy to learn data science: What do you hope students will learn? “I hope they get to experience a little bit of what those in the industry experience each day. I also hope they become more data-wise — there is so much data thrown at them through social media and the news. I want them to be more careful consumers of the information and data they see.” — Ashley Salisbury (Davis School District) “I think data literacy is critical. I teach in a Title I school, and many students don’t see math in their real world. I think Data Science can provide a door for them to use math” — Tyler Haslam (Granite School District) “We want students to be literate — part of being literate today is being data literate. Big data surrounds us, and data about us personally is online. By engaging in Data Science courses, students will walk away much better prepared to understand and engage in the world around them.” — Nicole Berg (Nebo School District) Were students or teachers intimidated by the “C-word” (coding)? “I told fellow teachers and students that they’ll be ok. It’s much like using your calculator, just a lot more powerful, and lets you do even more mathematics.” — Tyler Hassam (Granite School District) “Only one of our teachers had prior coding experience. Everyone is jumping in and is excited about it.” — Nicole Berg (Nebo School District) “We have a broad range of students engaging in our data science courses. We have students enrolled who took Calculus, and we have those who are working with an IEP (Instructional Education Plan)… these courses create a level playing-field in the math education space where every student can engage and learn.” — Nicole Berg (Nebo School District) Left to right: Lindsey Henderson (Secondary Mathematics Specialist for the Utah State Board of Education), Ashley Salisbury (Davis School District), Tyler Hassam (Granite School District), and Nicole Berg (Nebo School District). Each of these educators chose a different curriculum to follow — one of the many listed online at the DS4E Resource Center — but all focused on imparting a technology tool in synthesis with mathematics and statistics that translates to a real-world application. Are students enjoying your programs? “I had a prior coding background, and I know data science is clearly the future… I want to be a part of that and begin learning it now.” — a high school student panelist. “I am a first generation college student and the first to go in my family. I enrolled in the course to earn a required math credit, but it’s been unlike any other math course I have taken… it is actually fun.” — a high school student panelist. “Data science isn’t hard or easy… it’s a way to challenge yourself and grow” — a high school student panelist. “I like NOT getting the question of, “when am I going to use this?” — Ashley Salisbury (Davis School District) The early efforts are paying off. Superintendent Syndee Dickson of the Utah State Board of Education shared that over one fourth of school districts in Utah now have a teacher prepared to teach data science this school year. Deputy Superintendent (left) and Superintendent Syndee Dickson (right) of Public Instruction for the Utah State Board of Education share updates on the K-12 Data Science state pilot program. Yet much work and investment is needed to grow from pilot to scale. One policy leader noted that computer science “has only begun reaching the furthest corners of the state. Eight years ago, industry came together and said we don’t have the people to fill jobs, or enough people with the skills, and we’re willing to put up the funding to match what the state does. We need to keep building these programs, not just in college, but in the earlier years. We need to take it much further than that.” “I think data science education is at the very beginning, and it’s exciting we get to build this now — we get to unlock students’ talents and use them in ways they might not have thought possible,” Cyndi Tetro, CEO of Brandless and Founder of the Women Tech Council, concluded. “Data science is the next wave that unlocks so much critical thinking and interconnection to all of the emerging technologies: cybersecurity, UX, AI, etc.” For the next wave to empower every student, it will take concerted effort, investment, and collaboration to ensure anyone can graduate data literate, and be ready for the modern workforce. With continued coordination, Utah has an opportunity to lead the country in furthering the digital frontier, and to serve as a model nationally. Teachers participating in the Utah SBOE’s Data Science pilot were recognized at a statewide education summit in American Fork, UT.

High school students compete to predict which colleges “pay off” the most In the information era, data is quickly becoming currency. While 8 out of the 10 fastest-growing careers all stem from data science, navigating complex data with ease is increasingly critical for everyday decision-making – from healthcare to personal finance. Regardless of career or life path, every high school graduate will benefit from a strong upskilling in the basics of data. The technical skills, iterative problem-solving, and mathematical foundations of data science will empower students to take on the digital age with confidence. In collaboration with Data Science 4 Everyone at The University of Chicago, North Carolina State University Data Science Academy, and the CourseKata platform, Skew the Script – a nonprofit education initiative led by a collaborative of AP Statistics teachers – organized a national Data Science challenge for high school students, following their AP examinations. This past school year, approximately 2,500 students from around the country entered the competition to predictively evaluate a critical life question for many graduates: which colleges or universities will give the greatest return on financial investment, or “pay off” the most? The prompt challenged students to build the best possible model for predicting student loan default rates at different colleges across the country. An additional 4,000 students were waitlisted due to server capacity constraints, which we hope to build extra capacity for in future years’ competitions to accommodate. Do colleges “pay off? Lily Larsen, whose final model placed third in the challenge, was motivated to participate because she hopes to pursue biostatistics in college, and saw the challenge as an exciting intersection between coding and statistics. “It was something that piqued my interest initially; it was just an enjoyable experience overall.” Similarly, Prahit Yaugand, whose model won first place, wanted the opportunity to practice using the programming language R. Leveraging data from the U.S. Department of Education’s College Scorecard and IPEDS portals, students filtered, analyzed, modeled, visualized, and communicated complex data on U.S. colleges and universities from as recent as last school year. The mission: conquer the college debt question, maximizing financial return and minimizing the likelihood of defaulting on student loans. Lily’s teacher at Essex High School, Stacey Anthony, thought the prompt was timely and highly relevant to high school students. “We see it on the news all the time – the issues with student loan debt and what’s been going on at the national level with loan forgiveness.” She believes students have to weigh all the data and make the right choices for themselves. The Department’s College Scorecard data is large and complex: students were tasked to navigate a portion of the data, focusing on 26 different variables across 4,435 colleges, and encouraged to carefully choose which information was valuable to include and exclude in their final submissions. Most students had minimal programming experience – having recently completed an AP Statistics course. Others had minimal statistics experience – having recently completed an AP Computer Science course. And they had two weeks, at most, to find a good answer. Despite these challenges, over 75% of student participants completed all components of the project. The work and engagement from students demonstrates that even highly technical data science skills are still accessible for high school students. Furthermore, when those skills are used to analyze problems that are genuinely relevant for students, they’ll engage deeply with the investigation process. For Prahit, figuring out how to effectively combine different models was the most rewarding and challenging aspect. “I had so many ideas, including using a subset method and using different degrees, so I had to find a way to combine all my ideas into one central final model.” And mistakes also led to deeper understanding – Lily made a mistake in one of the notebooks she was working on, but going back through her work to see where she went wrong ended up giving her a much better understanding of that notebook. For students to succeed in the challenge, mastery of linear regression, polynomial regression, the basics of machine-learning (artificial intelligence), and the intuition of calculus were all required. Using Jupyter Notebooks and R, an open-source statistical software, students engaged in complex data analysis techniques using digital technology, advanced algebra, and complex statistical techniques. For students to succeed in the challenge, mastery of linear regression, polynomial regression, the basics of machine-learning (artificial intelligence), and the intuition of calculus were all required. Prahit’s teacher, Bellamy Liu, says that using real-world data and thinking about how data science is used in students’ own lives is the right approach to data science education. And throughout the challenge, students often collaborated with each other on those real-world questions, which Stacey Anthony was thrilled to see with her students. “Being a little more skeptical, asking questions, figuring out where numbers come from, and how data is collected would help us all become better consumers of data in the media,” Stacey said. Among students from over 35 states, 75 schools, and entering from a range of academic backgrounds – including course-completers in AP Statistics, AP Computer Science, and AP Calculus – we are pleased to announce our National Data Science Champions: Note: All students submitted their work anonymously for evaluation. Student names listed as ‘anonymous’ chose to remain anonymous or have not yet submitted permission forms. This post will be updated as permission forms continue to come in. The top prediction model was submitted by Prahit Yaugand of Mission San Jose High School. Prahit’s model had a final test R^2 value of approximately 0.78. This means that, when predicting student loan default rates for new colleges (schools that the model hadn’t “seen” previously), the model explained roughly 78% of the variation in default rates. This is quite impressive! Here is Prahit’s final model: We’d also like to congratulate all the runners-up of the challenge, who submitted highly predictive and accurate models for the same task: Elliott Salpekar, Ithaca High School Junyoung Sim, Ithaca High School Kenneth Tsay, Buckingham Browne & Nichols School Leo Ren, Buckingham Browne & Nichols School Lei Cao, Ithaca High School Nikol Miojevic, Ithaca High School Ashley Park, South High School Jaya Kolluri, Winsor School Gunnika Singh, Fort Zumwalt West High School Xander Black, Ithaca High School Michael Perelstein, Ithaca High School Ira Geller, Baltimore Polytechnic Institute Cosima Billotte Bermudez, Baltimore Polytechnic Institute Abigail Hartman, Baltimore Polytechnic Institute Dhilen Mistry, Westlake High School Diya Muni, Freedom High School Jamie Wong, Mills High School Anonymous, John D. O'Bryant School of Math & Science Sam Him Yuan, John D. O'Bryant School of Math & Science Alexandros Lambrou, Ithaca High School Curan Palmer, Georgetown Day School Chengwu Meng, Mills High School On behalf of the national organizers, we congratulate each student team who participated in the challenge – choosing to dedicate two weeks at the end of the school year, when time and perseverance are the hardest to find. Each and every one of you gained valuable skills that will carry with you for the next several years, and long into your career – in addition to hopefully learning how to navigate the college landscape. Thank you for your efforts, and we know this is just an early preview of what you will accomplish.

Data science and statistical reasoning must be part of what all students learn The calculator has replaced the slide rule. Latin is rarely offered in high school. Sentence diagramming has disappeared from most English classes. Academic disciplines continually evolve to reflect the latest culture and technology. Why, then, are recent attempts to tinker with the high school math canon eliciting such a backlash? Students deserve a chance to learn up-to-date topics that reflect how mathematics is being used in many fields and industries. Read more of Pamela Burdman's (executive director of Just Equations, a California-based policy institute focused on the role of mathematics in education equity) Op-ed in The Hechinger Report here.

California becomes the 17th state to back introductory Data Science in K-12. The country's most populous state is now recommending that all students learn introductory data science in high school, and graduate with a strong data literacy to be prepared for the 21st century. On July 12, the California State Board of Education adopted the state's new Mathematics Framework: a guidance document that provides recommendations to schools and districts for how to implement K-12 mathematics credit requirements, and for how to go beyond the required two years of mathematics in high school. The Framework garnered national attention (and controversy) on a range of issues since its first draft in 2021 - including the timing of Algebra 1, pedagogical approaches for teaching elementary math, and the practice of student "tracking" in middle school. While generating numerous debates, the Framework's efforts to modernize content and teaching strategies for mathematics in the 21st century centered on incorporating introductory Data Science, including: Adds a dedicated "Chapter 5: Mathematical Foundations for Data Science" Adds a "Content Connection" for "Reasoning with Data" (CC1) Recommends guidelines for integration of Data Literacy in Grades K-10 Recommends guidelines for a one-year Data Science capstone course in High School The SBE recommends that schools "encourage students across age spans to become proficient at understanding and using data—a key skill in the 21st century job market." The Framework goes as far to recommend foundational Data Science and Statistics learning as essential for every mathematics course and sequence: "data science can and should be integrated into math instruction across the grade levels, from elementary school through high school, regardless of which pathway a school has selected." (see Chapter 8). Several national press outlets covered the Framework adoption, and the addition of Data Science, including EdWeek, the NYTimes, and the LATimes. While some centered on a narrow debate over Algebra 2 vs. Data Science - which DS4E Coalition members believe is a false tradeoff and impacts less than 1% of students - the addition of Data Science across the Framework was strongly supported by CA education leaders, education researchers, and teachers: "The K-12 framework’s emphasis on data science proved popular during Wednesday’s public comment on the document, with several teachers, professors, and former California public school students speaking in favor of expanded pathways (Edweek)." It also mirrors momentum in higher education for the discipline, with the University of California Berkeley recently launching an entire college dedicated to Data Science, the first new college at Berkeley in over 50 years. Regardless of post-secondary pathway, the new Framework recognizes that all students will need a strong data literacy by high school graduation. Public comments supporting K-12 Data Science programs during the Framework proceedings can be found here. Examples of advocacy for creating modernized math experiences to incorporate Data Science can be found here, here, here, and here. DS4E looks forward to aiding schools and districts in California, and around the country, in implementing high-quality and challenging data science education programs for students to be prepared for the 21st century.

8+ states host summer institutes for teacher learning in data science As U.S. education begins to respond to the era of Artificial Intelligence (AI), educators were gearing up to teach the fundamentals fueling these emerging technologies: data. This summer, teachers in nearly every part of the country assembled to tackle these subjects head-on, together. The mission: how to incorporate modern technology, data analysis techniques, and 21st-century problem-solving into the existing K-12 curriculum — that will prepare today’s students for the world they will graduate into tomorrow. From state to state, teachers joined practicums — in some cases boarding together on the same campus — to tackle these new technologies head-on. In many cases, they broke precedent or created entirely new programming for educator professional learning. Every program focused on peer collaboration. The North Carolina School of Science and Mathematics — a public STEM school in Morganton, North Carolina — was the first high school in the country to require data science for all of its graduates. As part of their summer institute, they shared how they adopted college-level data science courses from the University of California Berkeley, with 50 teachers joining from around the state. How were so many summits “suddenly” planned and organized? In North Carolina, a STEM school that began offering data science in 2019 invited teachers from around the state, sharing lessons learned and workshopping new program designs. In Virginia, the State Department of Education facilitated a weekly teacher cohort throughout the entire summer to prep for this coming school year, building upon course and standards development from the prior two years. In Arizona, the University’s Data Science Academy invited teachers for a series of workshops on how to adapt introductory data science to K-12 students. In each state, training opportunities may have originated differently, but all arrived at the same conclusion: data is now fundamental, and will only become more important for students to know deeply. Convenings were organized in AZ, MA, MI, NC, OH, OR, UT, VA, and virtually, in addition to countless locally-focused or district-sponsored programs. Stated another way, we have moved from research & innovation to early scale for data science education in K-12. In collaboration with the Oregon Department of Education, the Central Oregon STEM Hub and High Desert Education District hosted the inaugural “Oregon Summer Math Institute” to bring over 80 teachers from around the state, including from rural communities. Boarding in the Community College’s dormitories for a week, educators worked through data teaching intensives to learn multiple curricula programs and design options, including several data science courses, statistics, and quantitative reasoning programs. While ChatGPT is new, these programs benefit from drawing upon over a decade of research and implementation for K-12 data science programs, and an even richer education research literature for statistics, mathematical modeling, and computer science education. They also coalesce research surfaced from the National Academies of Sciences, to the American Statistical Association, to the Friday Institute for professional learning and classroom practice. Most important is why teachers are jumping in. Our team was lucky enough to tag along on just a few of these convenings and workshopped these subjects together with anyone who joined. Each time, we asked teachers directly their “why” — why they chose to spend their summers learning new technology and a decidedly challenging subject. These were just some of the reasons we managed to capture: “I think it’s important for my students to understand these new technologies, and to understand why mathematics is so important for them.” “My students enjoy learning math through authentic problems.” “I want to be doing something innovative for my students.” “I have students who start small businesses or help run their families business after they graduate. They need data analysis skills to start and to compete today.” Many reasons. A clearly changing world. A mission to help ensure U.S. education is relevant, in the context of technology that can automate everything: from coding to writing to calculating. Teachers are finding ways to prepare our students regardless. We need the whole system to move with them.

Are you going to SXSW EDU in Austin next year? We can’t wait to see you there! Voting for SXSW EDU 2024 sessions is now open through August 20. Whether you’re attending the conference in person or not, SXSW EDU wants to hear from YOU as they pick next year’s programming – and Data Science 4 Everyone needs your help! How to Vote Voting takes about a minute! Log in or create a SXSW PanelPicker account. Use the links for each session, and vote for as many sessions as you like! Our Sessions Cast your vote for our expert-led sessions on accessibility in data science education, global data science innovation, AI-enabled edtech, expansive career opportunities with data science, and an engaging meet up on teaching math in an AI world. Learn more about our sessions – and when you cast your vote, let us know in the comments why you’re excited to hear about the future of data science! Making Data Science That’s Really for Everyone: Join this panel for a conversation about how we’re making data science education that’s really for everyone. From accessible tools to approaches that celebrate disability as a fundamental component of diversity, the leaders in this panel are transforming who can thrive in the data future. VOTE HERE Strength in Borders: Sourcing Global Data Science Innovation: Born from global collaboration, data science education advocates leveraged global connections to accelerate a new field from its inception. From the first-ever cross-country K-12 learning framework to international exchange of research and curricula models, join us to learn how going global made data science education a reality and what lessons we can learn from our partners across the globe. VOTE HERE EdTech vs. TechEd: Making Emerging Technology Accessible: With AI changing the skills students need for tomorrow, learners must understand the technologies underneath emerging edtech, rather than simply using them. Join this panel for a discussion on the tools for content delivery, assessment, and instructional support that meet next-level learning experiences and what teaching about, and not just with, technology offers a post-ChatGPT world. VOTE HERE Putting Data Science to Work: Join this panel to discuss how leaders in career and technical education, industry, and post-secondary education are incorporating data science into diverse pathways that benefit all learners. With options in K12 career education, corporate development, and certificate programs for a rapidly changing workforce, learn how we’re putting data science to work in the data future. VOTE HERE Meet Up: Teaching Math in the World of AI: Everyone knows that artificial intelligence is changing the ways we teach and learn. But these rapidly changing technologies are also serving as an inspiration and a call for action among math educators seeking to energize their teaching for the 21st-century – or to simply answer the many questions from their students. From incorporating data science concepts to ensuring that math includes connections that are meaningful for students, the options are endless. Join this meetup to collaborate, ask questions, learn from each other, and come away ready to take on teaching math in an AI world. VOTE HERE

From the Data Science Collaboration Challenge to events that feature leaders and innovators across data science and computer science education, collaboration drives the work we do and the partnerships we form at Data Science 4 Everyone. We’re committed to collaboration because the data revolution is strengthened and spurred forward when we work together. In this spirit of collaboration, we’re thrilled to partner with DataCamp for DataCamp Donates, an opportunity for people – K-12 educators in particular – to access free data science training opportunities! This scholarship opportunity offers 410+ courses on the latest technologies, practice exercises to sharpen your skills, assessments to test your knowledge, and projects that stem from real world queries. You'll even get access to extra DataCamp features like Workspace, Certification, and Jobs! This is an over $399 USD value. DataCamp Donates is making this offer available because accessible data training can bridge the skill gap and make strides in data science education. If you’re interested in this opportunity, fill out this form to determine your eligibility.