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The three-year battle over Californias new math framework has produced calamity and confusion on all fronts. As fighting raged across op-ed pages and X (formerly known asTwitter), the fog of war obscured the inescapable truth: The data revolution is here and our kids are not prepared for it.

From ChatGPT to personal finance, nearly every decision we make in our daily lives is now dominated by data. Eight out of ten of the fastest-growing careers this year involve data science. A decade from now, it will be difficult to find any job that is not data-driven.

We need to equip our students for this new reality by teaching them basic data literacy in K-12. We can all see this, but somehow the politics of the moment have turned this idea into a raging debate.

The new critics of data science instruction seem to have three common objections. Their first claim is that data science programs are somehow watering down math. That is indeed possible, especially if districts treat data-related classes as a form of remediation, but this should not be the case. Data science is a very challenging subject, combining traditional math, statistics, computer programming, and complex datasets. In many ways, it demands more of students, requiring critical thinking, creativity, and a nuanced understanding of the context within which data have been generated.

A second objection is that learning data science in high school is somehow illegitimate because students wont yet have the mathematics skills required of professional data scientists. This is an odd argument. Can high school students never learn anything about physics because they dont understand differential calculus? Can they not find beauty in a Shakespearean sonnet if they dont know the rules of iambic pentameter?

The third claim is that data science coursework will crowd out calculus or some of the other math required for college STEM degrees. This is an important concern, but it assumes that every part of todays curriculum is absolutely critical to that path. Do we really think that is true? Having spent many nights at the kitchen table helping my kids with their homework, I suspect its not. And we (parents) shouldnt ignore the more than 130 college disciplines that now require data and statistics basics as the world changesincluding math and engineering.

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We adults can stand around and dither, but young people are not waiting for us to figure this out. In college, students are rushing toward data science courses with astonishing speed. The number of data science undergraduate degree programs has exploded nationallyandin every state. At the University of Wisconsin, Madison, it has quickly becomethe fastest-growing major. Not to be outdone, UC Berkeley recently launchedan entire collegededicated to the subject. Our own institution, the University of Chicago, has hired 25 faculty in data science to keep up with student demand.

Sixteen other states have already officially launched or recommended data science in K-12. Some are creating full-year courses, while others are completely redesigning their math pathways. Leading STEM high schools throughout the country are teaching their students the UC Berkeley Data8 program, one of the best collegiate data science courses in the country. Just recently, a group of AP Statistics teachersorganizeda national data science challenge that attracted more than 5,000 students.

Without leadership from policymakers and educators, this revolution will still happen, but the benefits will go disproportionately to the students who are already advantaged. Wealthy parents and tech employees will teach their kids these skills through summer and after-school programs. Is this what we want? Or do we want to ensure that every child gets at least a basic level of data literacy?

If this all rings true to you, if you believe that a modern K-12 education requires at least some data science instruction, then you can help move us toward action. Ask your local school to incorporate data across school subjects throughout K-12. Ask your teachers to bring modern data tools into the way they teach. And ask your school leaders to offer data-focused math coursesand support their educators with the right resources to do so.

Lets put down our weapons in this math war and start fighting again for our kids futures.

StevenLevitt is an economist, the founder of The Center for Radical Innovation for Social Change (RISC) at UChicago, and the author of Freakonomics.

The opinions expressed in Fortune.com commentary pieces are solely the views of their authors and do not necessarily reflect the opinions and beliefs ofFortune.

This story was originally featured on Fortune.com

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Lawrence Livermore National Laboratorys Data Science Summer Institute (DSSI) hosted summer student interns from Japan on-site for the first time, where the students worked with Lab mentors on real-world projects in artificial intelligence (AI)-assisted bio-surveillance and automated 3D printing.

From June to September, the three students Raiki Yoshimura, Shinnosuke Sawano and Taisei Saida lived in rental apartments near the Lab and worked at the Lab on different data science projects using electronic health records and neural networks trained on experimental data.

Sponsored by Japans Agency for Medical Research and Development (AMED) and the Japan Science and Technology Agency (JST), the relationship between the Data Science Institute (DSI) and the Ministry of Japan stems from a series of agreements that arose in the wake of the Fukushima nuclear accident, to conduct academic exchanges and expand scientific collaboration. The DSSI opportunity began in 2019 but had to go all-virtual due to the COVID-19 pandemic. This summer was the first time that students from Japan were brought on-site.

DSI Director Brian Giera said the relationship is part of a continuing trend of Livermore positioning itself to partner with Japan via scientific connections and one the Lab hopes to expand in the coming years.

The Data Science Institute has found itself an example of the U.S.s posture in using science and technology to partner with geopolitically relevant allies, Giera said. Livermore is establishing itself as a global leader in data science, and its very clear that Japan has oriented in the direction of producing candidates that are highly attractive to us. The collaboration is showing that we are helping the students steer their curricular or scientific focus and sharing the messaging to academia [in Japan] that indeed, data science is relevant. Livermore can help be a partner in realizing that vision, and students are the boots on the ground of having that occur.

Yoshimura, Sawano and Saida had never been to the U.S. before arriving in Livermore for their internships. The DSSI program helped the students find housing and matched them with mentors based on the students technical knowledge. Although the students had almost no knowledge of LLNL prior to their internships (outside of photos on the Internet) and were challenged with cultural and language barriers, they were able to acclimate to working in a national laboratory environment and had successful experiences.

While the students said it took some time to get used to the Labs security procedures and American food, they said they found working with their mentors rewarding and eye-opening.

Sawano, a Ph.D. student at the University of Tokyo with a clinical background in cardiology and treating patients with cardiovascular disease, said he thought his internship was a perfect opportunity to combine his knowledge of data science and medicine. His work is supported in Japan by AMED, and though he didnt know what national labs did prior to his internship, he considers LLNL a fascinating option for a career after he finishes his Ph.D.

Working with his lead mentor, LLNL computer scientist Priyadip Ray, and Lab researchers Andre Goncalves and Jose Cadena-Pico, Sawano applied machine learning to electronic health records obtained from Kaiser Permanente, for a bio-surveillance project funded by the Department of Homeland Security. The Lab is developing AI tools to perform faster diagnostics to allow scientists to detect biological threats earlier, thus providing more time to develop possible countermeasures, according to Ray.

As these tools advance, and if we are able to look at the clinical record of everybody in this country in real-time, then we can detect these kinds of anomalies much faster, Ray said. That would give us more time for developing countermeasures.

Sawano said he hopes the research can someday make it into clinical practice.

Collaborating with the professionals on the team is a really good experience for me because Im usually analyzing data by myself and I check my data by myself, but in this team, Priyadip, Jose and Andre can check my coding and my results. As a result, our output is bigger than I do myself, Sawano said. We were really fortunate to get great mentors; they gave me a great deal of advice and support. It will take time to deliver our results to society, but Im excited about the potential output of our research.

Ray, who also worked with Yoshimura on a project using neural networks to evaluate and predict the impact of gene interactions on the viral load of the HIV virus, said the students did a remarkable job in contributing to advancing the research, which could have concrete impacts to public health in the future.

Yoshimura, who attends Nagoya University and studies biology, said he recently began using machine learning to predict clinical outcomes and was drawn to the DSSI internship to expand his skill set and apply his knowledge to large-scale datasets.

This internship experience has been great because they have very big data sets here that I can apply deep learning to, Yoshimura said. In Japan, we have to pay for data and the data is very little so I cant apply a graph or something like that.

Saida, who studies civil engineering in Japan and wants to be a university professor, worked with his mentor LLNL postdoctoral researcher Aldair Gongora on a project on self-driving labs for additive manufacturing, where machine learning approaches are used to help decide which experiments to do next to speed up manufacturing processes with the goal of eventually deploying these approaches on fully automated robotic systems.

There has been a big push toward autonomous experimentation or self-driving labs, and I think that our work really puts us in a position to continue contributing to those fields in a way that really adds value to the modeling and decision-making components of these systems, said Gongora, who works in the Analytics for Advanced Manufacturing group in the Materials Engineering Division. With all the tools that we now have in data science, its blurring the lines between data scientists, chemists, physicists and engineers.

Gongora said he found Saidas background in programming, machine learning and data science a perfect fit for the project and marveled at how his mentee was able to pick up algorithms and implement them at an extremely fast pace.

Saida said he learned how to use and program robots and integrate the hardware with the software on 3D printing machines, and that he found his internship valuable in expanding his knowledge of Bayesian optimization and mechanical engineering.

Its the first time for me researching outside of Japan, so its been a great experience to research with people in the U.S., Saida said. I had a really positive experience working on these projects, especially 3D printing.

During their summer in Livermore, the students explored the Bay Area, enjoying local pizza and ramen restaurants, seeing tourist attractions and even taking a trip to Yosemite National Park. They also attended regular DSSI community events, meet-ups and ice cream socials, where they got to know their fellow intern cohort.

The mentors said they found the experience just as valuable as the students did. Gongora, who came to the Lab as a foreign national himself, said he resonated with the cultural challenges the students faced and said the opportunity epitomized the strength of diversity in science.

The benefit for me has really been being able work with someone from another country and learning more about Japan; learning about their lives, how their academic journey differs from education here in the U.S., and really finding the commonalities and differences, Gongora said. I'm taking away a lot of new perspective leveraging the expertise that [Saida] was able to bring to the project, both in terms of how very skilled he was at the data science concepts and the brainstorming of new ideas from the civil engineering perspective. Time and time again, the Lab teaches me that it's really through the diversity in thought that these interdisciplinary and multidisciplinary ideas emerge, and I think were stronger for it.

Fellow mentor Ray added that the experience was an incredible opportunity for the Lab to get some of the best students from Japan and that he looks forward to mentoring more students from that country.

At the Lab, we are trying to solve very impactful and challenging problems, and we want the best teams to work on these problems, Ray said. There are communication barriers and cultural barriers, because many of them are coming for the first time, but once they're on site, I see them working very hard to overcome all the challenges and really contribute to the mission and push things forward.

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LLNLs Data Science Summer Institute hosts student interns from ... - Lawrence Livermore National Laboratory (.gov)

Python!No, R.Fools, its obviously Rust.

Many data science learners and experts alike are keen to pin down the very best language for data science. In my opinion, most people are wrong. Amidst the hunt for the newest, the sexiest, the most container-able data science language, people are looking for the wrong thing.

Its easy to overlook. Its easy to even discount it as a language. But the humble Structured Query Language, or SQL, is my pick for the language to learn for data science. All those other languages certainly have their place, but SQL is the one non-negotiable language that I consider a base requirement for anyone working in data science. Heres why.

Look, databases come hand in hand with data science. Its in the name. If youre working with data science, youre working with databases. And if youre working with databases, youre probably working with SQL.

Why? Because SQL is the universal database query language. There is no other. Imagine someone told you that if you just learned a specific language, youd be able to speak to and understand every single person on Earth. How valuable would that be? SQL is that language in data science, the language that everyone uses to manage and access databases.

Every data scientist needs to access and retrieve data, to explore data and build hypotheses, to filter, aggregate, and sort data. And hence, every data scientist will need SQL. As long as you know how to write a SQL query, youll go far.

Someone, reading this article right now, is piping up about the NoSQL movement. Indeed, certain data is now more commonly stored in non-relational databases, such as by key-value pairs or graph data. Its true that there are benefits to storing data like that you gain more scalability and flexibility. But theres no standard NoSQL query language. You might learn one for one job, and then need to learn an entirely new one for a new job.

Plus, you will very rarely find a business that works entirely with NoSQL databases, while many companies dont need non-relational databases.

Theres that famous (and debunked) stat about how data scientists spend 80% of their time cleaning. While its not true, I think if you ask any data scientist what they spend time on, data cleaning will rank in the top five tasks. Thats why this section is the longest.

You can clean and process data with other languages, but SQL in particular offers unique advantages for certain aspects of data cleaning and processing.

SQL's expressive query language allows data scientists to efficiently filter, sort, and aggregate data using concise statements. This level of flexibility is especially useful when dealing with large datasets where manual data manipulation would be time-consuming and error-prone. Compare that to a language like Python, where achieving similar data manipulation tasks might require writing more lines of code and dealing with loops, conditions, and external libraries. While Python is renowned for its versatility and rich ecosystem of data science libraries, SQL's focused syntax can expedite routine data cleaning operations, enabling data scientists to swiftly prepare data for analysis.

Plus, any data scientist will complain about the bane of their existence: missing values. SQL's functions and capabilities for handling missing valuessuch as using COALESCE, CASE, and NULL handlingprovide straightforward approaches to address gaps in data without the need for complex programming logic.

The other bane of a data scientists existence is duplicates. Happily, SQL offers efficient methods to identify and eliminate duplicate records from datasets, like the `DISTINCT` keyword and the `GROUP BY` clause.

Youve probably heard of ETL pipelines. Well, SQL can be used to create data transformation pipelines, which take raw or semi-processed data and convert it into a format suitable for analysis. This is particularly beneficial for automating and standardizing that repetitive data-cleaning processes we all know and hate.

SQL's ability to join tables from different databases or files streamlines the process of merging data for analysis is essential for projects involving data integration or aggregating data from diverse origins. Which, for a data scientist, comprises a majority of projects.

Finally, I like to remind people that data science does not happen in a vacuum. SQL queries are self-contained and can be easily shared with colleagues. This fosters collaboration and ensures that others can reproduce data cleaning steps without manual intervention.

Now, you wont get far in data science if you only know SQL. But happily, SQL integrates perfectly well with any other of the top data science languages like R, Python, Julia, or Rust. You get all the benefits of analysis, data viz, and machine learning while still retaining SQLs strength for data manipulation.

This is especially powerful when you think about all that data cleaning and processing I talked about earlier. You can use SQL to preprocess and clean data directly within databases, and then lean on Python, R, Julia, or Rust to perform more advanced data transformations or feature engineering, leveraging the extensive libraries available.

Many organizations rely on SQL or, more accurately, rely on data scientists who know how to use SQL to generate reports, dashboards, and visualizations that inform decision-making. Familiarity with SQL enables data scientists to produce meaningful reports directly from databases. And because SQL is so widespread, these reports are usually compatible and interoperable across almost any system.

Because of how interoperable it is with reporting tools and scripting languages like Python, R, and JavaScript, data scientists can actually automate the reporting processes, seamlessly combining SQL's data extraction and manipulation capabilities with the visualization and reporting features of these languages. The upshot is you get comprehensive and insightful reports that effectively communicate data-driven insights to stakeholders, all inside one place.

Theres a reason youll get asked a bunch of SQL interview questions at any data science interview. Almost every data science job requires at least a basic familiarity with SQL.

Heres an example of what I mean: the job listing says, Expertise in SQL, and R or Python for data analysis and platform development. In other words, SQL is a must. And then either R or Python, but one is as good as another to most employers. But thanks to SQL domination, theres no alternative to SQL. Every data science job will require you to work with SQL.

The really cool thing about it is that it makes SQL the ultimate transferable tool. One job may prefer Python, while a startup might require Rust due to personal preference or legacy infrastructure. But no matter where you go, or what you do, its SQL or bust. Take the time to learn it, and youll always be able to tick off a job requirement.

Ultimately, if you find a job as a data scientist that doesnt require SQL, youre probably not going to be doing a whole lot of data science.

It really comes down to the database. Data science requires the storage, manipulation, retrieval, and management of a lot of data. That data lives somewhere. It can only be accessed with one tool, normally, and that tool is SQL. SQL is the language to learn for data science and will be for as long as we rely on databases to do data science.

Nate Rosidi is a data scientist and in product strategy. He's also an adjunct professor teaching analytics, and is the founder of StrataScratch, a platform helping data scientists prepare for their interviews with real interview questions from top companies. Connect with him on Twitter: StrataScratch or LinkedIn.

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