15 Data Science Competitions for High School Students

As a high school student, finding opportunities to challenge yourself academically can be a great way to build new skills, and competitions can help you do just that! This can also be a chance to test your existing knowledge and skills. Contests and challenges designed for high schoolers typically allow you to work on a mock or real-world issue by coming up with a solution within a team of peers or individually. If your interests lie in emerging tech and quantitative fields, data science competitions are worth exploring. 

Why should you participate in a data science competition?

Data science challenges move beyond theoretical coursework, requiring you to work with high-level statistical models and utilize industry-standard software to solve unstructured problems. These events are often hosted by prestigious research universities and tech organizations, providing you with access to massive, real-world datasets, ranging from professional sports statistics to NASA research papers. This early exposure to the “data-driven” decision-making process is a significant differentiator for elite college admissions and future technical career paths. Additionally, as a participant, you will develop a pre-professional portfolio that demonstrates proficiency in predictive modeling, exploratory data analysis (EDA), and algorithmic efficiency.

To help you test your skills with the right opportunities, below we have shortlisted 15 data science competitions for high school students.

If you are looking for structured opportunities to build a foundation in data science, check out our curated list of high school programs here and research programs here.

15 Data Science Competitions for High School Students

1. University of Pennsylvania’s Wharton High School Data Science Competition

Wharton High School Data Competition is a free opportunity to use professional sports analytics and test your predictive modeling capabilities. Sponsored by Google Gemini, the competition is designed to help you build data analysis and prediction skills through the lens of sports. Your team will work with a simulated dataset from a fictional hockey league, using the regular season stats to build power rankings and predict how teams will do in a tournament. You will also identify key performance metrics and put together data visualizations to support your analysis. The competition runs in three phases: an initial submission, a semifinal in which you create a detailed slide presentation, and a final round in which the top five teams present live via Zoom to a panel of judges. If your team makes it to the end, you will compete to place first, second, or third, and to win trophies and free access to Wharton’s high school programs.

2. University of Virginia’s Data + Society Challenge

Location: Online submissions + winners’ event at University of Virginia (UVA) School of Data Science, Charlottesville, VA
Cost/Prizes: Free to enter; prizes include $500 in gift cards split evenly among winning team members + a lunch with UVA data scientists.
Competition dates: February – April 26
Registration deadline: April 26
Eligibility: 10th, 11th, and 12th graders residing in Virginia; individuals or teams of up to five can compete.

The Data + Society Challenge, hosted by the UVA School of Data Science, requires you to apply data science methods to a real environmental and social issue, specifically, analyzing conservation data tied to Virginia’s state goals for protecting natural land. You can enter solo or with a team of up to five people. The challenge is organized around four areas of data science: analytics, systems, design, and value, giving you and/or your team room to contribute in multiple ways depending on your strengths. If you win, you will receive gift cards and get to have lunch with data scientists at UVA.

3. Kaggle NFL Big Data Bowl: Analytics Track

Location: Online (hosted on Kaggle); finalists present in person at the NFL Scouting Combine in Indianapolis, IN
Cost/Prizes: Free to enter; $100,000 in shared prize money (track-wise amount varies by year)
Competition dates: Submission period: September 25 – December 17; Judging period: December 18 – January 19; Results announcement: January 20
Registration deadline: December 17
Eligibility: Open to all — students, professionals, and independent analysts with a Kaggle account (minors require parental consent); the University Track is restricted to undergraduate and graduate students only.

The NFL Big Data Bowl: Analytics is an annual data science competition run by NFL Football Operations, hosted on Kaggle, and powered by Amazon Web Services. The challenge asks you to analyze NFL player tracking data, called Next Gen Stats, to understand and predict how players move while the ball is in the air after a pass. You can work solo or as part of a team, and choose between tracks: a University Track for students or a Broadcast Visualization Track if you are interested in creating compelling visuals or animations. Your submission will be reviewed by actual NFL team analysts, and if you make it to the finals, you will present your work live at the NFL Scouting Combine. This is an opportunity to build real data science experience with professional-grade sports data and influence stats that you can see during live NFL broadcasts.

4. Kaggle NFL Big Data Bowl: Prediction Track

Location: Online (hosted on Kaggle); finalists present in person at the NFL Scouting Combine in Indianapolis, IN
Cost/Prizes: Free to enter; $100,000 in shared prize money (funded by AWS)
Competition dates: Submission of entries: September 25 – November 26; final submission deadline: December 3; forecasting timeline: December 4 – January 5; results announcement: January 6
Registration deadline: November 26
Eligibility: Open to all; minors require parental consent

The NFL Big Data Bowl Prediction track is a machine-learning competition run by NFL Football Operations, where you will build a model that predicts how players move after a quarterback releases the ball. You will have access to Next Gen Stats tracking data from before the snap up to the moment the ball is thrown, and your model needs to forecast each player’s movement while the ball is in the air. The NFL then evaluates your predictions against what actually happened during Weeks 14–18 of the season, scoring you on a public leaderboard based on accuracy. Unlike the Analytics track, this one is less about storytelling and more about the technical precision of your model. If your submission ranks among the best, you will be invited to present at the NFL Scouting Combine and get a chance to produce work that NFL teams actually use.

5. Rochester Pre-College Data Science Challenge

In this team-based competition, you will learn how to function as a data science consultant commissioned to resolve the “Two Crises” of environmental degradation and public health. You will use high-fidelity, open-access datasets from global authorities like the World Health Organization (WHO), NASA, and the CDC. Then, you will implement exploratory data analysis (EDA) and predictive modeling to uncover latent correlations between variables—such as climate-driven disease outbreaks or pollutant-linked respiratory patterns—and test hypotheses to engineer actionable policy solutions. The goal is to synthesize these findings into a 10-page scientific manuscript and a professional digital poster. The experience culminates in a final presentation at the Imagine RIT festival, where you will learn to pitch your data-driven innovations to a panel of academic and industry leaders.

6. Saint Joseph’s University’s Analytics and Data Visualization Competition

This competition, run by Saint Joseph’s University, challenges you to use a shared dataset, analyze it, and turn your findings into an interactive dashboard that tells a clear, audience-focused data story. You will present your work live to a panel of judges from academia and industry, who will score you on how well you communicate, how strong your analysis is, and how thoughtfully your visuals are designed. The competition runs in two rounds, so if your team advances, you will get judge feedback and have a chance to improve your dashboard before presenting again to a fresh panel. Special awards are given for standout dashboard design and creative use of AI.

7. MathWorks Math Modelling Challenge (M3 Challenge)

In this math modeling challenge, you will take on a specific real-world problem and find a way to solve it using math modeling and data. The competition is entirely internet-based. During a continuous 14-hour window, you will learn how to apply mathematical modeling to an unscripted, real-world problem. You can also use MATLAB to perform technical computing, focusing on the quantification of risk and uncertainty. Your submission must include a full report detailing your assumptions, mathematical derivations, and sensitivity analysis. This competition tests your ability to work with data tools and perform under extreme time constraints.

8. Modeling the Future Challenge by The Actuarial Foundation

Modeling The Future Challenge (MTFC) is an academic challenge hosted by The Actuarial Foundation with design and operations support from the Institute of Competition Sciences. As a participant, you will develop and present your own mathematical models that describe how you envision a specific technology or industry changing the future using the actuarial science approach to data. You will learn to analyze datasets through a Scenario Phase to identify potential risks in healthcare, transportation, or climate sectors. You will learn to develop risk-mitigation strategies using predictive modeling and critical thinking. Finalists are paired with industry mentors to refine a full-scale research project that integrates data science with strategic decision-making.

9. STEM Fellowship’s National High School Data Analysis and AI Challenge

STEM Fellowship, a Canadian nonprofit, runs this interdisciplinary research marathon designed to help you use computational thinking and open science methodologies to solve complex societal problems. You will learn how to source and clean extensive open-access datasets and utilize industry-standard AI tools to analyze variables within a specific annual theme, such as sustainable development or public health. You will learn the rigors of the formal research cycle, including drafting a peer-reviewed manuscript and designing a digital research poster. The program culminates in a national symposium where you will learn to distill complex data findings into a three-minute thesis (3MT) presentation for a panel of experts. Upon completing this challenge, you will gain exposure to the STEM Fellowship Journal publication process and see your analytical work published.

10. DrivenData Competitions

DrivenData hosts competitions at the intersection of data science and social impact. You can participate in competitions focused on building models for organizations like NASA and the World Bank, and learn how to solve problems like disease outbreak prediction or wildlife classification. You will learn how to utilize supervised and unsupervised machine learning to extract insights from raw text or image data, often using datasets that have a direct impact on public health and conservation. The competitions have provided $4,986,000+ in prizes in the past, drawn 253,000 submissions, and have led to solutions in climate change, health, and education. 

11. Kaggle’s March Machine Learning Mania 

Location: Online
Cost/Prizes: Free; prizes worth $5,000 to $10,00
Competition dates: Submission phase: February 20 – March 19; Results announcement: March 19 – April 6
Registration deadline: March 19
Eligibility: Open to all Kaggle users; typically, participants are over 18, but students under 18are  accepted based on competition sponsor approval and appropriate parental/guardian consent.

March Machine Learning Mania is Kaggle’s annual NCAA basketball tournament prediction competition. Your task will be to build a model that predicts the probability of one team beating another for every possible matchup in both the men’s and women’s NCAA tournaments. Rather than picking a single bracket, you will be submitting win probabilities across thousands of possible game combinations. You will have access to historical NCAA game data to train your model, and your predictions will be scored against actual tournament results as the games are played in real time. Winners earn cash prizes.

12. Kaggle WiDS Global Datathon

Location: Online (hosted on Kaggle)
Cost/Prizes: Free to enter; certificates and prizes of $2,500 – $3,000 available
Competition dates: January 28 – May 1
Registration deadline: April 24
Eligibility: Open to all worldwide; at least 50% of each team must identify as women, and teams of up to 4 participants are allowed. Typically, participants are over 18, but students under 18are  accepted based on competition sponsor approval and appropriate parental/guardian consent.

The WiDS Global Datathon is an annual data science competition run by Women in Data Science Worldwide. Your challenge will be to build models that predict wildfire impacts on infrastructure and communities, using real data from Watch Duty, a nonprofit wildfire tracking organization. You will work with geospatial data to forecast how wildfires spread and affect different populations, with an emphasis on equity and who is most at risk. The competition is designed to be accessible, whether you are just getting started with data science or already have some experience, providing you with skill-building workshops and mentorship.

13. Connecticut Sports Analytics Symposium Data Challenge

Connecticut Sports Analytics Symposium’s Data Challenge requires you to analyze Major League Baseball (MLB) pitch-level data. You will learn how to use advanced metrics like bat speed and swing length to answer research questions. You will also write reproducible reports using code and visualization libraries. You will submit your analysis, which will be judged on the basis of originality and the robustness of your statistical methodology. Finalists will be invited to present their work at a final event in April.

14. Skew The Script’s After The AP Data Science Challenge

Designed for the post-exam period, this project-based competition requires you to work with high-dimensional datasets from the U.S. Department of Education to identify which collegiate institutions offer the best financial “pay off.” You will learn how to implement multiple regression and machine learning algorithms using the R programming language and a Jupyter Notebook environment to forecast student loan default rates across more than 4,400 schools. By analyzing various distinct variables, including institutional expenditures, SAT averages, and socioeconomic indicators, you will learn how to perform rigorous feature selection and model tuning to maximize your R² value. This challenge bridges the gap between theoretical statistics and professional data science, ensuring you learn to communicate complex quantitative insights while competing for national recognition.

15. DataCrunch Hackathon Weekly Sprints

DataCrunch functions as a financial modeling sandbox where members get to learn how to build ranking models for U.S. stock returns. You can participate in its weekly spring to will utilize scikit-learn and time-series analysis to predict market performance using weekly-scraped financial datasets. Evaluation is based on the Spearman rank correlation metric, which measures the monotonic relationship between your predicted rankings and actual live market returns. The final payout is calculated based on the rank of your prediction for each target. The higher the Spearman Rank between the submitted prediction and market realisation, the higher your rank on the leaderboard will be.

One more option—Horizon Academic Research Program

If you are looking for a competitive mentored research program in computer programming, statistical analysis, and data science, you can consider applying to Horizon’s Research Seminars and Labs! It is a selective virtual research program that allows you to engage in advanced research and come up with a research paper on a subject of your choosing. Horizon has worked with 1,000+ high school students so far, offering 600+ research specializations to choose from. You can find the application link here!

Image Source – University of Pennsylvania