15 Statistics Summer Programs for High School Students
If you are interested in business, finance, or data-driven fields, statistics summer programs can help you see how classroom concepts are used in real situations. Statistics plays a role in pricing, investing, market analysis, and decision-making, and summer programs give you focused time to understand how data supports these choices in practice.
What are statistics summer programs for high school students?
In a statistics summer program, you may work with real datasets, analyze trends, interpret results, and present findings using tools like spreadsheets, statistical software, or basic coding. This hands-on exposure helps you understand whether fields like finance, economics, analytics, or entrepreneurship are a good fit for you.
Why pursue statistics summer programs for high school students?
These programs also build skills that matter beyond school, such as evidence-based reasoning and problem-solving. They strengthen college applications by showing initiative and quantitative engagement, while giving you clarity about your academic and career interests.
If you’re also looking for business analytics summer programs, check this out, or go here for data analysis programs for high school students.
With that, here is our curated list of 15 statistics summer programs for high school students!
15 Statistics Summer Programs for High School Students
1. Wharton Data Science Academy
Cost: $10,599; need-based scholarships are available
Location: The Wharton School, University of Pennsylvania, Philadelphia, PA
Program Dates: June 21–July 11 or July 12–August 1
Application Deadline: Priority: January 28; Final: March 18
Eligibility: High school students in grades 10–11 with a strong background in math and coding; prior statistics experience preferred
The Wharton Data Science Academy is a statistics- and data-focused summer program where you work through real datasets using standard analytical workflows. You study probability and statistics fundamentals such as distributions, confidence intervals, and hypothesis testing, then apply methods including regression, classification, and model evaluation. You work primarily in R, with limited use of Python for applied machine learning modules. The program is project-based and ends with a team capstone presented at a Data Science Live showcase. Instruction is led by faculty from the Wharton School, with support from undergraduate and graduate teaching assistants.
2. Young Founders Lab
Cost: Varies depending on program type. Full financial aid available.
Location: This program is 100% virtual, with live, interactive workshops
Program Dates: Multiple cohorts throughout the year, including summer, fall, winter, and spring
Application Deadline: Varies according to cohort. You can access the application link here!
Eligibility: The program is currently open to all high school students
The Young Founder’s Lab is a start-up boot camp founded and run by Harvard entrepreneurs. In this program, you will work towards building a revenue-generating start-up that addresses a complex problem. You will also have the opportunity to be mentored by established entrepreneurs and professionals from Google, Microsoft, and X. Apart from building the start-up itself, you will also participate in interactive classes on business fundamentals and business ideations, workshops and skill-building sessions, case studies, panel discussions, and more. The program is an excellent opportunity to delve into the world of business in high school and have a space to explore multiple theoretical as well as practical frameworks that lead to a successful business. You can check out the brochure for the program here.
3. Statistics & Data Science Camp for High School Students - Carnegie Mellon University
Cost: Free. Students will receive a free bus pass and a stipend
Location: Carnegie Mellon University, Pittsburgh, PA
Program Dates: June 22–June 26
Application Deadline: March 15
Eligibility: High school students from Pittsburgh and surrounding school districts only
The Statistics & Data Science Camp at Carnegie Mellon University introduces you to statistics and data science through applied problem-solving. You work daily in computer labs using R, focusing on core methods such as linear regression, similarity measures, and basic text analysis. The curriculum moves from data collection and measurement to modeling and interpretation, combining short lectures with hands-on labs. You apply statistical models to real datasets and learn how results are tested and interpreted. The program also includes an industry field visit to Duolingo, where you see how data science is used in production settings.
4. Ladder Internship Program
Cost: Varies depending on program type. Financial aid is available
Location: Remote! You can work from anywhere in the world.
Program dates: Multiple cohorts throughout the year
Application deadline: Deadlines vary depending on the cohort
Eligibility: Students who can work for 10-20 hours/week, for 8-12 weeks. Open to high school students, undergraduates, and gap year students!
Ladder Internships is a selective start-up internship program for ambitious high school students! In the program, you work with a high-growth start-up on an internship. Start-ups that offer internships range across a variety of industries, from tech/deep tech, and AI/ML to health tech, marketing, journalism, consulting, and more. Ladder’s start-ups are high-growth companies on average, raising over a million dollars. Past founders have included YCombinator alums, founders raising over 30 million dollars, or founders who previously worked at Microsoft, Google, and Facebook. In the program, interns work closely with their managers and a Ladder Coach on real-world projects and present their work to the company.
5. Stanford Pre-Collegiate Studies - Introduction to Data Science
Cost: $3,200. Financial aid is available
Location: Online
Program Dates: June 15–June 26 or July 6–July 17
Application Deadline: Rolling until filled
Eligibility: Grades 9–11; prior exposure to programming and working knowledge of statistics required
Introduction to Data Science through Stanford Pre-Collegiate Studies focuses on statistics and applied data analysis using real datasets. You work with data from the natural and social sciences to answer concrete questions through structured analysis. The course teaches probability, statistical reasoning, and basic machine learning methods through R programming. You learn how models are built, compared, and evaluated, with attention to assumptions, tradeoffs, and limitations. Coursework emphasizes clear interpretation of results, ethical data use, and repeatable analytical workflows supported by regular assignments and office hours.
6. Brown University Pre-College - AI, Data Science, and Machine Learning
Cost: $5,554 (online, 4-week course); financial aid is available
Location: Brown University; Online
Program Dates: June 22–July 17
Application Deadline: Applications open January 14
Eligibility: High school students with prior programming experience (Python or R) and intermediate knowledge of statistics and probability
In this course, you work as an applied data analyst, moving from structured data exploration and visualization to diagnostic analytics and predictive modeling. Statistics is central to the curriculum, with students applying probability concepts and statistical reasoning to evaluate models alongside machine-learning techniques. You use R or Python to build and interpret predictive models, explore text analytics and recommender systems, and assess tradeoffs in model performance. The course emphasizes the full analytics pipeline, including ethical considerations and data architecture design. Business-oriented case contexts, such as healthcare, hospitality, and sports, help you connect statistical modeling to real-world decision-making.
7. Columbia University Pre-College - Statistics Applications in Finance
Cost: Residential: $12,800, Commuter: $6,375
Location: Columbia University, New York City, NY
Program Dates: June 29–July 17 or July 21–August 7
Application Deadline: Rolling until courses fill
Eligibility: High school students with basic knowledge of statistics and probability
In this course, you work directly with real-world financial data, using statistics as a core decision-making tool rather than abstract theory. You apply probability, regression, and quantitative reasoning to analyze market behavior, assess risk, and explore portfolio-level trends. Coursework includes group projects, stock market simulations, and case studies, allowing you to test statistical concepts in realistic finance scenarios. The program emphasizes interpreting results, making predictions, and understanding the limitations of statistical models in volatile markets.
8. Wolfram High School Summer Research Program
Cost: $5,500; need-based financial aid available (up to 90%)
Location: Bentley University, Boston, MA
Dates: June 24–July 11
Application Deadline: Early Decision: Apply by January 18; Regular Decision: Apply by March 22
Eligibility: High school students aged 14–17 (exceptional 13-year-olds considered); strong math background required
The Wolfram High School Summer Research Program is a research-focused program where you work as an independent quantitative researcher. You design and carry out an original project centered on statistics, modeling, or simulation under close mentorship. You use Wolfram Language and Mathematica to analyze datasets, build statistical or algorithmic models, and test hypotheses. Each student produces a computational essay that documents their methods, analysis, and results, following standard research workflows. A short pre-program workshop is available for students who need preparation in statistical programming and computational thinking.
9. University of Chicago DSI Summer Lab
Cost: Free; students get a stipend of $4,800
Location: University of Chicago, Chicago, IL
Dates: June 15–August 7
Application Deadline: January 12
Eligibility: High school students from the Chicago area; no prior research experience required
The DSI Summer Lab at the University of Chicago is a research-based data science program where you work as a research assistant on a faculty-led project. You apply statistical reasoning and data analysis methods to real datasets in areas such as public policy, social science, climate and energy, or biomedical research. Projects emphasize core research skills, including hypothesis development, data cleaning, exploratory analysis, and interpretation of results. You work in a cohort model that reflects how interdisciplinary data teams operate in academic and applied research settings.
10. Johns Hopkins University Pre-College - Data Analytics Workshop
Cost: $1,950 per 1-credit program; financial assistance is available
Location: Johns Hopkins University; Online
Program Dates: June 22–July 3 or July 6–July 17 or July 20–July 31
Application Deadline: Rolling
Eligibility: Pre-college high school students; prerequisite of Precalculus; no prior programming experience required
The Data Analytics Workshop in the pre-college program at Johns Hopkins University is a statistics-focused course where you complete a full data analysis project in teams. You take a dataset from a collection and clean it through visualization and interpretation. The curriculum covers probability distributions, exploratory data analysis, hypothesis testing, and regression. Most analysis is done in Microsoft Excel, with brief exposure to tools like Octave (MATLAB) and Octoparse for data scraping and support tasks. The workshop is largely asynchronous, requiring independent deadline management, and is designed for students who want a statistics-based introduction to data analytics without heavy coding.
11. The Coding School x Columbia University: National High School Research Program
Cost: Varies by program; paid research opportunities are available for select programs.
Location: Online
Program Dates: Summer (exact dates vary by track)
Application Deadline: Rolling
Eligibility: All high school students, with track-specific prereqs (e.g., basic coding for AI)
Programs at The Coding School focus on applied statistics and data science through project-based work. Depending on the track, you analyze datasets, build models, and use statistical reasoning in areas such as artificial intelligence, big data, or quantum-related applications. The programs focus on data interpretation, modeling, and computational thinking, with guidance from researchers and industry professionals. Some tracks follow a research-style format, where you contribute to longer-term technical projects rather than short exercises. The focus is on how statistics is used in modern technical and research settings.
12. Data Visualization and Analysis – Syracuse University Summer College
Cost: Residential $2,795; Commuter $2,309
Location: Syracuse University (On campus)
Program Dates: July 19–July 24
Application Deadline: Rolling (subject to seat availability)
Eligibility: Rising high school sophomores, juniors, seniors, or recent high school graduates
This one-week, on-campus course introduces you to applied data analysis and basic statistics through visual methods, making it accessible even if you have no prior coding experience. You work with raw datasets to assess data quality, perform cleaning, and apply foundational statistical techniques to generate insights. The course begins with Microsoft Excel and progresses to Tableau, where you learn dashboard design principles, chart selection, and data storytelling frameworks. Emphasis is placed on interpreting data for decision-making and communicating findings to non-technical stakeholders. The program concludes with a final presentation event.
13. Data Sciences Lab – Quinnipiac University
Cost: Residential $3,600; Commuter $2,600
Location: Quinnipiac University
Program Dates: July 6–July 17
Application Deadline: Rolling; early payment discount available until January 15
Eligibility: Current high school students, typically ages 15–18
The Data Sciences Lab at Quinnipiac University is a two-week, on-campus program focused on statistics-based data analysis. You study probability, core statistical concepts, algorithms, and data ethics while working with real datasets. You use tools such as Excel, R, and ggplot to perform analysis and visualization. The curriculum includes applied machine learning topics like supervised learning and clustering, with projects such as survival analysis using the Titanic dataset. The program ends with a formal poster presentation.
14. NextGen DATA Leadership Camp – University of North Texas
Cost: $350
Location: University of North Texas (Frisco Landing, TX)
Program Dates: July 22–July 26
Application Deadline: Rolling; payment due by July 15
Eligibility: Rising 9th graders through high school seniors
The NextGen DATA Leadership Camp at the University of North Texas is a five-day, in-person program focused on applied data analytics. You learn core data science workflows with an emphasis on Python programming, text analysis, and basic interaction with AI systems. The curriculum progresses in difficulty across the week and centers on hands-on work rather than lectures. You practice applying statistical and computational ideas in team settings, reflecting how data projects operate in real environments. Instruction is led by UNT research faculty and PhD students, providing early exposure to academic and applied data science paths.
15. UC Santa Barbara Research Mentorship Program — Mathematics Track
Cost: Commuter $5,175; Residential $12,474; need-based scholarships available with priority for California residents
Location: University of California, Santa Barbara, CA
Program Dates: June 15–July 31
Application Deadline: March 17
Eligibility: High school students in grades 10–11 with a minimum 3.80 academic weighted GPA
The Research Mentorship Program at UC Santa Barbara pairs high-achieving students with faculty researchers working on active projects across multiple disciplines, including mathematics. In the mathematics track, you conduct original research in areas such as pure mathematics, applied mathematics, computational mathematics, or mathematical modeling, working directly alongside UCSB faculty, postdoctoral researchers, and graduate students. The program combines hands-on research experience with university coursework; you earn eight college credits through two required courses: Introduction to Research and Presentation Techniques. You invest 30–50 hours per week in research activities, including problem-solving sessions, data analysis, literature review, and lab work.
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