15 Data Science Pre-College Programs for High School Students
Data science pre-college programs can help you build foundational skills in programming, statistics, and data analysis while you are still in high school. Many of these programs combine technical instruction with projects and research activities that introduce you to how data is used across fields like healthcare, business, public policy, and artificial intelligence. You can also gain exposure to academic research environments and collaborative problem-solving while exploring whether data science is a field you want to pursue further.
What are the benefits of a data science pre-college program?
These programs help you build a strong foundation in skills such as data cleaning, visualization, statistical reasoning, and basic machine learning concepts. You also explore how data science connects to fields like business, public policy, healthcare, and artificial intelligence, which can help you refine your academic direction. Working on projects teaches you how to translate raw data into meaningful insights while strengthening your ability to communicate findings clearly. Exposure to practical applications gives you a better sense of how data scientists think and operate, which can inform future coursework, internships, or research interests. For students considering STEM or quantitative majors, this kind of experience adds both clarity and depth to your college preparation.
To make your search more targeted, we’ve narrowed down a list of 15 data science pre-college programs.
If you’re looking for data analytics programs, check out our blog here.
1. University of Chicago’s DSI Summer Lab
Location: John Crerar Library at the University of Chicago, Hyde Park campus, IL
Cost/Stipend: No cost
Acceptance rate/cohort Size: Highly selective
Dates: June 15 – August 7
Application deadline: January 12
Eligibility: Current high school seniors starting college in the fall and residing in Chicago; Applicants familiar with at least 1 programming language are preferred
The DSI Summer Lab places you inside an active research environment where data science is used to address questions across fields such as public health, social science, climate policy, and computing. You work alongside a mentor, become part of a lab team, and, as the program progresses, gain practical experience in research workflows, collaborative problem-solving, and domain-specific analytical methods. Professional development sessions and guest speakers add a broader context by showing how researchers communicate, collaborate, and build careers in the field. The data science pre-college program for high school students concludes with a symposium-style presentation in which you share the outcomes of your summer work.
2. Research Science Institute
Location: Massachusetts Institute of Technology campus, Cambridge, MA
Cost/Stipend: None
Acceptance rate/cohort size: 100 students per cohort
Dates: June 22–August 2
Application deadline: December 11
Eligibility: Open to high school juniors worldwide
RSI takes you through the full research process, from studying advanced concepts and reading technical literature to carrying out an individual project under expert mentorship. While the program spans many STEM areas, students with an interest in data science can pursue topics involving computational modeling, statistical analysis, or algorithmic research. The opening academic component lays the conceptual foundation you need before transitioning to independent work. During the research phase, you are expected to think critically, refine methods, and engage with the same kinds of questions researchers face in professional settings. Written and oral communication are central to the experience, helping you clearly explain your findings. The program concludes with conference-style presentations that align with the expectations of serious academic research.
3. MITES Summer
Location: MIT campus, Cambridge, MA
Cost/Stipend: None
Acceptance rate/cohort Size: Highly selective
Dates: Late June through early August
Application deadline: February 1
Eligibility: High school juniors who are U.S. citizens or permanent residents
MITES Summer gives you a fast-paced introduction to college-level STEM through rigorous coursework, collaborative learning, and exposure to real applications. Alongside core classes, you can explore topics in data science through electives in machine learning, genomics, and other computational fields. The academics are demanding, but it also helps you build habits that matter in advanced quantitative work, including problem-solving, persistence, and analytical reasoning. Outside the classroom, seminars, lab visits, and company tours show how technical skills are used across research and industry settings. The program also places strong emphasis on college preparation, with advising that helps you think more strategically about applications and long-term goals.
4. Carnegie Mellon University (CMU) Pre-College: CS Scholars Program
Location: Carnegie Mellon University campus, Pittsburgh, PA
Cost/Stipend: No cost
Acceptance rate/cohort Size: Highly competitive
Dates: June 20 - July 18
Application deadline: February 1
Eligibility: High school sophomores who will be 16 years old by the program start date and are U.S. citizens, permanent residents, or DACA recipients
This data science pre-college program for high school students introduces you to the core habits of computational thinking through an intensive mix of programming, mathematics, and collaborative project work. You begin by learning Python and the foundations of algorithmic problem-solving, which also serve as an entry point into more advanced work in data science and related fields. College readiness workshops run alongside the academic component, helping you think about admissions and financial aid. Exposure to faculty, researchers, and industry voices adds another layer by showing how computing is used in both research and professional environments. You wrap up the experience with a final presentation where you publicly share the work your team has developed.
5. Texas Tech University’s Anson L. Clark Scholars Program
Location: Texas Tech University, Lubbock, TX
Stipend: $750
Acceptance rate/cohort size: Highly selective; 12 students/year
Dates: June 21 – August 6
Application deadline: February 16
Eligibility: High school juniors and seniors who are U.S. citizens/permanent residents and at least 17 by the start date
The Clark Scholars Program centers on independent research, giving you the chance to work closely with a faculty mentor on a project shaped by your academic interests. For students drawn to data science, that can mean pursuing research in areas such as computational analysis, applied statistics, artificial intelligence, or other data-driven topics. The work is serious and individualized, so you get to engage deeply with methods, sources, and scholarly questions over the course of the summer. Weekly seminars and discussions broaden the experience by introducing larger academic and career themes, with field trips and enrichment activities to help place your research in a wider intellectual context. At the end of the program, you produce a formal written report that reflects the depth and rigor of your work.
6. Stony Brook University’s Simons Summer Research Program
Location: Stony Brook University, Stony Brook, NY
Stipend: Paid
Acceptance rate/cohort size: 5%
Dates: June 29 – August 7
Application Deadline: February 5
Eligibility: Students in their junior year of high school (11th grade) who are at least 16 years old by the start of the program
The Simons Summer Research Program places you in a university research group where you contribute to an active project under faculty guidance. Depending on your placement, your work may involve computational methods, mathematical modeling, coding, or data visualization alongside more traditional scientific research practices. The experience is designed to move beyond classroom learning, so you take on real responsibilities within a lab or research team. Weekly talks and workshops help you understand how researchers frame problems, communicate findings, and connect their work to larger academic questions. The program concludes with a written abstract and poster presentation that gives you practice presenting technical work to an academic audience.
7. Discovery Partners Institute – Digital Scholars Program
Location: Illinois Institute of Technology, Chicago, IL
Cost/Stipend: None
Acceptance rate/cohort size: Approximately 5%
Dates: June 22 – July 31
Application Deadline: May 8
Eligibility: Rising 11th and 12th graders; Preference is given to Black, Latine, women, gender expansive, and first-generation students throughout the Chicagoland area
The Digital Scholars Program combines college-level instruction with practical skill-building in areas closely tied to computing and data science. If you choose the data science track, you work with real datasets while learning how analysis, visualization, and programming support decision-making and problem-solving. Other available tracks broaden the experience by showing how software, hardware, and mobile development connect to the wider tech ecosystem. Workshops on subjects such as artificial intelligence, entrepreneurship, and machine learning help you see how technical concepts translate into current industry and innovation spaces. The data science pre-college program for high school students also gives attention to communication, teamwork, and professional identity. Further, daily interaction with Chicago’s tech community helps you understand how these skills are used beyond the classroom.
8. ASPIRE Program
Location: Johns Hopkins Applied Physics Laboratory, Laurel, MD
Cost/Stipend: None
Acceptance rate/cohort size: 7%
Dates: June 23 – August 21
Application Deadline: February 15
Eligibility: High school juniors or seniors (15+) with a minimum 2.8 GPA; U.S. citizens
ASPIRE places you in a professional research setting where you work on a full-time project with guidance from staff mentors at a major applied research laboratory. Students interested in data science can engage with projects involving coding, machine learning, algorithm development, systems analysis, or other forms of technical problem-solving. The work is hands-on, and expectations are high, so you are treated as a contributor rather than a passive participant. You need to manage your time, communicate clearly, and keep moving through challenges as your project develops. Because placements vary, the program also gives you a useful look at how data-driven work appears across fields such as cybersecurity, artificial intelligence, and engineering.
9. New York University – ARISE Program
Location: New York University Tandon School of Engineering, Brooklyn, NY
Stipend: $2,000
Acceptance rate/cohort size: Highly selective
Dates: Remote Workshops: June 1 – July 6 | In-Person Lab Work Start: July 6 – August 14
Application Deadline: February 27
Eligibility: Rising high school juniors and seniors; Full-time NYC residents attending NYC schools in the upcoming school year
ARISE introduces you to academic research through a structured progression from foundational training to hands-on work in an NYU lab. The early part of the program focuses on building readiness through workshops in research skills, ethics, writing, and safety, which helps you enter the lab phase with more confidence. Once placed in a research setting, you contribute to ongoing work that may include computational modeling, coding, machine learning, or other data-intensive tasks, depending on the lab. Communication remains an important part of the program, with support for public speaking and scientific presentation built into the structure. You finish by presenting your work in formal showcase settings.
10. MIT’s Beaver Works Summer Institute
Location: Virtual and in-person at Massachusetts Institute of Technology, Cambridge, MA
Cost: Free pre-requisite course; the summer program fee is zero for students with family income less than $150,000, and is $2,350 for other applicants.
Acceptance rate/cohort size: Not specified
Dates: Online Courses: Starts February 3 | In-person Summer Program: July 6 – August 1 or 2
Application deadline: March 31
Eligibility: High school students in grades 9 – 11 who live in the U.S. or will live in the U.S. during the program
BWSI is built around intensive, project-based learning, making it a strong option if you want to develop technical skills by building rather than just studying. Several tracks connect directly to data science and machine learning, especially those focused on language, healthcare, and intelligent systems. In these courses, you work with Python and related tools to analyze data, train models, and solve applied problems through team-based projects. This data science pre-college program for high school students puts focus on understanding systems from the ground up, so you are pushed to think carefully about how algorithms, data, and engineering choices fit together. Collaboration is a major part of the experience, with students using professional workflows and development tools as they build capstone projects.
11. Harvard University Pre-College Summer School Program
Location: Harvard University, Cambridge, MA
Cost: $5,800 + $75 application fee; Financial aid available
Acceptance rate/cohort size: Competitive
Dates: Session I: June 21 – July 2; Session II: July 5 – 17; Session III: July 19 – 32
Application Deadline: January 7 (early), February 11 (regular), April 1 (late)
Eligibility: Rising high school juniors and seniors (16-18)
Harvard’s Pre-College Program allows you to explore data science through a focused college-level course taught in a short but intensive format. In data-oriented offerings, you learn how programming, cleaning, visualization, and interpretation work together to turn raw information into meaningful analysis. The coursework often includes tools such as Python, web scraping, and visualization libraries, giving you direct exposure to the mechanics of modern data work. Small class settings also make the experience more discussion-based and interactive than a typical lecture-heavy model. You leave with a completed project and a better sense of what studying quantitative subjects at the college level can look like.
12. Quinnipiac University’s Pre-College Summer Programs: Data Sciences Lab
Location: Quinnipiac University, Hamden, CT
Cost: Residential: $3,600 | Commuter: $2,600
Acceptance rate/cohort size: 30 participants
Dates: July 6 – 17
Application Deadline: June 1
Eligibility: Current high school students ages 15–18
This data science pre-college program for high school students introduces you to the foundations of data science by connecting analytical methods to practical questions across sectors like business, healthcare, and public policy. The coursework covers how data professionals identify patterns, interpret evidence, and use quantitative tools to support better decisions. The learning environment combines lectures with applied work, so you get to use datasets to explore them directly. The curriculum also helps you see that data science is not only about coding, but about asking good questions and working through complex information carefully.
13. UPenn’s Wharton Data Science Academy
Location: The Wharton School at the University of Pennsylvania, Philadelphia, PA
Cost: $10,599 + $100 application fee; need-based financial aid available
Acceptance rate/cohort size: Not specified
Dates: June 21 – July 11 | July 12 – August 1
Application deadline: Priority deadline: January 28 | Final deadline: March 18
Eligibility: Students in grades 10 – 11 with strong math and coding skills, an interest in data analytics, and preferably some knowledge of statistics
Wharton’s Data Science Academy is one of the more academically rigorous options on this list, with coursework that reflects the structure and pace of advanced undergraduate study. You move from foundational skills such as data wrangling, probability, and visualization into more complex topics like regression, classification, and elements of machine learning. The instruction is highly applied, so lectures are reinforced through labs, case studies, and guided work with real datasets. R serves as the main language for much of the program, while Python is introduced selectively for specific advanced topics. Along the way, you also engage with questions around bias, fairness, and responsible data use. The program ends with a team-based capstone project that asks you to build and present a data-driven solution to a real problem.
14. Columbia University’s Pre-College Programs: Data Science and Machine Learning
Location: Columbia University, New York, NY
Cost: Varies by session and format; financial aid available
Acceptance rate/cohort size: Not specified
Dates: Multiple sessions available
Application Deadline: Varies by session
Eligibility: Domestic and international high school students aged 15+
Columbia’s Data Science and Machine Learning course gives you an introduction to how data-driven systems are built, interpreted, and applied across different industries. You work with Python while learning the basics of data analysis and machine learning algorithms in a format designed for high school students entering the subject for the first time. The data science pre-college program for high school students does a good job of connecting technical concepts to practical impact, showing how models shape decisions, systems, and everyday technologies. Coding exercises are paired with broader conversations about ethics, communication, and the responsible use of data. As the course progresses, you begin to see how statistics, programming, and clear presentation work together rather than as separate skills.
15. Analytics Academy Summer Pre-College Program
Location: Bentley University, Waltham, MA
Cost: Commuter: $2,450 | Residential: $3,380
Acceptance rate/cohort size: Not specified
Dates: Multiple 5-day sessions, residential and commuter options available
Application Deadline: June 1 (rolling)
Eligibility: Rising high school juniors and seniors
Bentley University’s Analytics Academy gives you a focused introduction to how data is organized, interpreted, and communicated in practical settings. Over the course of the program, you work through core concepts such as data types, correlation, visualization, and the role of ethics in analysis. A major part of the experience centers on Tableau, where you learn how to build visualizations that move from basic charts to more refined, insight-driven dashboards. The program also helps you think about data storytelling, so you are not only identifying patterns but also learning how to explain them clearly to others. Guest speakers add perspective on how analytics is used in academic and professional environments. The data science pre-college program for high school students concludes with a practical capstone project.
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