Interested in turning numbers into discoveries? Data science programs for high school students let you work hands-on with data and use it to tackle real-world problems. By using powerful tools such as Python, R, or SQL, you will learn how to analyze and visualize data, uncover patterns and make smart decisions, while applying statistical reasoning to practical challenges. These programs often combine coding with research, making them an excellent way to strengthen both your technical and analytical skills.
Fall is a perfect time to pursue data science—you can balance your coursework with structured projects that help you stay focused and disciplined. Many universities, research mentorship programs, and nonprofits now offer data science opportunities explicitly designed for high school students. Some are in-person, while others are remote, making them accessible regardless of where you live. Whether you’re fascinated by Machine Learning, Computational Biology, or Social Science datasets, fall data science programs for high school students allow you to develop concrete skills and get a head start on future academic research or internships in one of today’s most in-demand fields.
1. Science and Engineering Apprenticeship Program (SEAP) by the Department of Navy
Location: Various lab locations across the country, including the Naval Health Research Center (NHRC) in San Diego, California
Cost/Stipend: No cost; Stipend: $4,000 (new participants) | $4,500 (returning participants)
Program Dates: June – August (8 weeks)
Application Deadline: November 1
Eligibility: Open to high school students in grades 10-12 who are 16 years old by the start of the program and are U.S. citizens
The SEAP program is an eight-week program that allows high school sophomores, junior and senior students with strong STEM grades to work in a Department of Navy lab lab. As an intern, you’ll work alongside professional scientists and engineers in cutting-edge areas such as robotics, ocean engineering, cybersecurity, and advanced materials. The program is competitive, with around 300 students placed across dozens of Navy labs nationwide each year – including several in California. Interns work full-time, and some may even extend their tenure for an additional two weeks.
2. Aspiring Scholars Directed Research Program (ASDRP)
Location: Bay Area, CA and remote Cost: $1,070 per term (No Stipend) Program Dates: Autumn: September 1–January 15; Spring: January 16–May 30; Summer: June 1–August 30 Application Deadline: Autumn – July 15 (priority), August 15 (final); Spring – November 15 (priority), December 30 (final) Eligibility: High school students in grades 9–12; no prior research experience required
The Aspiring Scholars Directed Research Program (ASDRP) offers research mentorship in STEM to high school students who are passionate about science and discovery. Many projects in ASDRP focus on data science, but you can also explore areas such as synthetic biology, computational chemistry, environmental science, or applied machine learning. You’ll join a lab group and work evenings or weekends on computational research, gaining skills in coding, statistical modeling, and experimental design. Students also learn to analyze data using Python or R, interpret results, and prepare findings for presentations or publication. Admission to ASDRP is highly competitive due to the limited number of positions and high demand.
3. Foundations of Data Science & Machine Learning – The Coding School
Location: Virtual Cost: $995 (No Stipend) Program Dates: September–December Application Deadline: Rolling until cohort fills Eligibility: High school students (18 and under); no prerequisites required
Foundations of Data Science & Machine Learning, offered by The Coding School, is a semester-long course that introduces you to Python programming and the fundamentals of machine learning. You’ll learn data analysis, build simple machine learning models, and create visualizations that translate raw data into meaningful insights. Along the way, you’ll take part in hands-on coding, explore applications of AI and even discuss the ethical implications of AI, and interactions with industry experts. Key topics include regression, classification, and algorithm design, giving you a structured entry point into applied data science. The course emphasizes reproducible coding practices and clear workflows, preparing you for more advanced AI and research programs.
4. ASPIRE – Johns Hopkins Applied Physics Laboratory (APL)
Location: Laurel, MD (in-person and virtual options)
Cost: Free (No stipend)
Program Dates: Academic year: September–May (80–130 hours, depending on grade level); Summer: late June–August (190+ hours)
Application Deadline: February 15 (for summer entry; academic-year continuation by mentor approval)
Eligibility: U.S. citizens; high school juniors or seniors (15+ years old) with a minimum 2.8 GPA; must reside in approved MD, VA, or DC counties (virtual option available for some)
The ASPIRE Internship Program pairs high school students with Johns Hopkins Applied Physics Laboratory (APL) mentors to conduct technical research in fields such as computer science, applied math, and data analytics. During the academic year, you’ll commit to 80–130 hours, developing coding skills, working with datasets, and contributing to lab projects. You’ll gain valuable experience in independent problem-solving, technical communication, and collaboration in a professional STEM environment. Each internship culminates in a digital poster and final presentation, where you’ll showcase your technical outcomes and applied data workflows.
5. Introduction to Data Science with Python – Harvard University (edX)
Location: Online
Cost: Free to audit; $299 for verified certificate (No stipend)
Program Dates: Self-paced; open December 4–December 3
Application Deadline: Rolling enrollment
Eligibility: High school students with prior Python and basic statistics knowledge
Introduction to Data Science with Python, offered by Harvard faculty on EdX, is a self-paced course that helps you learn how to use Python to analyze data, build models and think like a data scientist. The course walks you through essentials of regression (linear, multilinear, polynomial) and classification (kNN, logistic), while while teaching you to use Python libraries such as pandas, numpy, matplotlib, and scikit-learn. You’ll learn key Machine Learning concepts such as overfitting, regularization, and uncertainty assessment. Weekly coding assignments will help in building your ability to structure data pipelines, visualize results, and evaluate models. By completion of the program, you’ll be equipped with reproducible Python skills and a working knowledge of machine learning fundamentals.
6. High-Dimensional Data Analysis – Harvard T.H. Chan School of Public Health (edX)
Location: Online
Cost: Free to audit; $219 for verified certificate (No Stipend)
Program Dates: Self-paced; open November 27–November 26
Application Deadline: Rolling enrollment
Eligibility: High school students with prior exposure to statistics and programming (R recommended)
This four-week course teaches you how to make sense of large, complex datasets, especially those used in genomics and biomedical research. You’ll study mathematical distance, dimension reduction, and principal component analysis, applying these to real biological data. The course also covers advanced topics such as singular value decomposition, factor analysis, and clustering methods such as k-means and hierarchical clustering. Using R, you’ll gain hands-on experience with visualization, error estimation, and cross-validation. By the end of the course, you’ll have both applied coding ability and theoretical understanding of high-dimensional data problems.
7. Data4All High School Bridge Workshop
Location: Hyde Park, Chicago, IL
Cost: Free (No Stipend)
Program Dates: 8-week Saturday workshop (Fall cohort)
Application Deadline: Closed; next iteration opens in Fall
Eligibility: High school sophomores–seniors who have completed Algebra I
This Data4All High School Bridge Workshop helps you take the leap from introductory computer science to applied data science research. Working in small teams, you’ll work through the entire research lifecycle—problem framing, data cleaning, exploratory analysis, modeling, and communication—using Python with NumPy, pandas, and Matplotlib. Case studies use real research datasets from areas such as public health and spatial data, helping you see how data science helps in solving real-world problems. You’ll also gain hands-on practice with multidimensional arrays, visualization, and basic prediction workflows – key skills for future research. By the end of the program, you’ll have completed a data science project from scratch and gained confidence for more advanced research ahead.
8. Building Predictive Models for Biological Data
Location: Virtual (synchronous + asynchronous)
Cost: Contact program for fee details; scholarships available (No stipend)
Program Dates: October 11–January 3
Application Deadline: Rolling until 12 seats are filled (first-come, first-served)
Eligibility: Motivated high school students (grades 9–12); no advanced coding required
This Young Scholars program introduces you to the intersection of machine learning and bioinformatics. Using the Orange Data Mining platform, you’ll explore supervised and unsupervised algorithms and apply them to biomedical datasets. The program combines lectures with hands-on labs, emphasizing model building, evaluation, and visualization of models. You’ll also refine your own research paper, developing key skills in hypothesis design, evidence-based reasoning, and scientific communication. In merely four weeks, you’ll go from learning the basics of machine learning to applying it to real biological questions.
9. Junior Data Scientist Program – Clevered
Location: Virtual
Cost: Varies by track; scholarships and certification options available (No Stipend)
Program Dates: Multiple cohorts throughout the year, including fall term
Application Deadline: Rolling enrollment
Eligibility: High school students ages 12–18; beginner and advanced tracks available; no prior
coding experience required
This Junior Data Scientist Program by Clevered is a fun way to dive into the world of data science and artificial intelligence. This multi-level program combines structured coding lessons with project-based learning. Depending on your level, you’ll work with block coding or Python, learning how to use conditionals, loops, and data structures to solve problems. Advanced modules cover exciting challenges such as building apps, creating interactive games, and applying AI tools for image and voice recognition. You will also participate in AI competitions and hackathons, putting your new skills to the test. Top performers can even earn certifications from Google and IBM, helping you to stand out in college applications. By the end of the program, you’ll build your coding, problem-solving, and presentation skills, and confidence to take on bigger projects in the future.
10. Principles, Statistical and Computational Tools for Reproducible Data Science
Location: Online
Cost: Free to audit; $149 for verified certificate (No stipend)
Program Dates: Self-paced; open December 11-December 10
Application Deadline: Rolling enrollment
Eligibility: High school students with prior exposure to programming (R or Python) and statistics; suitable for students interested in computational biology, bioinformatics, or applied data science
Principles, Statistical and Computational Tools for Reproducible Data Science course helps you think — and work — like a real data scientist. This course focuses on reproducibility in data science workflows, blending statistical and computational tools. You’ll work with platforms such as Git/GitHub, RStudio, and Jupyter to manage version control, generate reports, and share results. Through hands-on modules, you’ll explore statistical frameworks for reproducible analysis, dive into case studies across biomedical and physical sciences, and learn how to avoid common pitfalls in experimental design. By the end of the program, you’ll complete a reproducible research project and master technical skills in dynamic reporting using R Markdown and Jupyter.
11. Data Science: R Basics
Location: Online
Cost: Free to audit; $219 for verified certificate (No Stipend)
Program Dates: April 16–December 17
Application Deadline: Rolling enrollment
Eligibility: Open to high school students; no prior R experience required
Data Science: R Basics course teaches you how to use the R programming language for data wrangling, analysis, and visualization. You’ll start with the fundamentals – learning basic syntax, data types, and vector operations, along with conditional logic and loops. From thereon, you’ll work with real-world datasets and use R packages such as dplyr for data manipulation, and ggplot2 for graphics. The course also focuses on building reproducible workflows and prepares you for advanced topics such as probability, regression, and machine learning, which are covered in later modules.
12. What is Data Science? – IBM
Location: Online Cost: Free to audit (certificate available with Coursera Plus subscription) (No Stipend) Program Dates: Flexible schedule; 1 week at 10 hours/week Application Deadline: Rolling enrollment Eligibility: Open to high school students; beginner-friendly, no prerequisites required
IBM’s What is Data Science course is a short introductory course that defines the scope of data science and career pathways in the field. Through four modules, you’ll examine how data scientists use tools such as machine learning, big data analytics, and AI to generate insights and make smarter decisions. You will also get an introduction to basic concepts in data mining, cloud computing, and deep learning, learning how they fit into real-world projects. Industry experts provide case studies and professional advice, giving you a clear picture of what it is like to work in the field.
13. Data Science Math Skills – Duke University
Location: Online
Cost: Free to audit (certificate available with Coursera Plus subscription) (No Stipend)
Program Dates: Flexible schedule; 1 week at 10 hours/week (self-paced)
Application Deadline: Rolling enrollment
Eligibility: Open to high school students; no prior math courses required beyond basics
Data Science Math Skills, offered by Duke University, is a short course that helps you build the mathematical foundation required for data science and machine learning. The key course topics include set theory, inequalities, interval notation, summations, and functions, as well as logarithms, probability, and Bayes’ theorem. You’ll also review slope, distance, and tangent line concepts on the Cartesian plane—helping you understand how Calculus connects to data science. More than just solving problems, the course emphasizes mastering notation, algebraic manipulation, and probability logic, ensuring you can transition smoothly into more advanced coursework in data analysis or machine learning.
14. Data Science Foundations: NumPy, Pandas & Visualization – Packt
Location: Online
Cost: Free to audit (certificate available with Coursera Plus subscription) (No Stipend)
Program Dates: Flexible schedule; 1 week at 10 hours/week (self-paced)
Application Deadline: Rolling enrollment
Eligibility: Open to high school students; beginner-friendly, no prior coding required
This course is a hands-on course that helps you build foundational skills in Python programming that every aspiring data scientist should have. You’ll start with the programming basics – learning variables, loops, functions, and conditionals before diving into Python’s data science libraries. You’ll practice manipulating arrays with NumPy, cleaning and transforming datasets with pandas, and creating statistical visualizations with matplotlib and seaborn. By completion of the program, you’ll have practical experience with the Python data ecosystem and the ability to handle and visualize structured datasets effectively.
One more option – Horizon Academic Research Program
If you’re looking for a competitive mentored research program in subjects like data science, machine learning, political theory, biology, and chemistry, consider applying to Horizon’s Research Seminars and Labs! This is a selective virtual research program that lets you engage in advanced research and develop a research paper on a subject of your choosing. Horizon has worked with 1000+ high school students so far and offers 600+ research specializations for you to choose from. You can find the application link here!
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