15 Statistics Courses for High School Students
- Stephen Turban
- 30 minutes ago
- 11 min read
A statistics course can be a strong way for you to build quantitative reasoning skills that go beyond what a typical math class covers. Many of these courses are self-paced or run through university-affiliated online platforms, giving you the flexibility to work through the material on your own schedule. These courses can also give you a clearer sense of how statistical methods are applied in fields like sports, healthcare, business, and social science research.
What should you look for in a statistics course?
A statistics course can give you access to structured, college-level material on probability, data analysis, and statistical inference. Depending on the course you choose, you could work through probability, hypothesis testing, or regression analysis using tools like R or Python, offered through formats ranging from self-paced modules to live virtual classes. Whether you're looking for something free or a paid program with more structure, you can find a course suited to your background and interests.
To help with your search, here are 15 statistics courses for high school students.
If you’re looking for online summer research programs, check out our blog here.
Key takeaways
These courses span probability, hypothesis testing, regression, causal reasoning, and applied statistics in fields like sports, business, and life sciences, so students with a wide range of interests can find a relevant statistics course.
Several courses are free, including Carnegie Mellon OLI Probability and Statistics, Harvard Statistics and R, and University of Michigan Statistics with Python Specialization, while others, such as UC Berkeley Extension and Wharton STAT 0001, carry significant tuition costs.
Most courses are self-paced and require only a background in basic algebra, making them accessible entry points for students without prior statistics coursework, while others, such as Johns Hopkins CTY Sports Statistics, require completion of honors-level math.
Application deadlines vary widely, with most self-paced courses accepting rolling enrollment year-round, while structured cohort-based programs, such as Wharton STAT 0001 and UC Berkeley Extension live sections, follow fixed seasonal start dates.
Location: Virtual
Cost: Varies depending on program type (financial aid available)
Dates: Multiple cohorts throughout the year, including Fall (September – December)
Application Deadline: Varying deadlines based on cohort; Fall (September)
Eligibility: High school students with a minimum unweighted GPA of 3.3/4.0
The Lumiere Research Scholar Program is an independent research program for high school students interested in exploring university-level research across subjects such as mathematics, engineering, psychology, economics, computer science, and data science. You will work one-on-one with a PhD mentor from Harvard, Stanford, Oxford, or MIT to design and complete an original research project over several weeks. The program introduces you to different stages of the research process, including literature review, identifying a research question, academic writing, and presenting findings in a structured format. Depending on your area of interest, you may produce a research paper, policy analysis, computational project, or interdisciplinary study connected to your chosen field. Workshops and writing support sessions are also included to help you develop research methodology, analytical thinking, and academic communication skills. Lumiere offers multiple research tracks with varying levels of depth and publication support, and students who complete the program may also become eligible for post-baccalaureate credit through UC San Diego Extended Studies.
Location: Online
Cost: Free
Dates: Self-paced
Application Deadline: Varies each year
Eligibility: Open to high school students
Probability & Statistics — Carnegie Mellon University Open Learning Initiative (OLI) is an open-access online course that introduces high school students to core concepts in probability and statistics through a self-paced format. You will study topics like data analysis, probability rules, random variables, sampling methods, hypothesis testing, and statistical inference using interactive lessons and problem-solving activities. The course also includes simulations and applied exercises that demonstrate how statistical concepts are used to interpret data across different disciplines. Designed for beginners, the program only requires a background in basic algebra, making it accessible to high school students interested in mathematics, data science, or research. You will work through structured modules that combine instructional content with practice questions and automated feedback.
Location: Virtual
Cost: Varies depending on program type. Need-based financial aid is available for AI Scholars.
Dates: Multiple 12-15-week cohorts throughout the year, including spring, summer, fall, and winter
Application Deadline: On a rolling basis. Spring (January), Summer (May), Fall (September), and Winter (November). You can apply to the program here.
Eligibility: Ambitious high school students located anywhere in the world. AI Fellowship applicants should either have completed the AI Scholars program or exhibit experience with AI concepts or Python
Veritas AI, founded and run by Harvard graduate students, offers programs for high school students who are passionate about artificial intelligence. Students who are looking to get started with AI, ML, and data science would benefit from the AI Scholars program. Through this 10-session boot camp, students are introduced to the fundamentals of AI & data science and get a chance to work on real-world projects. Another option for more advanced students is the AI Fellowship with Publication & Showcase. Through this program, students get a chance to work 1:1 with mentors from top universities on a unique, individual project. A bonus of this program is that students have access to the in-house publication team to help them secure publications in high school research journals. You can also check out some examples of past projects here and read about a student’s experience in the program here.
Location: Virtual and in-person at Johns Hopkins, Baltimore, MD
Cost: Join fee: $55, in-person registration fee: $75, online registration fee: $15-$20, need-based financial aid available
Dates: Varies each year
Application Deadline: Varies each year
Eligibility: Open to students in grades 7-11 who have completed honors grade 7 mathematics
The Johns Hopkins CTY Sports Statistics Course is an advanced mathematics course that covers statistics, probability, and data analysis through the context of sports. You can explore how statistical methods are used to analyze patterns, evaluate performance, and interpret data across sports such as basketball, soccer, and baseball. Coursework includes collecting and organizing data, identifying trends, designing research investigations, and applying classical statistical techniques to answer sports-related questions. The course also introduces concepts in probability, experimental design, and data interpretation, emphasizing practical applications of statistics.
Location: Online
Cost: Paid after free trial
Dates: Flexible schedule, approximately 11 hours to complete, starts May 21
Application Deadline: Varies each year
Eligibility: Open to students in grades 7-11 who have completed honors grade 7 mathematics
The Stanford Online Introduction to Statistics Course is an introductory statistics course in data analysis, probability, and statistical reasoning. Through the course, you will study topics such as exploratory data analysis, sampling methods, probability distributions, regression, hypothesis testing, and statistical significance while learning how data is interpreted across different fields. The material focuses on statistical thinking and quantitative reasoning, helping you understand how statistical methods are applied to research and decision-making. Assignments and practice exercises introduce you to analyzing datasets, identifying trends, and evaluating data-based claims using foundational statistical techniques. The course also explores concepts connected to experiments, sampling, and prediction, giving you exposure to methods commonly used in data science, economics, psychology, and social science research.
Location: Online
Cost: Paid after free trial
Acceptance Rate/cohort Size: Not specified
Dates: 2 weeks at around 10 hours per week, flexible schedule
Application Deadline: Varies each year
Eligibility: Open to high school students
Duke University Online’s Introduction to Probability and Data is an online course that introduces you to probability, statistics, and data analysis through lectures, programming assignments, and applied exercises. You will study topics such as probability rules, random variables, distributions, sampling methods, and statistical inference while working with datasets in R and RStudio. The curriculum also covers data visualization and exploratory data analysis, helping you understand how statistical concepts are used to interpret information. You will complete quizzes, coding exercises, and projects that involve analyzing real datasets and explaining statistical results.
Location: Online
Cost: Free, add a verified certificate for $219
Dates: Self-paced, approximately 4 weeks long
Application Deadline: Varies each year
Eligibility: Open to high school students
Harvard Online Learning’s Statistics and R is an online course that introduces you to statistical concepts and R programming through applications in data analysis and the life sciences. In this course, you will study topics such as probability distributions, random variables, hypothesis testing, confidence intervals, exploratory data analysis, and non-parametric statistics while working with R code and datasets. You will complete problem sets and coding assignments that involve creating visualizations, analyzing datasets, and applying statistical methods in RStudio. The course is part of Harvard’s Data Analysis for Life Sciences series and is designed for learners interested in statistics, data science, or quantitative research.
Location: Online
Cost: $1,025 for self-paced sections, $1,070 for live online sections
Dates: Self-paced
Application Deadline: Opens June 15
Eligibility: Live online from July 2 - 21
Introduction to Statistics — UC Berkeley Extension is an online course that examines how statistical methods are used to collect, organize, interpret, and present data. Throughout the course, you will work with topics such as descriptive statistics, probability, distributions, confidence intervals, hypothesis testing, correlation, and regression analysis. The curriculum introduces both conceptual understanding and quantitative problem-solving, with assignments focused on applying statistical techniques to different types of datasets. You will also learn how statistical reasoning is used to evaluate evidence and draw conclusions in academic and professional settings.
Location: Online
Cost: Paid after free trial
Dates: 2 weeks at around 10 hours per week, flexible schedule
Application Deadline: Varies each year
Eligibility: Open to high school students
Inferential Statistics — Duke University is an online course that explores how statisticians use sample data to make conclusions about larger populations. Through video lectures, quizzes, and applied assignments, you will examine topics such as probability distributions, confidence intervals, hypothesis testing, p-values, and statistical significance. The course also introduces concepts related to experimental design and data-driven decision-making, with examples drawn from real datasets and research scenarios. You will learn to interpret statistical results and understand the reasoning behind inferential methods. You will also engage with statistical software and quantitative exercises that reflect introductory college-level coursework in statistics and data analysis.
Location: Online
Cost: Paid after free trial
Dates: Self-paced
Application Deadline: Varies each year
Eligibility: Open to high school students
Linear Regression and Modeling — Duke University focuses on how statisticians use regression models to examine relationships between variables and make predictions from data. In this course, you will study simple and multiple linear regression, model interpretation, correlation, prediction, model diagnostics, and statistical inference using real datasets and applied examples. The curriculum also introduces you to statistical programming with R and RStudio, which are used throughout the assignments and analytical exercises. You will work through examples that explore questions in education, social science, and behavioral research while learning how to interpret patterns in quantitative data.
Location: Online
Cost: Free
Dates: Flexible schedule, around 4-weeks to complete
Application Deadline: Starts May 22
Eligibility: Open to high school students
Statistics with Python Specialization — University of Michigan is a multi-course online specialization that introduces you to statistical analysis and data science using Python programming. Across the program, you will study topics such as probability, hypothesis testing, regression analysis, data visualization, and statistical inference while learning to work with datasets in Python. The courses also introduce libraries commonly used in data analysis, including pandas, NumPy, and matplotlib, through coding exercises and applied assignments. The topics include interpreting data, building analytical workflows, and using programming tools to solve quantitative problems. You will complete projects that involve cleaning datasets, analyzing trends, and presenting statistical findings using computational methods.
Location: Online
Cost: Fee $4,230, fee waived for City of Philadelphia public or charter high school students
Dates: Offers Spring, Summer, and Fall cohorts
Application Deadline: Varies based on cohort
Eligibility: High school juniors and seniors
STAT 0001: Introduction to Statistics and Data Science — Wharton Global Youth Program is a pre-baccalaureate online course that introduces you to statistics and data science through business-focused applications and quantitative analysis. You will study topics such as descriptive statistics, probability, hypothesis testing, parameter estimation, and data interpretation while also learning introductory programming for data analysis. The course incorporates Python-based analytical work, including data management and statistical exercises completed through platforms such as Google Colab. Rather than presenting statistics only through formulas and calculations, the curriculum emphasizes statistical thinking and how quantitative methods are used to examine business and economic questions. You will participate in synchronous class sessions, complete assignments, and work on a culminating course project alongside other high school students enrolled in the program.
Location: Online
Cost: Fee $15
Dates: Self-paced
Application Deadline: Not specified
Eligibility: Open to high school students
Causal and Statistical Reasoning — Carnegie Mellon University Open Learning Initiative (OLI) is a self-paced online course that examines how statistical evidence is used to study cause-and-effect relationships. You will explore topics such as correlation, causation, conditional probability, confounding variables, experiments, observational studies, and causal graphs through interactive lessons and case studies. The course includes activities in the Causality Lab, a virtual platform where you can investigate data, test hypotheses, and analyze experimental outcomes. You will also study how researchers interpret evidence and evaluate statistical claims in fields such as science, healthcare, and public policy. The curriculum focuses on analytical reasoning and research interpretation alongside foundational statistics concepts.
Location: Online
Cost: Fee of $275
Dates: Self-paced
Application Deadline: Varies each year
Eligibility: High school students in grades 11 and 12
Introduction to Statistics — UC Berkeley Extension introduces statistical methods used to collect, analyze, and interpret data in academic and professional settings. As part of the course, you will examine descriptive statistics, probability distributions, sampling, confidence intervals, hypothesis testing, correlation, regression analysis, and data visualization through quantitative exercises and dataset-based assignments. The curriculum also covers statistical notation, interpretation of results, and the use of statistical reasoning to evaluate evidence across disciplines such as business, healthcare, and social sciences.
Location: Online
Cost: Fee of $260
Dates: Self-paced
Application Deadline: Varies each year
Eligibility: Open to high school students
AP Statistics, Semester A — UT High School Independent Learner Program covers the first half of the AP Statistics curriculum through an asynchronous online format designed for high school students. Coursework includes exploratory data analysis, probability, sampling techniques, experimental design, distributions, correlation, regression, and statistical inference. Throughout the semester, you will analyze datasets, interpret graphs and statistical summaries, and examine how data is used to support conclusions in research settings. The program also introduces concepts such as bias, variability, normal distributions, and simulation-based probability through assignments and assessments aligned with AP-level expectations.
Frequently asked questions
What statistics courses are available for high school students?
Options include free self-paced courses, such as Carnegie Mellon OLI Probability and Statistics, Harvard Statistics and R, and University of Michigan Statistics with Python Specialization, paid university-affiliated courses, such as Stanford Introduction to Statistics, Duke's statistics series, and UC Berkeley Extension, applied courses, such as Johns Hopkins CTY Sports Statistics and Wharton STAT 0001, and independent research programs, such as Lumiere Research Scholar Program.
Are there free statistics courses for high school students?
Yes, Carnegie Mellon OLI Probability and Statistics is free, Harvard Statistics and R is free with an optional paid certificate, and University of Michigan Statistics with Python Specialization is free. Causal and Statistical Reasoning from Carnegie Mellon OLI carries only a $15 fee.
Which statistics courses focus on programming or coding with real datasets?
Duke University's Introduction to Probability and Data with R, Inferential Statistics, and Linear Regression and Modeling all use R and RStudio for applied coding exercises. University of Michigan's Statistics with Python Specialization uses Python libraries such as pandas, NumPy, and matplotlib, and Wharton's STAT 0001 incorporates Python-based analysis through Google Colab.
Which statistics courses are best for students interested in AP-level preparation?
UT High School's AP Statistics course covers the first half of the AP Statistics curriculum in an asynchronous format aligned with AP-level expectations, including exploratory data analysis, sampling, and statistical inference. UC Berkeley Extension's Introduction to Statistics also covers comparable foundational topics, including regression and hypothesis testing, though it is not explicitly AP-aligned.
When should I enroll in a statistics course as a high school student?
Most self-paced courses, including those from Carnegie Mellon OLI, Harvard, Duke, and University of Michigan, accept enrollment on a rolling basis, so students can start at nearly any time. Structured programs with fixed cohorts, such as Wharton STAT 0001 and UC Berkeley Extension's live online sections, follow set seasonal start dates, so students should check each program's specific enrollment window in advance.
Stephen is one of the founders of Lumiere and a Harvard College graduate. He founded Lumiere as a PhD student at Harvard Business School. Lumiere is a selective research program where students work 1-1 with a research mentor to develop an independent research paper.