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    Introduction to Data Science
    Online Program

    Introduction to Data Science

    by Stanford Pre-Collegiate Studies

    Introduction to Data Science builds practical skills in algorithms, machine learning, and R through investigations of real-world datasets.

    Last verified: 6 Oct 2026Reviewed by:SSSean Stevens

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    Introduction to Data Science at a glance: cost, dates and eligibility

    Current status

    Upcoming

    Eligibility

    Grades 9-11; programming and statistics prerequisites

    Age range / Year group

    Ages 14-17

    Location / Region

    Online

    Cost

    $3,200

    Duration

    2 weeks

    Format

    Online

    Next deadline

    Not announced · Next cycle not announced

    Get next-cycle reminder
    Computer ScienceEngineering
    Sessions / cadence: 10 × 2-hour weekday classes; 2–3-hour daily homework
    Assessment style: Programming exercises, assignments, and projects
    Main deliverable / output: R exercises and dataset investigations
    Time-zone / scheduling model: PDT; one assigned section
    Latest verified cycle: 2026
    Academic support: Online office hours

    Introduction to Data Science summary

    This Stanford Pre-Collegiate Summer Institutes course turns data science concepts into practical investigations using algorithms, machine learning, and R.

    Students work with natural and social science datasets, compare models, and explore questions connected to their own lives.

    It suits students who enjoy combining coding with scientific reasoning and want to understand both the usefulness and limitations of data models.

    What is Introduction to Data Science?

    Introduction to Data Science is an academic course within Stanford Pre-Collegiate Summer Institutes. It introduces computer algorithms and the different models they produce, helping students consider how data can be used effectively and ethically.

    Students investigate datasets from the natural and social sciences and apply machine learning through integrated R programming exercises. Live online classes, office hours, and independent assignments connect technical practice with questions relevant to students' own lives.

    Why Succeed highlights Introduction to Data Science

    • Integrated R exercises give students a practical way to apply machine learning rather than encounter it only as theory.

    • Natural and social science datasets connect technical methods with real-world questions.

    • Comparing models encourages students to recognize trade-offs instead of treating every algorithm as equally useful.

    • The course explicitly raises effective and ethical uses of data alongside technical skills.

    Who is Introduction to Data Science for?

    Best for

    Students who enjoy coding, have a foundation in statistics, and want to investigate real questions through data.

    Not ideal if

    You need a first introduction to programming or want a light summer activity with little independent work.

    Is Introduction to Data Science worth it?

    The course is a strong option for students who want to move from basic coding toward investigating data scientifically. Its combination of model comparison, R exercises, and personally relevant questions gives students concrete opportunities to test their interest in data science.

    The workload requires sustained attention beyond live classes, so it is best approached as an academic commitment. Families should judge its value by the learning experience: Stanford states that participation does not guarantee undergraduate admission.

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    Founder, Succeed

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    A practical overview of cost, time commitment, provider details, student outcomes and online support to review before deciding.

    Cost and what is included

    $3,200; includes Live online classes., Online office hours., Integrated R programming exercises..

    Dates and time commitment

    2 weeks; 10 × 2-hour weekday classes; 2–3-hour daily homework.

    Provider details

    Review the Stanford Pre-Collegiate Studies provider page and official sources before making a final decision.

    What the student gains

    Potential output: Students produce programming work and data-focused assignments and projects.. Check whether feedback, certificate, recommendation or application evidence is included.

    Online support and safeguarding

    Check how online sessions are supervised, how mentor communication works, and what support route exists if the student needs help.

    What to check before committing

    Confirm current dates, session schedule, payment terms, cancellation rules, support expectations and what output or certificate is included with the provider.

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    How much preparation does Introduction to Data Science need?

    Effort level

    High for a short summer course.

    Best started

    Allow several weeks before a future session for application materials and prerequisite review.

    Main challenge

    Balancing coding exercises, statistical reasoning, and independent projects.

    Typical preparation

    • Review basic programming and statistics before the course begins.

    • Gather recent grade reports, transcripts, and a work sample that follows the course's application instructions.

    • Write application responses independently and leave time for guardian review.

    • Plan a daily study block alongside the selected live-class window.

    Useful if

    You want to test whether data science suits your academic interests.

    What do students do on Introduction to Data Science?

    You combine live instruction with R programming exercises and independent data-focused work. The central task is to use algorithms and models to investigate meaningful questions while understanding their strengths and limitations.

    Step-by-step process

    1. 1

      Explore computer algorithms and the models they generate.

    2. 2

      Compare models and consider effective and ethical uses of data.

    3. 3

      Apply machine learning through R programming exercises.

    4. 4

      Investigate natural and social science datasets using real-world questions.

    5. 5

      Complete assignments and projects outside class and use online office hours for support.

    Introduction to Data Science dates and deadlines

    Next cycle not announced yet. These dates are from the latest verified cycle and should be used as a reference only.

    Session One

    15-26 June 2026

    Live classes meet Monday-Friday; students attend one course section.

    Reference date

    Session Two

    6-17 July 2026

    Live classes meet Monday-Friday; students attend one course section.

    Reference date

    Eligibility and requirements

    • Grades 9-11 at the time of application.

    • Prior exposure to a computer programming language.

    • Working knowledge of statistics.

    • Admitted Summer Institutes students may attend only one course.

    How much does Introduction to Data Science cost?

    Financial aid

    Available — Domestic and international participants can receive aid.

    What's included

    Live online classes., Online office hours., Integrated R programming exercises.

    How to apply to Introduction to Data Science

    1. 1

      Create an account in the official application portal using the student's legal name and birthdate, or sign in to an existing account.

    2. 2

      Select Introduction to Data Science among the courses you genuinely want to attend.

    3. 3

      Complete the application responses independently and upload recent grade reports, transcripts, and the required course-specific work sample.

    4. 4

      Have a parent or legal guardian review the completed application and sign it.

    5. 5

      Check every component before submitting, complete the application-fee payment, and submit supplemental materials by the posted deadline.

    Next step with Succeed

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    Learning format

    Live online classes meet for two hours each weekday, with approximately two to three hours of daily asynchronous assignments and projects. Students attend one section within an 8:00-11:00 a.m. or 4:00-7:00 p.m. PDT window, with the third hour used for online office hours.

    Exact class and office-hour schedules are set closer to the program start.

    Mentorship and feedback

    • Live online instruction takes place Monday-Friday.

    • Online office hours use the third hour of the meeting window.

    • Students attend one assigned course section and time.

    Time-zone considerations

    • Published meeting windows use Pacific Daylight Time.
    • The morning window is 8:00-11:00 a.m. PDT.
    • The afternoon window is 4:00-7:00 p.m. PDT.
    • Students attend one section, with two hours of live class daily.
    • Plan another two to three hours daily for asynchronous homework.

    Assessment style

    Coursework centers on R programming exercises, assignments, and projects.

    • Apply machine learning through integrated R exercises.
    • Investigate natural and social science datasets.
    • Complete assignments and projects outside live class.

    Portfolio / outputs

    Main deliverable / output

    Students produce programming work and data-focused assignments and projects.

    • R programming exercises applying machine learning.

    • Investigations of datasets and personally relevant questions.

    • Assignments and projects completed outside class.

    Introduction to Data Science FAQs

    Students need exposure to a computer programming language and working knowledge of statistics.

    Students use R programming exercises to apply machine learning and investigate datasets.

    Plan for two hours of live class Monday through Friday and approximately two to three hours of daily assignments and projects. A third hour in the published meeting window is used for online office hours.

    No. Students attend one course section and time. The published windows are 8:00-11:00 a.m. or 4:00-7:00 p.m. PDT; exact class and office-hour schedules are set closer to the program.

    Students must complete all application responses independently. Parents or legal guardians review the completed application and sign afterward.

    No. Standardized test scores are optional for both domestic and international applicants.

    Yes. Financial aid can be awarded to both domestic and international participants.

    Stanford undergraduate admissions is separate and independent from Stanford Pre-Collegiate Studies. Participation does not guarantee admission; the educational value comes from developing skills, curiosity, and academic interests.

    Alternatives to Introduction to Data Science

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    Sean Stevens

    Content reviewed by

    Sean Stevens

    Co-founder, Succeed | Founder, Immerse Education (2012–2026)

    Sean works at the intersection of academic enrichment, program quality and university preparation, with expertise in evaluating pre-university experiences for ambitious secondary school students.

    View author page

    Sources & verification

    Source types reviewed

    • Welcome
    • Questions: Application - SI | Stanford Pre-Collegiate Summer Institutes
    • Introduction to Data Science | Stanford Pre-Collegiate Summer Institutes

    Succeed uses official provider information where available, but keeps this public page focused on comparison and planning inside Succeed.

    What we verified (on 6 Oct 2026)

    • Official course page checked

    • Session dates checked

    • Daily workload reviewed

    • Prerequisites and grades checked

    • Application requirements reviewed

    • Financial aid eligibility checked

    How Succeed uses this information

    We use source material to verify core facts, then show older cycle dates as reference when a current cycle is not available. Always check current application instructions before applying.