Resources to Teach and Learn Data Science in High School
Read to learn how teachers can incorporate data science into their classrooms, how students can learn at home and what kinds of resources are available.
Resources for Children
Apr 23, 2026-7 MIN READ
MastersinDataScience.org is owned by 2U, LLC, parent company of edX. Our goal is to help learners make confident, informed decisions about their education and career. Some programs shown here are offered by universities that partner with 2U, for which 2U provides marketing and operational support and receives compensation. Other programs shown may be paid advertisements from third parties. Both types of programs are identified with the word AD or Advertisement. We aim to keep information current and accurate. Learn more about edX and our partners.
Data science is no longer just a college or career subject; it's increasingly finding a home in high school classrooms. Early exposure to data science helps students build skills in reasoning with real data, and those skills matter whichever field they choose to study next.
How does data science complement the ongoing push for coding and computer science classes? CS classes pose unique challenges for educators, who may be hindered by limited subject-matter knowledge, technical training, and resources, according to the Computer Science Teachers Association's 2025 Computer Science Teacher Landscape report. Data science is a more holistic field that builds on coding and computer science skills while also emphasizing storytelling, pattern recognition, and visualization — skills that matter regardless of what path a student takes after high school.
Data science is increasingly positioned as a "reasoning-and-real-world" subject, relevant to students heading into healthcare, business, public policy, environmental science, and virtually every other field. The Introduction to Data Science (IDS) curriculum, which began as a 2014 pilot in the Los Angeles Unified School District and is now supported by ThinkData Ed, is one example of this movement at scale: according to ThinkData Ed, IDS has been taught to more than 90,000 students in 250+ schools across the globe. Built around hands-on investigations using authentic datasets, the curriculum is designed to help students reason critically with data and communicate their findings — practical skills that translate across fields.
Continue reading to learn how teachers can incorporate data science into their classrooms, how students can learn at home, and what kinds of resources are available.
Learning Data Science at Home
Data science can complement the existing curriculum or be accessible to students learning independently. Below are a few core areas of data science explained, along with ideas for at-home practice.
Research Design
Research design is the strategy behind a research project, the plan for how you'll answer one of two fundamental questions: What is happening? Why is this happening? Good research design anticipates challenges and maps out each step of the process before you begin.
How can I learn this at home? The basic components of research design can be practiced in almost any setting. Review the six steps of the scientific method laid out in a homeschool science fair guide, and think about what experiments you can run in your own home or backyard. For example, how can you determine the average number of dogs that walk past your home every day?
Data Analysis
Data analysis is the process of inspecting, cleaning, and manipulating datasets to discover useful information. It can guide decision-making, power an application, or tell a compelling story. Professional data scientists typically use Python or R to wrangle and automate large datasets.
How can I learn this at home?
There are many tutorials and coding classes online for beginners looking to get familiar with Python or R. The Python Tutor visualizer, a free tool that shows step by step how code runs, can be helpful when learning programmatic thinking and debugging. Check out the technical tools section below for more resources. For first-time coders, Scratch, a free programming language and online community, is also a great place to start.
Machine Learning
Machine learning uses statistical methods to identify patterns in data and build algorithms that improve with use. Data scientists build models from sample data to train computers to recognize patterns and make predictions.
How can I learn this at home?
Kaggle competitions are a great way to practice or build machine learning skills. Kaggle is a data science and AI community that hosts hundreds of open-entry competitions for all skill levels, from beginners to experts. Read more about getting started with Kaggle competitions.
Data Visualization
Data visualization is the art of telling a story with data using design and graphics — everything from interactive dashboards to static infographics. A great data visualization is clear, compelling, and suited to its audience.
How can I learn this at home?
Try regular sketching and doodling. Grab a pen and paper and imagine how you would show any information around you, in as many ways as possible. Giorgia Lupi and Stefanie Posavec's Dear Data project, a year-long exchange in which the two collected data about their own lives each week and hand-drew it on postcards, is a great source of inspiration for low-tech, human-centered data storytelling. For examples of professional data visualization done with personality and creativity, The Pudding (listed under Readings and News below) publishes visual essays on topics ranging from film and music to politics and health.
To support educators and students, MastersInDataScience.org has compiled resources and tools to help teach and learn data science skills. Below is a collection of lesson plans, tutorials, datasets, and career guides.
Lesson Plans and Activities
Bootstrap Data Science Pathway, Bootstrap World: Lessons for data visualization, measures of center and spread, programming, linear regression, and more. Bootstrap says it was the first national provider to offer an integrated data science course. Its Data Literacy materials (grades 5–12) can run as a semester-long elective or as units within existing classes, and its full Data Science materials (grades 9–12) make up a full-year class. Modules can be mixed and matched for anything from a one-week introduction to a full-year course.
Introduction to Data Science Curriculum, UCLA / ThinkData Ed: A high school data science curriculum that began as a 2014 pilot in the Los Angeles Unified School District and is now supported by ThinkData Ed. The course uses real-world, student-collected data. ThinkData Ed offers professional development workshops for new teachers and year-round technical and curriculum support, and educators can also review the curriculum materials and lab slides online.
Databasic.io: A toolkit that introduces working with data, including analyzing spreadsheets with WTFcsv and exploring data networks with ConnectTheDots. Each tool comes with an educator Activity Guide.
Wharton Global Youth Program Lesson Plans: More than 400 free business and finance lesson plans from the Wharton School's youth education program, organized by National Business Education Association (NBEA) subject areas that include computation.
Data Nuggets: Free activities that bring real research data into K–16 classrooms to teach data analysis, exploration, and pattern-finding. Each activity is rated by the difficulty of its reading and content, so teachers can choose a level that suits their students.
Classroom-Ready Data Resources, NOAA: Curricula and activities for exploring ocean, climate, drought, Great Lakes, and other environmental data, including several for grades 9–12. Each listed resource has a teacher guide and links to the standards it meets, and several are hosted off site.
Dynamic Data Science, The Concord Consortium: Activities for grades 9–14 built on the Common Online Data Analysis Platform (CODAP) from the nonprofit Concord Consortium.
Technical Tools and How-Tos
Tract-Level Census Map, Storybench: A short tutorial on building a Census-tract map in R with the tidycensus and tmap packages. It requires R, a notebook or code editor, and a free Census API key.
First Python Notebook: A free, step-by-step guide to analyzing data with Python and Jupyter notebooks, using the pandas library for filtering, grouping, and merging data and Altair for charts.
Teaching Resources, Partners in Data Literacy: Handouts and tutorials to teach students foundational data science skills: organizing data, graphing, and analysis strategies.
Google's Python Class: Lecture videos, written materials, and exercises to learn Python from setup to utilities. Recommended for learners with at least a little programming experience.
Python for Beginners, Python.org: Python's official starting point, with a beginner's guide for non-programmers, a guide for experienced programmers, and links to documentation, tutorials, books, and other resources.
Readings and News
Finding Stories in Spreadsheets by Paul Bradshaw: A book on how to "interview data" in Excel and Google Sheets by asking the right questions of a spreadsheet. It is a paid book sold through Leanpub.
How to Lie with Statistics by Darrell Huff: A classic introduction to how statistics, graphs, and averages can be used to mislead — an essential read for developing critical thinking about data claims.
The Pudding: A publication of data-driven visual essays on culture and society, with stories on topics such as film, music, politics, and health. The Pudding is known for making complex data stories accessible and engaging — a great model for student projects.
What's Going On in This Graph? American Statistical Association and The New York Times Learning Network: A joint feature in which students analyze a timely graph by saying what they notice and wonder and what headline would capture its main point.
Datasets
Census Reporter: A free, open-source project that makes U.S. Census Bureau data easier for journalists and others to explore, with charts and maps for places across the country.
Data Is Plural newsletter: A newsletter of newsworthy datasets, usually five per issue, from Jeremy Singer-Vine. Its full archive collects past editions published from 2015 to 2025.
Data.gov: A searchable catalog of the U.S. government's open data, with more than 400,000 datasets.
OECD Data Explorer: Data, charts, and tables from the Organisation for Economic Co-operation and Development, organized by topics such as the economy, education and skills, health, and employment.
Gallup Poll Topics and Trends: Polling data and trend analysis across Gallup's key topic areas, including civil liberties, sports, religion, and housing.
Awesome Public Datasets: A community-maintained, topic-centered list of open datasets on GitHub.
Career Planning
Data Scientist: Common steps to becoming a data scientist and characteristics of a successful data science professional.
Business Analyst: What a business analyst does and the common steps to enter the field.
Computer Scientist: How to think like a computer scientist, along with typical qualifications and job responsibilities.
Data Analyst: The role of a data analyst and the kinds of tools they tend to use day-to-day. (For a detailed comparison between this role and a data scientist, see Data Analyst vs. Data Scientist.)
Statistician: Statisticians' responsibilities, qualifications, and skills.
