Resources to Teach and Learn Data Science in High School

Data science is no longer just a college or career subject; it’s increasingly finding a home in high school classrooms, and for good reason. According to the Bureau of Labor Statistics, employment of data scientists is projected to grow 34% over the next decade, far faster than the average for all occupations. That kind of demand makes early exposure to data science skills more valuable than ever.

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. 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 UCLA/LAUSD-founded Introduction to Data Science (IDS) curriculum, now supported by ThinkData Ed, is a leading example of this movement at scale: according to ThinkData Ed, IDS has been taught to more than 67,000 students in 250+ schools worldwide since its 2014 LAUSD pilot. 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.

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 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 five steps of experimental research 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 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 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 visual block-based language from MIT — is also a great place to start.

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 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?

Data visualization professionals recommend 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 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 publishes visual essays on topics ranging from pop music trends to sports analytics.

Table of Contents:
Lesson Plans and Activities
Technical Tools and How-Tos
Readings and News
Datasets
Career Planning

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.

Bootstrap Data Science Pathway, Bootstrap World: Lesson plans, slide presentations, and student workbooks for a full data science curriculum. Bootstrap was the first national provider to offer an integrated K–12 data science course, and its materials align with Common Core, CSTA K–12, and other national and state standards. 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: The leading high school data science curriculum for grades 9–12, created by UCLA and LAUSD and now supported by ThinkData Ed. The course addresses Common Core State Standards for High School Statistics and Probability and uses real-world, student-collected data. Full implementation requires training and technical support, but educators can 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.

Computation Lesson Plans, Knowledge @ Wharton High School: Math and problem-solving lesson plans from the Wharton School of the University of Pennsylvania’s youth education program, aligned with National Business Education Association (NBEA) standards and focused on business applications.

Data Nuggets: Classroom activities using real science research projects to teach data analysis, exploration, and pattern-finding. High school educators should look for Level 3 activities.

Data Lessons and Activities for Grades 9–12, National Geographic Resource Library: Classroom activities for grades 9–12 drawing on data from earth science, geography, U.S. history, and more, compiled by National Geographic Education. Teachers can filter by subject area.

Classroom-Ready Data Resources, NOAA: Curricula and activities for exploring NOAA atmospheric and oceanic data, each with a Teacher Reference Page listing the national standards it meets.

Dynamic Data Science, The Concord Consortium: Dynamic data science activities using the free Common Online Data Analysis Platform (CODAP) from the nonprofit Concord Consortium.

Storybench Tools for Educators, Northeastern University: A “living syllabus” of tutorials on R, data visualization, graphic design, and storytelling from Northeastern University’s School of Journalism. Particularly useful for students interested in data-driven writing and communication.

My First Python Notebook: A detailed, step-by-step guide to analyzing data using Python and Jupyter notebooks, using real campaign finance data from the California Civic Data Coalition.

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: Free lecture videos, written materials, and exercises to learn Python from setup to utilities. Recommended for learners with at least a little programming experience.

Introduction to Python: Open curriculum for teachers and students learning Python as a first coding language, covering object types, loops, functions, and classes.

Hour of Python, Trinket: A collection of tutorials and challenges to test Python knowledge, with lessons available in Spanish, Chinese, and Korean.

Finding Stories with Spreadsheets by Paul Bradshaw: An accessible book covering how to “interview data” in Excel — useful formulas, shortcuts, and strategies for finding patterns in datasets.

How to Lie with Statistics by Darrell Huff: A classic introduction to how statistical techniques can be used to mislead — an essential read for developing critical thinking about data claims.

The Pudding: A visual journalism publication that uses data analysis and interactive design to explore culture, sports, music, and society. The Pudding is known for making complex data stories accessible and engaging, and it publishes its methodologies alongside each piece — a great model for student projects.

Literature and Other Resources on Data Literacy, Partners in Data Literacy: A curated collection of articles, web resources, and books on data and quantitative literacy.

What’s Going On in This Graph? The New York Times: A free weekly feature from The New York Times Learning Network in which students discuss and analyze news-related graphs and data visualizations. New posts pause during school breaks, but an archive of past features is available year-round.

Census Reporter: An interactive tool for exploring U.S. Census Bureau data, designed to help journalists and students find and visualize demographic information.

U.S. Department of Education Data: Government data covering students, educators, and schools at all levels — including costs, demographics, discipline, and safety across thousands of datasets.

Data Is Plural newsletter: Five unique, interesting, or newsworthy datasets delivered to your inbox weekly. The Data Is Plural archive is a searchable trove of hundreds of dataset ideas.

Data.gov: A searchable database of the U.S. government’s open data, from county to federal level.

OECD Data Explorer: Charts, maps, tables, and research on global economies from the Organization for Economic Co-Operation and Development.

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 list of open, mostly free datasets covering more than 30 topic areas.

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 job outlook, including an interview with a working statistician.

Information last updated: April 2026