Resources

Our community has an impressive collection of materials and tools that can be useful in assisting faculty and programs in transforming the teacher preparation courses in which future teachers learn to teach statistics and data science. This collection of resources will continue to grow!

Our resources are organized into categories to help you find materials that best meet your needs. These categories include ESTEEM-Created Materials, Teacher Education Curricular Resources, K-12 Curriculum Resources, Technology Tools, and Open-Access Journals.

We’re always looking to strengthen our community through collaboration! If you have a resource that could benefit others, we’d love for you to share it! Please see the information at the bottom of the page to get involved.

ESTEEM-Created Teacher Education Resources

ESTEEM Foundations in Data Science and Statistics Teaching Modules

This set of 2 modules are an update from the 2021 “Foundation” materials. These new materials provide a foundation for teachers about how students learn data science and statistics and how to teach it effectively for all learners. Materials are packaged into LMS courses to provide maximum flexibility for instructors to tailor materials to their own contexts.

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ESTEEM Teaching Statistical Association Modules

This set of 2 modules are an update from the 2021 “Association” materials. The Teaching Statistical Association Modules Set explores the teaching and learning of statistical association. These modules are organized into two parts: the first focuses on categorical variables, and the second on quantitative variables.

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2021 ESTEEM Modules

The initial ESTEEM project produced more than 40 hours of teacher education curriculum materials for preparing to teach statistics in grades 6-12. Modules (last updated 2021) include videos, brief readings, data investigations with CODAP, activities, assessments, and discussion prompts. Three modules:
• Foundations in Statistics Teaching
• Teaching Association
• Teaching Inferential Reasoning

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Preparing to Teach Mathematics with Technology: Teaching Statistical Investigations

In 2017, the ESTEEM team released 4 sets of materials to introduce secondary teachers to core concepts such distribution, comparing groups, measures of center and spread and linear regression with technology. The lessons emphasize statistical investigations using large multivariate datasets and take advantage of linked representations across tables, graphs, and maps in CODAP.

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AMTE Standards Supplemental Materials: Roller Coaster Investigation in a Statistics Classroom

These materials are shared as Supplemental Materials for implementing the AMTE Standards for Preparing Mathematics Teachers.  The selected activities use a roller coaster investigation and 3 classroom-based videos of students and teachers to illustrate key ideas and experiences that set the stage for teacher learners to begin developing strong statistics pedagogy. Materials connect to frameworks used in mathematics teacher education such as professional noticing and practices for productive classroom discussions.

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Data Moves with a Purpose

This resource describes a framework that includes different actions with data that help to restructure a dataset (e.g., grouping, ordering) or enhance ways to view information about a single case or groups of cases (e.g., summarizing, linking representations). Examples provided with CODAP.

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Teacher Education Curricular Resources

MODULE(S2)

The NSF-funded MODULE(S2) project has published free curriculum materials that develop prospective teachers’ statistical knowledge for teaching secondary mathematics.

Intended for use in university content courses that include statistics, the materials are organized into 3 modules:
– Module 1: Study Design & Exploratory Data Analysis
– Module 2: Statistical Inference
– Module 3: Statistical Association

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Statistics and Data Science for Teachers

This book provides teacher educators with a resource to guide courses and professional development when preparing teachers of all grade levels to teach the foundations of statistics and data science in their classrooms.

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Score Network

This national network creates and shares sports datasets and modules specifically designed for teaching statistics and data science.

Content is organized by sports as well as by statistics and data science concepts.

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Introduction to Statistical Investigations

The text differs from traditional texts in both content and pedagogy.  Statistical inference is introduced using simulation-based methods in Chapter 1. Students are actively engaged in the material through numerous activities in which they flip coins, draw cards, use real data, do simulations with free applets and run experiments. 

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InSTEP Platform

InSTEP is designed to support online self-paced professional learning about teaching statistics and data science for middle and high school teachers. There are 50+ hours of learning materials plus tools to help you find and save resources specific to your learning goals, track your progress, and engage with InSTEP community members.

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META Math Project

The META Math Project was designed to create, field-test, and research curriculum materials that connect undergraduate mathematics and statistics courses with high school teaching applications. By integrating high school-level mathematics as a meaningful application area, META aims to deepen mathematical understanding for both prospective teachers and mathematics students. This approach not only enriches connections between undergraduate coursework and secondary education but also fosters more motivated new teachers and more engaged mathematics students.

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Introduction to Modern Statistics (2e)

Introduction to Modern Statistics (2e) is a free, open-access textbook that blends statistical thinking with practical, real-world data analysis. Emphasizing both computational and mathematical approaches, it features multivariable data visualization, simulation-based inference, and regression modeling, all supported by interactive R tutorials and hands-on labs.

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Statistical Thinking: 
A Simulation Approach to Modeling Uncertainty

This open-access introductory statistics textbook and lab manual are designed to engage students in active, collaborative learning through small-group activities and rich multimedia resources. Centered around the use of TinkerPlots software, the materials emphasize conceptual understanding and data exploration, helping students develop critical habits of mind for reasoning with data.

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A Project-based Approach to Introductory R and Python for Data Science

This open online book supports learning of R and Python through a data-centric, project-based approach. It guides learners in building programming skills and broader data science thinking by utilizing real-world data science projects. It can be used as a course preview, review, and supplements to other classroom materials.

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OpenStax Principles of Data Science

Principles of Data Science is intended to support one- or two-semester courses in data science. It is appropriate for data science majors and minors as well as students concentrating in business, finance, health care, engineering, the sciences, and other fields. The text emphasizes data ethics, AI, and hands-on practice through Python code, examples, and exercises, with downloadable resources included.

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K-12 Curriculum Resources

Introduction to Data Science

The IDS Curriculum teaches students to reason with, and think critically about, data in all forms. The Common Core State Standards (CCSS) for High School Statistics and Probability relevant to data science are taught along with the data demands of good citizenship in the 21st century. Additionally, IDS provides access to rigorous learning that fuses mathematics with computer science through the use of R/RStudio.

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Enhancing Statistical Thinking And Reasoning in Schools (ESTARS)

ESTARS developed modules that integrate the statistical investigation process, data visualization, and simulation into high school statistics.

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Fourth-Year Math Course Frameworks

Explore new fourth-year math course frameworks, designed by the Charles A. Dana Center, to equip students with essential skills in Data Science, Statistics, and Quantitative Reasoning. These frameworks prioritize real-world applications and critical thinking for our data-driven world, preparing students for both higher education and the workforce. Whether you’re an educator or policymaker, our resources offer the tools and insights needed to create a lasting impact.

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Illuminations Project

An NCTM  collection of vetted activities incorporating data into earlier grades with accompanying technology tools. Can search on statistics/data.

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Investigating US Society with Data

Two curriculum modules are designed to promote high school students’ statistical thinking and data literacy skills through investigations of social and economic conditions in the U.S. To conduct these investigations, students analyze large-scale U.S. population data using CODAP. Students learn and apply fundamental data practices as they examine questions of relevance and social importance to themselves, their families, and their communities.

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Bootstrap

Bootstrap’s focus is on integration of free, evidence-based computing and data science with core subjects like math, with materials designed for maximum flexibility. These materials cover data visualization collection, and analysis, summary statistics (center, spread, etc.) modeling with linear and nonlinear functions, ethics, social impacts and more.

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Data Science Project-Based Learning

This resource contains three lesson sequences addressing the high school Data Science Standards of Learning through project-based learning: Data Science Unplugged, Data Science with CODAP, and Data Science with Python. Teachers can select a sequence of lessons using their preferred tool, or mix and match lessons from the three sequences to meet their goals and their students’ needs.

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API CAN CODE CURRICULUM

The API Can Code project explores ways to introduce learners to foundational ideas about computer science by having them explore the data that shapes their lives. In particular, the learning experiences will center on application programming interfaces (APIs) for popular platforms and services, allowing students to better understand the digital worlds surrounding them.

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Youcubed Introduction to Data Science Course

This course introduces students to data science through free tools such as Google Sheets, Python and Data Commons. Students will learn to be data explorers in project-based units, through which they will develop their understanding of data analysis, sampling, correlation/causation, bias and uncertainty, probability, modeling with data, making and evaluating data-based arguments, the power of data in society, and more! The curriculum is adaptable so that teachers can bring in datasets or use what is provided to find datasets most relevant to their students.

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WeatherX

WeatherX promotes interest and skills in scientific data practices among middle school students in rural areas. Students use large-scale NOAA data  within the tool CODAP to investigate typical and extreme weather in their rural communities.

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Data Biographies

Data Biographies introduce educators to curated datasets for middle and high school classrooms. Data Biographies are not lesson plans, but rather launchpads designed to support the integration of complex data into the classroom. Each Data Biography includes background information specifically designed to help educators explore the data and determine potential uses in various classroom contexts.

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Data Science for High School:
An Innovation Pathway for Data Careers

This site shares curricula and resources for providing students with critical 21st century data skills. The collection of course materials enable high schools to form the core of a pathway linking to post secondary data science pathways. Resources for Course integration include:
• Civics + Data
• Math + Data
• Visualization + Data
• Python + Data

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Data Education in Schools

Data Education in Schools is a UK and Scottish Government-funded project to teach data literacy and data citizenship skills to learners aged 3-18 years old in the Edinburgh and surrounding area. Their website includes many resources for teaching data literacy and data science in K-12 classrooms, including lesson plans and teacher resources.

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Public Data in the Public Interest: A Spreadsheet-Based Project for High School Computing

Data for Healthy Communities (DHC) is a 15-hour high school curriculum that uses spreadsheets and public data to provide an accessible introduction to data science in the broader context of decision making for complex problems. Students work with real-world government data and learn how to use data as evidence to support an argument for investment in their local communities.

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Infusing Data Science Education into Elementary Mathematics

This website provides activities and a template for a 4-day workshop for in-service teachers on infusing data science concepts into elementary mathematics. It makes use of the data life cycle framework to guide teachers through explorations and opportunities to adapt/create activities for their studnents. Two Goals for Project IDEEM:
To strengthen content knowledge of data science concepts for TK-5 elementary teachers.
To gain experience infusing data science activities into existing TK-5 elementary mathematics curricula.

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How to get started: Cybersecurity for K-12

This guide provides information and resources aimed at educating K-12 students about cybersecurity. Whether you’re a teacher looking to introduce cybersecurity in your classroom or a student curious about hacking for good, this guide will help you get started.

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Technology Tools

CODAP

CODAP (Common Online Data Analysis Platform) is an easy­-to-­use data analysis environment designed for grades 5 through 14. CODAP can be used across the curriculum to help students summarize, visualize, and interpret data, advancing their skills to use data as evidence to support a claim.

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Tuva

Tuva provides an interactive data literacy platform designed to make data analysis engaging and accessible. With a vast collection of datasets and intuitive tools, Tuva helps learners build statistical reasoning skills through hands-on exploration.

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Tableau

Tableau Public is a free, powerful data visualization tool that allows users to create, share, and explore interactive charts and dashboards. Educators and students can use it to analyze real-world data, develop critical thinking skills, and communicate findings through compelling visual storytelling. By making data visualization more accessible, Tableau Public helps foster a deeper understanding of statistics and data science.

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Open-Access Journals

Statistics Teacher

An online journal and news from ASA & NCTM that promotes the growth and knowledge of teachers of statistics through articles, opportunities, and lesson plans. This is targeted at PreK-12+ teachers.

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Journal of Statistics and Data Science Education

The Journal of Statistics and Data Science Education (JSDSE) is an open-access peer-reviewed journal published by the American Statistical Association. It disseminates accessible knowledge for the improvement of data science and statistics education at all levels, including: elementary, secondary, post-secondary, post-graduate, continuing, and workplace education. Although JSDSE is a journal of the American Statistical Association, submissions are welcome from educators, practitioners, and researchers around the world.

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Harvard Data Science Review

As an open-access platform of the Harvard Data Science Initiative, Harvard Data Science Review (HDSR) features foundational thinking, research milestones, educational innovations, and major applications, with a primary emphasis on reproducibility, replicability, and readability. We aim to publish content that helps define and shape data science as a scientifically rigorous and globally impactful multidisciplinary field based on the principled and purposed production, processing, parsing, and analysis of data. By uniting the strengths of a premier research journal, a cutting-edge educational publication, and a popular magazine, HDSR provides a crossroads at which fundamental data science research and education intersect directly with societally-important applications from industry, governments, NGOs, and others. By disseminating inspiring, informative, and intriguing articles and media materials, HDSR aspires to be a global forum on everything data science and data science for everyone.

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