Data Science 4 Everyone Content Partner Network
Comprehensive data science education resources across diverse implementation approaches
📚 Full Course Implementations
For schools ready to offer dedicated data science courses, these comprehensive curricula provide everything needed for semester or year-long programs
Cost:
Free curriculum and software; Professional Development $1800 per teacher
Grades:
5-12
Tech:
Pyret
Approach:
Research-based, integrated Computer Science and Data Science modules
Special Features: Can be implemented as standalone course OR integrated into existing Math, Science, History, or CS classes. Comprehensive materials including lesson plans, slides, workbooks, answer keys, and datasets.
CodeVA (Virginia)
Cost:
Completely free including professional development for Virginia educators
Grades:
High school Data Science course alignment
Tech:
"Unplugged" activitiesCODAPPython
Approach:
Three course sequences through project-based learning
Special Features: Developed with Virginia Department of Education support and aligned to Virginia's Data Science Standards
Cost:
FREE first-year fellowship (includes curriculum, tech, and PD); $24/student subsequent years
Grades:
9-12 (High school and college)
Tech:
RCloud-based Jupyter Notebooks
Courses:
Statistics, Data Science, Algebra with Data
Special Features: Interactive online textbook with real-time progress tracking and LMS integration
Cost:
Free online textbook and summer workshop; districts pay for infrastructure
Level:
College/Community College and upper level high school
Tech:
PythonJupyterHub
Approach:
Combines statistics and computer science principles. Data 6 covers introduction to CS and Data Science while Data 8 focuses on intro to CS and Data Science as well as an intro to Statistics.
Special Features: Very technical implementation; modules can be integrated into other courses
Cost:
Free curriculum; fee-based professional development; technology costs per teacher/student
Grades:
10-12
Tech:
RAWS CloudRStudio
Approach:
Full-year, student-centered, highly interactive course aligned with ASA statistics guidelines
Support: Extensive PD (9 days first year, 4 days second year) plus ongoing ticketing system
Cost:
Free curriculum and technology; fee-based professional development for high school course
Grades:
K-12 (with dedicated high school course option)
Tech:
CODAPGoogle SheetsGoogle Data CommonsGoogle ColabEduBlocksTableau Cloud
Approach:
Project-based learning with both full course and individual lesson options
🔄 Modular and Supplemental Approaches
Perfect for schools wanting to integrate data science into existing courses without major restructuring
Cost:
Free curriculum and software; Professional Development $1800 per teacher
Grades:
5-12
Tech:
Pyret
Approach:
Research-based, integrated Computer Science and Data Science modules
Special Features: Can be implemented as standalone course OR integrated into existing Math, Science, History, or CS classes. Comprehensive materials including lesson plans, slides, workbooks, answer keys, and datasets.
Cost:
Free and paid access options; licenses at district, school, or teacher level
Grades:
6-12
Subjects:
Math, Science, Social Studies
Tech:
Web applicationMajor LMS integrations
Special Features: Currently used in 41 states; meets all federal digital security and privacy laws
Cost:
Per-seat pricing with scale discounts; full instructor support included
Grades:
Maps to most state data science standards
Courses:
Applied Data Analytics, AI and Machine Learning, Python and SQL, Power BI/Excel, Cybersecurity, Statistics
Tech:
Adaptive AI-driven platformReal-time assessment tools
Special Features: In-platform simulations using real-world data; data skills lessons and microcredentialing; integrates with Google Classroom, PowerSchool, Schoology
Cost:
All curriculum free; low-cost summer professional development
Grades:
8-12
Courses:
AP Statistics, Algebra I, Algebra II
Tech:
DesmosGraphing calculators
Special Features: Lessons ready to use immediately without new technology or training
Cost:
Free and paid versions available
Grades:
2-12
Tech:
Web applicationExtensive LMS integrations
Approach:
Supplements core Science, Math, or Social Studies curriculum with real-world datasets and visualization tools
Special Features: Three programs (Tuva Jr. for grades 2-5, Tuva for 6-12, Tuva 3D Science Assessments)
Cost:
Free curriculum and technology; fee-based professional development for high school course
Grades:
K-12 (with dedicated high school course option)
Tech:
CODAPGoogle SheetsGoogle Data CommonsGoogle ColabEduBlocksTableau Cloud
Approach:
Project-based learning with both full course and individual lesson options
👩🏫 Professional Development Platforms
A lot of the full course and modular integration platforms already mentioned have PD specific to their tools and curricula. However, we also work with providers who offer more targeted professional development about the field of data science
Cost:
Free strategies via website/YouTube; $5-20 for digital resources; $25-80 for asynchronous PL
Grades:
K-12 + Pre-service
Tech:
Platform agnostic
Approach:
Classroom-ready strategies to integrate data literacy into existing curriculum
Special Features: Coordinated professional learning across subject areas and grade bands
InSTEP
Cost:
Completely FREE
Grades:
6-12
Tech:
CODAPinZight Litespreadsheets
Approach:
Personalized professional learning for teachers and instructional coaches
Special Features: Customized recommendations and choice of learning modules; multiple modalities (online, face-to-face, hybrid)
While many curriculum providers offer their own integrated tools, there are also standalone platforms that can be used across multiple curricula and subject areas. These versatile tools can enhance any data science learning experience
Cost:
Completely FREE and open source
Grades:
5-14 (K-12 and college)
Tech:
Browser-basedDrag-and-drop interfaceCSV support
Approach:
Web-based data analysis platform designed for exploratory data science learning
Special Features: Dynamically linked visualizations (graphs, maps, tables); extensive plugin ecosystem; works across all subjects and curriculum; built on decades of research in interactive learning environments
Support: Active community forums, sample datasets, tutorials, and comprehensive documentation
Jupyter Everywhere
Cost:
FREE software and eliminates infrastructure costs (cloud hosting, JupyterHub setup)
Grades:
6-12 and higher education
Tech:
40+ programming languagesPythonRJuliaScala
Approach:
Interactive computing notebooks that combine live code, visualizations, and narrative text
Special Features: Industry-standard tool used in data science careers; enables reproducible research; seamless integration with big data tools; collaborative sharing capabilities
Support: Large open source community; extensive documentation; integration with platforms like Google Colab and AWS
Cost:
Completely FREE instructional routine and resources
Grades:
K-12 and adult education
Tech:
Platform agnostic
Approach:
Pedagogical technique for gradually unveiling graph components to promote data sensemaking
Special Features: Cross-curricular applications (math, science, social studies); develops critical thinking about data sources and interpretation; increases access without sacrificing rigor
Support: Extensive library of ready-made slow reveal sequences; professional development resources; active educator community via SlowRevealGraphs.com
📋 DS4E Content Partner Evaluation Rubric
Rubric Overview
Our comprehensive evaluation rubric was developed to fairly assess current and prospective content partners for the Data Science 4 Everyone Content Partner Network. This rubric ensures we maintain high standards for data science education while supporting diverse implementation approaches.
Scoring Scale: Each criterion is evaluated on a 0-3 scale, where 3 represents the highest level of excellence and 0 indicates significant gaps or absence of the evaluated component.
🎯 Core Data Science Educational Content
This category evaluates the fundamental quality and depth of data science education provided by each partner. These criteria ensure students receive authentic, comprehensive, and ethically-grounded data science experiences.
Inquiry-Based and Iterative Investigation
3: Resources center around authentic, motivating questions that require investigation; explicitly incorporates multiple iterations of analysis; students develop their own questions
2: Resources include guided inquiry with some student autonomy; allows for some iteration in analysis
1: Resources provide structured questions with limited opportunity for student-led investigation; minimal iteration
0: Resources follow rigid procedures with predefined outcomes; no iteration in analysis process
Integration of Technology
3: Seamlessly integrates appropriate technology tools; builds student proficiency with industry-relevant programming (Python, R) or specialized data tools
2: Incorporates technology tools that enable data manipulation and visualization; some skill development
1: Limited technology integration or uses tools with minimal data science application
0: No meaningful technology integration or obsolete/inappropriate tools
Multivariate Dataset Quality
3: Provides access to rich, authentic multivariate datasets; supports hypothesis testing across multiple variables; data is culturally relevant and engaging
2: Includes adequately complex datasets with multiple variables; somewhat authentic context
1: Offers simplified datasets with few variables; limited authentic context
0: Uses single-variable or artificial datasets with no authentic context
Mathematical Application
3: Seamlessly integrates appropriate algebraic functions, statistics, probability, and algorithmic thinking in authentic contexts
2: Includes most key mathematical concepts applied to data analysis tasks
1: Contains limited mathematical applications or concepts disconnected from meaningful context
0: Lacks appropriate mathematical integration or contains conceptual errors
Ethical Communication with Data
3: Explicitly teaches ethical data communication; students create and critique visualizations; addresses bias, misrepresentation, and responsible communication
2: Includes components on data visualization and communication ethics; some critique elements
1: Minimal coverage of ethical data communication; focuses on creation without critique
0: No meaningful attention to ethical communication or visualization principles
🛠️ Resource Implementation Quality
This category assesses how well partners support successful implementation in diverse educational settings. Strong implementation support is crucial for widespread, effective adoption.
Instructional Design and Accessibility
3: Comprehensive, clear materials with differentiation strategies; meets accessibility standards; culturally responsive
2: Well-designed materials that are usable and mostly accessible
1: Basic materials with limited guidance or accessibility considerations
0: Poorly designed or inaccessible materials
Learning Progression Alignment
3: Fully aligned with DS4E learning progressions; includes clear skill development pathways
2: Mostly aligned with learning progressions; some skill development articulated
1: Partially aligned with limited attention to skill progression
0: Not aligned with DS4E learning progressions
Professional Development Support
3: Comprehensive PD offerings with ongoing support; addresses both content and pedagogy
2: Adequate PD options available; covers essential implementation needs
1: Limited PD support; basic implementation guidance only
0: No meaningful PD support provided
🤝 Partnership Potential
This category evaluates partners' capacity to engage meaningfully with the DS4E community and scale their impact broadly. Strong partnerships amplify the reach and effectiveness of data science education.
Resource Availability and Scalability
3: Resources fully available nationwide/globally; proven scalability across various contexts
2: Resources available nationally with demonstrated implementation in multiple contexts
1: Limited availability or unproven scalability
0: Highly localized or impractical to scale
Partnership Engagement
3: Demonstrates strong commitment to all CPN expectations (meetings, Slack community, webinars, etc.)
0: Unable to meet partnership expectations