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Data Dunkers

Welcome to our Learning Modules

The Data Dunkers program empowers students to craft their unique data narratives, addressing a series of compelling questions. Through this guided journey, participants actively enhance their data science expertise, applying key skills and concepts by immersing themselves in the art and science of data storytelling. It's designed to foster an experiential learning environment where students not only gain conceptual understanding but also apply what they learn in creating meaningful, data-driven presentations. The Learning Modules for the Data Dunkers program have been designed to be flexible to support a variety of program approaches. You can embed them in your school day and cross-connect them with other subject areas, hold a series of after-school sessions, or have a number of 1/2 or full day events around the program.

The Program Approach

Instructional Approach

The Data Dunkers program employs a hands-on, project-based learning approach to engage students in data science through the lens of basketball sports analytics. This approach is characterized by several key strategies:

  • Engagement through Physical Activity: To keep students engaged, the program intersperses short lesson segments with basketball games. This strategy not only breaks up the monotony of classroom learning but also helps to reinforce the connection between the physical sport and its analytical aspects.
  • Project-Based Learning: Students work on individual or small group data science projects, applying what they've learned to real-world basketball data. This allows them to explore their interests, develop critical thinking, and apply problem-solving skills in a practical context.
  • Focus on Application of Tools and Skills: The curriculum introduces students to Python, Jupyter Notebooks, and libraries like pandas and Plotly, equipping them with modern data science tools and skills. This includes both coding and analytical techniques, as well as the ability to interpret and tell stories with data.
  • Adaptation to Student Needs: Recognizing varying levels of prior knowledge and interest, the program is designed to be accessible to beginners while still offering depth for more experienced students. This adaptability helps to ensure that all participants can find value and growth in the experience.

Learning Activity Components

The learning modules of the Data Dunkers program are structured around a blend of technical instruction, hands-on application, and physical activity. This design aims to not only practice data science skills but also to foster a holistic understanding of how these skills can be applied to real-world scenarios, particularly in the context of sports analytics. The inclusion of physical activity and project-based approaches helps to make the program engaging and relevant to students' interests, potentially inspiring a lifelong interest in both data science and sports.

Key components include:

  • Foundational Concepts: Introduction to Python programming, focusing on basic syntax and essential libraries for data analysis, such as pandas and Plotly.
  • Data Analysis Projects: Students undertake projects that involve analyzing open data from the NBA and WNBA, allowing them to apply coding and analytical skills to real datasets.
  • Integration of Design Thinking: Lessons include elements of design thinking to encourage creativity in how data is presented and stories are told through graphs and analyses.
  • Physical Activities: Basketball games and exercises are integrated into the curriculum to reinforce the connection between the data analysis work and the physical sport, enhancing engagement and understanding.
  • Individual Projects: A significant portion of the curriculum is dedicated to individual or group projects where students explore specific questions of interest, such as analyzing the performance trends of professional players like Pascal Siakam.
Data Science Competencies
Competency Core Concepts Skills
Foundational Data Literacy Understanding what data is, types of data, and the importance of data in decision-making. Identifying data collection methods and performing simple data collection and entry.
Computational Thinking and Programming Basics Basic principles of computational thinking, including decomposition, pattern recognition, abstraction, and algorithm design. Introduction to programming for data manipulation and simple analyses.
Statistical Reasoning Basic statistical concepts and introduction to probability and inferential statistics. Using software tools to compute statistical measures and visualize data distributions.
Critical Data Analysis How to evaluate data sources for reliability and bias, assessing data completeness and accuracy. Identifying trends and patterns, recognizing potential biases or errors.
Data Visualization and Communication Principles of effective data visualization and the role of storytelling in data science. Creating clear, informative visualizations; communicating findings through storytelling.
Ethical Considerations in Data Science Key ethical considerations in data collection, analysis, and use, including privacy concerns. Applying ethical considerations to data projects, including consent and anonymizing data.
Application of Data Science to Real-World Problems How data science is applied across various industries to solve problems and make decisions. Engaging in project-based learning to address real-world questions.
Critical Thinking Skills Understanding critical thinking essentials for data science: selecting vital metrics, assessing dataset integrity, discerning trends and underlying causes, identifying biases or inaccuracies, and deriving actionable insights. Applying critical thinking through project work, including defining metrics and evaluating data.
PS

Explore Data Dunkers Fundamentals

A 6-9 hour program that focus on an inquiry approach to introducing data science through basketball.

Modules

Explore Data Dunkers Full Court Press

A 10-20 hour approach that provides a robust project-based learning approach to learning data science through basketball

Teachers

Learn more about the Teacher Support

Additional guides for teachers are coming soon!

The Program Structure

Data Dunkers Fundamentals
The Data Dunkers Fundamentals program, lasting 6 to 9 hours, introduces students to data science fundamentals through the engaging context of basketball analytics. Utilizing Jupyter Notebooks, participants initially analyze their own basketball performance data, laying the groundwork for foundational skills in data analysis, storytelling, and visualization techniques. The program then transitions to a scaffolded, inquiry-based exploration of NBA/WNBA statistics. This methodical approach encourages students to hypothesize reasons for performance differences in the larger dataset and formulate data-driven recommendations for personal skill improvement. Ideal for students in grades 5-12, this format offers a hands-on experience that seamlessly blends the basics of data science with the universal appeal of basketball.

Data Dunkers Full Court Press
The Data Dunkers Full Court Press program, designed for a commitment of 10 to 20 hours, is aimed at students in grades 5-12. This standalone offering encompasses everything introduced in the Fundamentals program and extends beyond, providing a more comprehensive dive into data science through basketball analytics. By retaining the core elements of data analysis, storytelling, and visualization, this format enriches the learning experience with added depth. Beginning with the analysis of class performance data—akin to the Fundamentals—the Full Court Press leverages project-based learning to open doors to expansive research opportunities. Students are empowered to undertake individual or group projects, exploring basketball analytics and beyond.
The adaptability of the Full Court Press is a hallmark of its design, allowing educators the freedom to tailor the depth of data science exploration to fit their classroom's unique needs and available time. Whether by tackling more complex questions or integrating diverse datasets—ranging from health and nutrition to career data—educators can customize the program to enhance student engagement and outcomes. This approach not only solidifies foundational learning but also invites in-depth analysis, fostering critical thinking, problem-solving skills, and a profound appreciation for data science within the captivating context of basketball and beyond.

Partners

PS43 Foundation
Dell Technologies
Callysto
Digital Moment

Contact Us

datadunkers@dell.com

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