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Announcing the 2023 Data Science Collaboration Challenge Winners! 

Check out the winning lesson plans below! 

1st Place:
“The Hamster Project!"

Danika Gupta, Dr. Nisha Talagala
2nd Place
“Gerrymandering and Big Data in Democracy"

Matt Kennedy, Ethan Gabel
3rd Place
"March Madness Lesson Plan"

Arty Smith, Anne Sterry

What is the DS Collaboration challenge?

In the 2021-22 school year, the Center for RISC and Data Science 4 Everyone piloted the first-ever Bite-Size Lesson Plan Competition for teaching Data Science and Data Literacy in K-12 contexts. We received over 100 submitted lesson plans for data science from teachers across the country. 

In the 2022-23 school year, we are going even bigger, and we are encouraging collaboration between teachers across school subjects. Our challenge this year is for teachers to work together across school subjects to create data science opportunities that are authentic to their respective disciplines.

For this year's challenge, we're looking for lesson plan submissions that show students how data can help draw connections between subjects and further enhance their understanding of various school subjects. 

You can read more about last year's competition here and view the winning submissions here. 

How were the winning entries chosen?

What have previous entries looked like?

Check out last year's winners!

1st Place:
“The Thrills of Roller Coasters: Using Data to Make Recommendations”

Hollylynne Lee, Gemma Mojica, Emily Thrasher, Zachary Vaskalis, Bruce Graham
3rd Place
“The Banana Survey Project”

Ned Diamond


Why should I submit a lesson plan? 

All educators who submit a high quality entry will receive a small stipend. Runners-up will receive $1000 and the grand prize winner will receive $2000


Who should the lesson plan be written for? 

The lesson plan should be written for a K - 12th grade teacher (like yourself) to implement in their classroom. We encourage submissions relevant for any grade-level. 


How long should the lesson be?

The lesson should be able to be taught in 1-2 hours (either for a regular or block schedule). You may opt to design content for up to ~ 5 hours (~ 1 school week), but please provide clear suggestions for how to break up instructional plans over multiple class periods.  


What counts as a “lesson plan”? 

We don’t have a strict definition of a “lesson plan.” As long as the materials you submit could help a fellow instructor impart a data science or a data literacy experience, feel free to use whatever format you think is most helpful for another colleague. In the past, submissions have taken the form of slide decks, worksheets, videos, written documents, and more. We encourage you, as an educator, to think carefully about what would be most helpful for YOU when first learning techniques in data analysis.


Please see our rubric above for more detailed guidance on lesson plan goals that may help inform format. 


If including a dataset, please consider using the guidance we created in collaboration with Bootstrap and on datasheets for K-12 educators. 


How will the winning lesson plan be chosen? 

We’re looking for lessons plans with: 

  1. High potential for student engagement

  2. A cross-disciplinary approach

  3. A strong data science focus

  4. Usability & feasibility

  5. Clarity & audience relevance


In short, we’re looking for lesson plans that are easy for teachers to implement while showing students the magic of data science. See the rubric embedded above for more details.


NOTE: We want these lesson plans to be accessible to everyone, so please make sure the necessary technology and data are reasonably accessible in terms of cost and technology barriers. 

More context

What counts as “data science”? 

As long as students are drawing insights from data, they’re engaging in data science! At a minimum, we’d like the lesson plan to guide students through some data points or data visualizations (e.g. graphs). Beyond that, it’s up to you — lessons can cover concepts like distributions in statistics, bar plots in biology, or the intuition behind machine-learning in social studies. Advanced experimentation or analysis is not necessarily expected  — these are meant to be bite-size introductions. You can view more about the progression of data acumen, data literacy and data science techniques in our advocacy deck. We are hoping to build a national workforce and citizenry of data literate leaders who can tackle the challenges of tomorrow with confidence. We need every level of the ramp to help us get there. 


Why is collaboration across subjects important for teaching data science? 

Data is utilized in nearly every line of work, so data science education should showcase how data science techniques are versatile and apply across a variety of knowledge domains. Definitions in industry, academia and K-12 education emphasize interdisciplinary connections between math, computer science, statistics, and the domain of which the dataset is focused (meaning: science, social studies, geography, or any other subject). Seeing data as a common thread among school subjects will also help reinforce students’ understanding of each subject jointly, and help demonstrate their convergence. 


Writing your lesson plan

Where can I find data? 

We have some great datasets for you to peruse on our website here, but we also encourage you to find data that’s specific to whatever topic you’re most excited to create a lesson around. 


Any tips for getting started?

Think of a lesson you already love teaching — is there a way for you to embellish it with a data exercise? Or maybe there’s a subject you’ve always wanted to teach — this is an opportunity to create a lesson on something outside of your day-to-day classes! 


Once you have a lesson idea, try searching online for some relevant data (make sure the data adheres to our datasets specification guide). Then, think about how you can incorporate various facets of data science, such as collecting, visualizing, analyzing, or presenting it. 


The great thing about data science is that it’s so diverse, so nearly any problem or topic can be grounded in data somehow. We hope this challenge encourages you to bring out your creative side! 

Further Questions

If you have questions about anything not covered on this page, please reach out to

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