Let’s use data science to
solve our biggest problems.

We believe that we can’t make informed decisions, or fully understand problems, without understanding the data behind them first. With the Data Science-Driven Science Education (DSDSE) project, we want to help learners and educators to understand how to use data both within the classroom and in their everyday lives.

1. Ask a question.

Every data-driven investigation starts with a statistical investigative question.

Example: How did statewide mask mandates affect COVID-19 hospitalization rates?

2. Collect the data.

Collecting reliable data means using good experimental design and accounting for variability.

Example: Consult state government websites and CDC hospitalization data.
(CDC Mortality and Morbidity Weekly Report, Feb 5th 2021)

3. Analyze the data.

Statistical analysis, modeling, and data visualization are essential for drawing accurate conclusions.

Example: Tables concisely convey numerical data, while dot plots illustrate both averages and variability.
(CDC Mortality and Morbidity Weekly Report, Feb 5th 2021)

4. Communicate the results.

Conclusions that are communicated through meaningful visualizations support data-driven solutions.

Example: Mask mandates reduced COVID-19 hospitalization rates for adults between the ages of 18 and 64 years old.
DSDSE Data science driven science education logo

The DSDSE project is an initiative to raise national data literacy and preparedness among high school students for critical data science careers. We are integrating data science concepts with existing high school STEM curricula to promote adoption, as well as building educator capacity to confidently lead students in data science explorations. Our mission is to make learning data science easier and more accessible.

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Goal one
Prepare high school students for careers involving data science.
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Goal two
Build educator confidence in leading data science exploration.
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Goal three
Create a more data-literate public.

"...our collective goal in advancing data science research and education is the betterment of our global human society."

Dr. Xiao-Li Meng, Harvard University, DSDSE Co-PI


Clusters explore topics from many angles. These groupings of pathways are designed by LabXchange and other collaborators to help users to see a topic through different lenses. In contrast to traditional courses or syllabi, clusters provide a multidimensional approach to learning. Within each cluster, learners will develop skills related to one or more of the aforementioned steps of solving problems with data science.

Climate change

The climate change cluster illustrates data science concepts through the perspective of studying our ever-changing climate. Topics in this cluster align with NGSS high school Earth Science standards and AP Environmental Science coursework (see how in our Curriculum Alignment Guide).

Coming soon


The biotechnology cluster teaches level-appropriate data science concepts through the lens of modern biotechnology problems, including disease testing and clinical trials. Topics in this cluster align with many NGSS high school Life Sciences standards and AP Biology coursework (see how in our Curriculum Alignment Guide).

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Enhance your teaching

LabXchange is an innovative online learning platform that provides educators with a wide range of resources to enhance their teaching. One of the key features of LabXchange is the availability of worksheets and teaching guides that are designed to help educators create effective lesson plans and activities.

"Through this project, we aim to infuse data science into the high school science curriculum, introducing data science concepts to deepen students' understanding of biotechnology and climate science, and, conversely, using biotechnology and climate science examples to make data science more intuitive and accessible."

Dr. Joseph K. Blitzstein, Harvard University, DSDSE Co-PI


We have assembled a dynamic group of leaders, experts, and educators to spearhead this project.

Dr Joseph K. Blitzstein

Dr. Joseph K. Blitzstein

Professor of the Practice in Statistics, Co-Director of Undergraduate Studies, Harvard University

Professor Blitzstein received his PhD in Mathematics from Stanford University, and has taught Statistics 110: Introduction to Probability at Harvard since 2006. His research interests include inference for network data, foundational issues in statistics and data science, and education.

Read more
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Dr Xiao-Li Meng

Dr. Xiao-Li Meng

Whipple V. N. Jones Professor of Statistics, Harvard University
Editor in Chief of the Harvard Data Science Review

Professor Meng received his PhD in Statistics from Harvard University, and he is the founding editor in chief of the Harvard Data Science Review. His research interests include statistical theory and principles for data science, philosophical and foundational issues in statistics, and uncertainty assessments.

Read more
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Project team

The project team are the driving force behind the DSDSE project. They are responsible for designing, building, and overseeing the production of all educator and learner resources.

Advisory board

The advisory board is a select group of professors, educators, and industry experts who review learning resources to ensure their scientific accuracy and that the relevant data science skills are covered.

Co-Development Teachers

Through our collaboration with the Teaching Institute for Excellence in STEM (TIES), we are proudly developing this content with consistent input from expert high school educators. Members of the co-development cohort both review content for high school relevance and develop educator resources to facilitate adoption by teachers.

LabXchange is proud to collaborate with the Teaching Institute for Excellence in STEM (TIES) on the DSDSE project. TIES is dedicated to making STEM accessible to all learners by connecting, collaborating, and catalyzing dynamic STEM ecosystems. TIES connects LabXchange with teachers nationwide to ensure that these data science resources meet the needs of today's educators.


If you would like to be a part of the DSDSE project, if you have any thoughts or suggestions, or even if you would simply like to learn more, please fill out the form below.

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