MOOSE Data Science Learning Progression

Data Science

Data Science for Maine Students

Data Science is a dynamic discipline relevant to today’s world! These Data Science modules are designed to integrate all students with their communities through data science. The Data Science process requires all students to formulate statistical investigative questions, collect and consider the data, analyze the information, then interpret and communicate the findings.

venn diagram of three circles overlapping with math, science, and computers as the main elements and data science where they overlap in the center

Teachers from all over the state of Maine collaboratively built five developmentally appropriate learning modules that increase in complexity. These modules are interdisciplinary, meaning they embed a variety of disciplines into the data science process: math, the arts, humanities, and sciences.  Modules in the Data Science Learning Progression answer the following guiding question:  How can I use data science to understand, describe, and improve my world?

The Data Science Learning Progression

Before building the modules a team of Maine teachers collectively studied data science to develop a collective understanding. Then, teachers developed a learning progression of essential knowledge and critical skills students would need at each grade-span in order to be successful as data scientists. This work was largely informed by Stanford University’s Big Ideas.

Explore the MOdules

In the earliest grades, students use their natural curiosity to ask questions about their world and then make investigative observations in order to answer their own questions with their own statistics.
In later elementary grades, students collect and consider data collected with special focus on types of data, organizing data, and data visualizations.
Students in grades 6-8 use critical and computational thinking to engage with patterns and probability, helping them draw conclusions based on data sets.
Students in high school create their own data content with purpose and clarity, deciding how to represent conclusions for the greatest impact on their chosen audience.