Data Science in a Box is a fully developed curriculum for an introductory data science course in the R programming language. The curriculum contains high-quality instructional videos, slides, and activities developed by leaders in the Data Science community. The curriculum incorporates readings from two OER texts: R for Data Science and Introduction to Modern Statistics. In addition to providing instructional resources for students, the curriculum also provides guidance for faculty on implementation, pacing, and assessment of student learning. The curriculum has excellent accessibility and adaptability. It is freely available via a GitHub repository which enables all materials (slides, assignments, assessments) to be downloaded and edited. The curriculum is well organized. The videos are well developed, contain accompanying slides, and present clear and engaging examples that foster understanding and fluency with R. The online texts are well written, well organized, modular, and contain useful examples and exercises for students. The texts complement the activities well and strengthen student understanding of data science and statistics in meaningful ways.
This project will be focused on designing, constructing and evaluating different containers to determine the optimal design for heat retention. After students have constructed their designs and collected and shared data, students will evaluate the class data to create an optimal design for our culminating event: warming ooey, gooey chocolate chip cookies to perfection! Through this activity, students will learn about energy transfer, engineering design process, data collection, graphing, rate of change, optimization, surface area and proportions. The students will test the effectiveness of their design using Vernier Probes to gather quantitative data and graphing the rate of temperature change. They will then create a poster presentation to share their data to the class. Students will use their mathematical skills to quantitatively analyze the strength and weaknesses of their designs while enjoying some delicious, toasty, warm cookies.
This is a 21 day unit on the topic of floods. Students will plan and prepare for what might happen in the event of a flood in our area. We have had floods in the past that have affected the Walterville School, its campus, and the surrounding areas. Using this as a springboard, students will discuss the effects of flooding, do research and interview family members who have experienced flooding, and then discuss possible ways to prevent significant damage on the buildings and surrounding areas. They will then design a barrier that could protect an area from damage for a period of time. Students will need materials to conduct experiments. We have listed these in the lesson plan. We have also included a trip to the Leaburg Dam so that students can learn about dams and their uses. We plan on teaching this unit in the fall.
Introduction to seismic theory, measurements and processing of seismic data to final focussed image for geological and/or physical interpretation.This course deals with the most important aspects of reflection seismics. Theory of seismic waves, aspects of data acquisition (seismic sources, receivers and recorders), and of data processing (CMP processing, velocity analysis, stacking, migration) will be dealt with. The course will be supplemented by a practical of 6 afternoons where the students will see the most important data-processing steps via exercises (in Matlab).
Consistent housing is a continual issue for our community, evidence of this is readily observable in the neighborhoods surrounding our classrooms. Over the course of 15 classroom hours, students will be exploring how they can insulate structures to protect from extreme hot and extreme cold using recycled and/or repurposed materials.
Students will make observations and collect data related to temperature. Student findings will be communicated through science journals, student generated models (charts, 3D structures, drawings, etc.).
Locate and use numeric, statistical, geospatial, and qualitative data sets, find data management templates, find data repositories to house your own data and find tools for data visualization.