Form a group of two or three students and develop a data-driven solution to address a specific climate change issue. Reply to this task with the outcome of your research.
Students will explore and analyze real-world datasets using a web-based data visualization tool (Datawrapper), practising their data interpretation and critical thinking skills. They will select a sample dataset on global CO2 emissions, identify key variables, and create visualizations such as bar charts, stacked bars, grouped bars, split bars, bullet charts, and column charts. Students will then interpret these visualizations, highlighting key observed trends and patterns.
Following this, students will present their findings, explaining the dataset, the visualizations they created, and the conclusions drawn from the data. After the presentation, students will reflect on the limitations of the dataset, such as missing data, and suggest ways further analysis could enhance understanding. This exercise aims to improve data literacy and provide students with hands-on experience using visualization tools commonly employed in research and real-life situation.
Use a carbon footprint calculator via the embedded web link to estimate your own household’s carbon footprint by entering data related to home energy use, transportation, waste, and consumption habits. This exercise will help you see the environmental impact of your daily activities, including your household.
In a group, students can discuss which areas (e.g., energy, transportation, food) contribute the most to their carbon footprints and brainstorm ways to reduce their overall impact.
Students will propose one concrete action to reduce their carbon footprint. These ideas can then be shared via a reply button and discussed in a group, with students giving feedback and refining their plans.
A hospital aims to reduce patient wait times in the emergency room. They implement a data-driven solution by hiring additional staff. The hospital collects data on patient wait times before and after hiring the new staff. Evaluate the effectiveness of the solution based on the data provided below:
Before: Average wait time = 45 minutes
After: Average wait time = 30 minutes
A city wants to decrease traffic congestion in a busy downtown area. They implement a data-driven solution by introducing a new traffic management system. The city collects data on the average travel time during peak hours before and after the implementation. Evaluate the effectiveness of the solution based on the data provided below:
Before: Average travel time = 40 minutes
After: Average travel time = 25 minutes
➢ Describe in some detail the main disciplines that contribute to data science.
➢ Let the teacher explain the role of data scientists and students may write a small report on the same.
Data is an integral tool for understanding the real impacts, both projected and unknown, of climate change. It informs decision-making and directives for behavioural changes
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Church released!
hello!
Hi =)
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My frist comment, help me please
My frist comment, help me please again
hello!
Hi =)
Is there any other videos available? Really love this Graphic.
Sure, you can find more videos on Epic Games chanel
Great Slider, will you add SliderShare API support in the future?
Of course, our developers are working on it, we will try to add it in future updates.
hello!
Hi =)
Discussions on LMS on the economic relevance of data in addressing climate change
Short writing assignment: Explain the relationship between data and climate change.
Form a group of two or three students and develop a data-driven solution to address a specific climate change issue. Reply to this task with the outcome of your research.
Presentations and peer evaluations of the proposed solutions. Reply to solutions from other groups other than your group.
Individual written assignment: Analyze the economic impacts of climate change on a specific sector. Reply to this task with your findings.
Data Visualization
Analyzing and Visualizing Real-World Data
https://www.datawrapper.de/
Students will explore and analyze real-world datasets using a web-based data visualization tool (Datawrapper), practising their data interpretation and critical thinking skills. They will select a sample dataset on global CO2 emissions, identify key variables, and create visualizations such as bar charts, stacked bars, grouped bars, split bars, bullet charts, and column charts. Students will then interpret these visualizations, highlighting key observed trends and patterns.
Following this, students will present their findings, explaining the dataset, the visualizations they created, and the conclusions drawn from the data. After the presentation, students will reflect on the limitations of the dataset, such as missing data, and suggest ways further analysis could enhance understanding. This exercise aims to improve data literacy and provide students with hands-on experience using visualization tools commonly employed in research and real-life situation.
Carbon Footprint
Carbon Footprint Calculator (Free Licence)
https://www.carbonfootprint.com/calculator.aspx
Use a carbon footprint calculator via the embedded web link to estimate your own household’s carbon footprint by entering data related to home energy use, transportation, waste, and consumption habits. This exercise will help you see the environmental impact of your daily activities, including your household.
In a group, students can discuss which areas (e.g., energy, transportation, food) contribute the most to their carbon footprints and brainstorm ways to reduce their overall impact.
Students will propose one concrete action to reduce their carbon footprint. These ideas can then be shared via a reply button and discussed in a group, with students giving feedback and refining their plans.
Building Students Analytical Skills1
Problem 1:
A hospital aims to reduce patient wait times in the emergency room. They implement a data-driven solution by hiring additional staff. The hospital collects data on patient wait times before and after hiring the new staff. Evaluate the effectiveness of the solution based on the data provided below:
Before: Average wait time = 45 minutes
After: Average wait time = 30 minutes
Building Students Analytical Skills2
Problem 2:
A city wants to decrease traffic congestion in a busy downtown area. They implement a data-driven solution by introducing a new traffic management system. The city collects data on the average travel time during peak hours before and after the implementation. Evaluate the effectiveness of the solution based on the data provided below:
Before: Average travel time = 40 minutes
After: Average travel time = 25 minutes
➢ Describe in some detail the main disciplines that contribute to data science.
➢ Let the teacher explain the role of data scientists and students may write a small report on the same.
Data is an integral tool for understanding the real impacts, both projected and unknown, of climate change. It informs decision-making and directives for behavioural changes