API CAN CODE �Data in Learners’ Lives

Lesson 3: Using Data

This work was made possible through generous support from the National Science Foundation (Award # 2141655).

Warmup

• Read this article: User data stolen from 23andme�
• What stakeholders are involved?
• What issues of privacy are �highlighted?
• How could those involved �have mitigated (decreased) �this risk?
• In other words: how could�they have better protected�user data privacy?

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Lesson 1.2 Recap

• Last lesson, we talked about different kinds of data that we create and consume, and who may be interested in collecting that data�
• We also discussed some instances where data collection may under-represent important parts of the population, and what effect that might have on the conclusions of those studies

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From Raw Data to Wisdom

The DIKW Model:

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Data

Information

Knowledge

Wisdom

Numbers and texts without context

Processed data with context

Information acquired by experience

Analysis of �complex knowledge structures

The Epidemic Outbreak

In 1845 there was an outbreak of a cholera epidemic in London.

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Within 10 days, more than 500 people died in that neighborhood.

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Epidemiologist John Snow realized that the water system was the cause of the outbreak.

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How did he figure that out?

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Cholera epidemic is a widespread outbreak of a severe diarrheal disease caused by the bacterium Vibrio cholerae.

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Epidemic Outbreak Map�Source: National Geographic Education

The Epidemic Outbreak

John Snow marked the map with all �the deaths as a bar graph.

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He found that victims increase near �the Broad Street water pump.

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This discovery led to the public’s �conviction of the necessity of a �sewage system.

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What’s a recent or local issue like this that you might be interested in exploring?

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Epidemic Outbreak Map�Source: National Geographic Education

The Epidemic Outbreak

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Data

Information

Knowledge

Wisdom

The number of victims in the area of impact

Mapping the data on the map

Identifying patterns and the source of the problem

A municipal sewer system is needed to improve sanitation

DIKW - COVID-19 Dashboard

Take ~10 minutes to review the DC COVID-19 Data from July 18, 2020 and answer:

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What do you recognize on this page? �

What did you learn? �

What are you left wondering about?

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DIKW - COVID-19 Dashboard

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DIKW - COVID-19 Dashboard

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DIKW - COVID-19 Dashboard

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Heat Sensitivity Exposure Index

Heat Sensitivity Exposure is a health issue in many cities worldwide.

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DIKW - Heat Sensitivity Exposure Index

Review the Heat Sensitivity Exposure Index dataset on OpenDataDC. Answer:�

• What type of variable is the ”Total 2020 census population”?
• What type of variable is the ”Percent of population below 5 years of age”?

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DIKW - Heat Sensitivity Exposure Index

Review the Heat Sensitivity Exposure Index dataset on OpenDataDC.

�What variables are stored in this dataset? Are they quantitative or qualitative variables?�

At what stage of analysis are we in the DIKW model?�

What would we need to do to progress through the later stages?

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What kinds of questions do you think we could answer with this dataset?

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Local Issue for Investigation

• Talk with students around you.

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• What are some local issues you might want to investigate with data? �You could use data that already exists, or �collect new data. �
• Write down a few ideas in your notes so that �you can return to the ideas in later lessons.

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Conclusion

• What does each stage of the DIKW model look like?

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• Why do we need to analyze data instead of using it in its raw form?

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• Does the process of analysis always produce useful or correct wisdom? Why or why not?

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Exit Ticket

• What stage of the DIKW model does this graph represent?�
• What do you think the cases or data �in this investigation were?

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Thanks!

apicancode@umd.edu

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This work was made possible through generous support from the National Science Foundation (Award # 2141655).

API Can Code is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike

4.0 International (CC BY-NC-SA 4.0) License