Deliverable 5 – Information Architecture Presentation

Deliverable 5 – Information Architecture Presentation
Assignment Content

Competency

Translate quantitative and qualitative data into appropriate visualizations.

Student Success Criteria

View the grading rubric for this deliverable by selecting the “This item is graded with a rubric” link, which is located in the Details & Information pane.

Scenario

The Covid-19 pandemic has had a tremendous impact on communities around the world. The impact has been felt in healthcare, economic, business, government, education, and social areas. It has put people at odds over vaccines, placed local government policies against federal policies, and beliefs versus medicine. Some may say the world we are living in is full of chaos over this situation. Businesses are demanding that employees get vaccinated or face termination of employment. And this has led to a situation where the legality of getting vaccinated has reached the state courthouse in Your State, USA.

The stage has been set for two opposing legal teams to argue their case before the state supreme court in Your State, USA. A very large grocery store chain, Fine Foods, has asked that their employees get vaccinated or face termination of employment. The company argues that as a supplier of goods and services to government entities, Fine Foods must follow vaccine guidelines set by Your State, USA. The Grocery Union

Workers of America is arguing that their members cannot be required to get vaccinated and that this is against the law.

For this assignment, you will choose a legal team, either Fine Foods or Grocery Union Workers of America (GUWA). Your role is a data analyst for the respective law firm representing the side YOU choose. The lead attorney has tasked you with extracting data to support their legal case for vaccinations. If you choose the Fine Foods legal team, you will support all employees getting vaccinated. If you choose the Grocery Union Workers of America (GUWA) legal team, you will support a person’s right to choose to get vaccinated and, of course, not lose their job. It is important for you to focus on your role as data analyst and provide data. You should not try to present any actual laws pertinent to Covid-19.

Although this topic may be emotionally charged, your job is to present data-centric evidence which can be presented in court. Your data should be credible and convincing. 

Instructions

Now that you have provided a readout of the MOCK trial day, the focus is returning to the source data for Covid-19. The lead attorney has mentioned to you that the numbers sometimes get confusing, and the legal team doesn’t really understand the differences between quantitative data and qualitative data. It all looks the same to them except when categories are used (qualitative data), but even those counts look like numeric data to them.

The lead attorney has asked that you conduct a brief tutorial for the legal team to explain the differences between quantitative data and qualitative data. The lead attorney believes this is an essential element to understanding the Covid-19 data.

Create a tutorial defining quantitative and qualitative data.
Include a visual of quantitative data from one of the Covid-19 data files.
Include a visual of qualitative data from one of the Covid-19 data files.
Summarize how to easily determine if data is quantitative or qualitative in nature.
Note: The submission for this assignment should be in Microsoft PowerPoint format. 

Data Files

https://covid.cdc.gov/covid-data-tracker/#rates-by-vaccine-status
https://covid.cdc.gov/covid-data-tracker/#vaccine-effectiveness
https://covid.cdc.gov/covid-data-tracker/#covidnet-hospitalizations-vaccination
https://covid.cdc.gov/covid-data-tracker/#cases_casesper100klast7days
https://covid.cdc.gov/covid-data-tracker/#trends_dailycases
https://covid.cdc.gov/covid-data-tracker/#compare-trends_newcases
https://covid.cdc.gov/covid-data-tracker/#demographicsovertime
https://covid.cdc.gov/covid-data-tracker/#demographics
https://www.cdc.gov/coronavirus/2019-ncov/science/forecasting/mathematical-modeling.html

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