Preparing for the flu season in the U.S.
The CareerFoundry educational project, which has opened up to me the possibilities of using Excel for analytics, the ability to perform hypothesis tests, correlations, forecasting, and spatial analysis within it.
Project Objective: In order to submit a request to the medical staffing agency that provides this temporary staff, it is necessary to determine which states are facing the greatest problems on the current day.
Context: During the influenza season in America, many clinics suffer from a shortage of medical personnel necessary to serve a large influx of patients. Moreover, some individuals belonging to vulnerable groups develop serious complications and require increased attention.
What was done:
- Cleaned and merged data sets from the US Census and Centers for Disease Control and Prevention (CDC).
- Conducted a comprehensive analysis to determine which states faced the most significant staffing challenges during the influenza season.
- Examined influenza trends across the U.S including identifying hotspots and affected regions.
- Developed an analytical dashboard to visualize and communicate key findings to stakeholders.
Primary stakeholder:
CareerFoundry Project
Project Duration:
Mai – Juni 2023
Tools:
Excel, Tableau
Skills:
Translating business requirements
Cleaning & Wrangling
Statistical hypothesis testing
Visual analysis and Forecasting
Creating Dashboard
Storytelling
Links:
Resuls:
Identified the states facing the most significant challenges during influenza seasons through comprehensive analysis of influenza trends across the United States. Developed an analytical dashboard to provide stakeholders with a clear, visual representation of the data for informed decision-making and strategic planning.
Retrospective:
Despite the numerous limitations of the dataset, it was possible to obtain interesting insights and develop recommendations. For instance, in order to accurately calculate the death rate by age groups, further information regarding the counts of Influenza-like-illness cases by state and year was required, specifically for each separate age group. Additionally, to address the issue of personnel allocation across different states and hospitals, it would be necessary to understand the workload in each region and the available workforce.
As for what I would like to learn next, with my current knowledge, I would like to conduct this analysis again, but this time using Python.
Some slides from the final presentation:
analyticswithlechner 