The Influenza Data Analysis Project is designed to assist a medical staffing agency in preparing for the influenza season by analyzing historical influenza data and demographic information. The goal is to optimize staffing resources across the United States to ensure adequate patient care during peak influenza periods.
- Analyze historical flu data to identify trends and patterns
- Determine the optimal temporal and spatial distribution of healthcare workers
- Prioritize states with large at-risk populations for workforce allocation
- Evaluate data limitations and address data quality issues
- Develop a staffing plan based on analysis results
- Present findings and recommendations to stakeholders in an accessible format
The following data sets were used for the analysis:
- Influenza deaths by geography (CDC)
- Population data by geography, time, age, and gender (US Census Bureau)
- Counts of influenza laboratory test results by state (CDC Fluview)
- Survey of flu shot rates in children (CDC)
- Excel: Used for data profiling, data cleaning, and basic statistical analysis in Achievement 1
- Tableau: Used to create data visualizations and interactive dashboards to present insights effectively
Visualizations generated during the project can be found in the Tableau Workbook. This includes spatial, temporal, and statistical visualizations illustrating key findings and insights derived from the analysis. The Tableau storyboard provides a comprehensive overview of the data-driven narrative developed throughout the project.
An oral presentation of the Tableau Workbook can be found on YouTube. This presentation serves as the final deliverable of the project and presents findings and recommendations to stakeholders.
The goal of the Influenza Data Analysis Project is to use data-driven insights to proactively plan for staffing needs during the influenza season. By integrating multiple data sources and applying statistical analysis techniques, the project provides actionable recommendations to the medical staffing agency. This project highlights the importance of data analysis in healthcare decision-making and demonstrates the value of Python programming skills in extracting insights from complex data sets.