Case Study: Preparing for the Flu Season

This was a project for a Data Analytics bootcamp through CareerFoundry. The goal was to continue our development with Excel and to learn how to create visualizations, dashboards and storyboards in Tableau.

Tableau Storyboard

The
Process

Project Understanding

Objectives and goals
Download data
Perform exploratory analysis and descriptive statistics
Identify limitations
Duration

Analysis

Data preparation
- Cleaning, wrangling, aggregation, integration
Hypothesis Testing
Creating visualizations
Extracting insights

Determine the Tools

Microsoft Excel
Tableau Public

Presentation

Determining layout and storyline
Creating a theme
Answer key questions
Creating dashboard
Concluding analysis

Project Overview

Objective

A medical staffing agency provides temporary workers to clinics and hospitals as needed during flu seasons.

Goal: Help the staffing agency identify states in need of additional medical staff for the upcoming season. Highlight trends in seasons, locations, and vulnerable populations.

Key Question

Determine when to send staff, and to which states based upon need.
Identify vulnerable populations who are most affected by the flu season.

Skills

Translating Business Requirements
Data cleaning
Data integration
Data transformation
Statistical Hypothesis Testing
Visual analysis
Forecasting
Storytelling in Tableau

Duration

This was a 2 week project that was delivered on time.

Analysis

The first phase is always to clean the data and remove any inconsistencies, duplicate data, fixing errors, and assess missing values.

Next I needed to integrate the data into one file to perform analysis. This was done using the VLOOKUP function in excel.

The spread of the death rates for those of 65+yrs of age is greater than those of ages 0-64. And on average the data for 65+yrs of age is .00042 units from the mean of .1%.

The average death rate for ages 0-64 is 0.0015% and the average death rate for those 65 years and older is 0.1%. Making those 65+, 66% more likely to die form flu.

Once all my data was cleaning and combined, I was able to perform some basic statistics, run correlation assessments, and perform my hypothesis testing.

At this point I was able to start diving into the remaining analysis by aggregating my data and creating visualizations. These visualizations were then put together into a cohesive storyboard presentation.