Example storyline from skills learned in Udemy's Tableau course and my first try at a dashboard. It appears there is no cheap option for hosting tableau visualizations online, so unfortunately this will not include the fully interactive dashboard. In place of this, the images of the storyline will be substituted in succession, highlighting key findings in the EDA performing with the dashboard.
The data used for this project is an example dataset of a newly formed UK bank and its customer-base. The goal was to segment the customer's out into demographic and geographical splits, determining significant representations that may lay hidden in the data. A few interesting findings are included below:
First image is an overall view of the bank's customers. We get an idea of the mean age sitting around 30-35, with a slight right skew. We also can see that over 50% of the sample has a balance of 30k or less. Further, the customer-base is about 50% white collar workers and there is a close to even gender split. Tableau dashboard really make this type of evaluation very easy.
With England in focus, we can see that the main disparity here is the high representation of white collar workers.
It may not be too much of a surprise, but Scotland accounts for a high percentage of the blue collar workers in the sample data. Customers from here are also typically older in age.
While there was a need to change the bin size of the balance, we can see a unique aspect of Whales is that there is an increase to the mid-size balance ranges, before falling back in line with the sample-wide averages.