It’s the final day of dashboard week!!

Today was a little bit different to other weeks as we had to present the same day. Usually, we get to work on it the whole day and present the next morning. Today, we all presented back to each other at 3:00pm which meant we needed to scope out our work carefully to ensure we would have a dashboard ready. I began the week being proud of my dashboards but have certainly dropped off during the week however I learnt a lot of lessons about timeboxing and planning dashboards early. It has been a huge week with a dashboard and presentation each day.

Now, on the final day of Dashboard Week, we were presented with a unique challenge: to work with prison population data. Although the dataset was quite limited, we decided to supplement it with additional data to uncover insightful trends and comparisons. In this blog, I will share how I approached this challenge, focusing on comparing the prison population per 100,000 people in each state, exploring state averages, and investigating the influence of political affiliations on prison population rates.

The prison population data provided us with a starting point, but it was evident that to gain a comprehensive understanding, we needed more context. My first step was to collect data on the population of each state to calculate the prison population per 100,000 people. This allowed us to normalize the prison population figures and make comparisons between states with varying population sizes.

Next, I explored the state averages, determining the overall average prison population rate per 100,000 people across the United States. This served as a benchmark to evaluate individual states’ performance in terms of incarceration rates. States with rates above the national average would be deemed to have higher-than-expected prison populations, and vice versa.

Beyond the basic data, I wanted to investigate whether political affiliations influenced prison population rates. To do this, I collected data on the political leaning (red state or blue state) of each state. We all know that political decisions can have far-reaching consequences, and understanding their potential impact on prison populations would add an intriguing layer of insight to the analysis.

After analyzing the data and preparing my dashboard, several key findings emerged:

Disparities in Prison Population: The prison population rates varied significantly across states, with some states showing much higher rates than the national average and others far below.

Political Affiliations and Incarceration: Interestingly, there appeared to be some correlation between a state’s political leaning and its prison population rates. Certain blue states had lower incarceration rates, while some red states had higher rates.

Completing Dashboard Week has been an incredible learning experience. Each day presented its unique challenges, and the final day was no exception. Working with prison population data and integrating additional context allowed me to uncover intriguing insights and patterns. As I presented my dashboard to the group, I felt a sense of accomplishment in the work I had accomplished throughout the week.

As we wrap up Dashboard Week, I’m excited to carry forward the knowledge and skills I’ve gained, ready to tackle new data challenges with enthusiasm and creativity. Data analysis is an ever-evolving field, and I am grateful for the opportunity to have honed my skills and shared this journey with my fellow data enthusiasts.