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Dashboard Week (Day 3) – Power BI Day

This post was originally published on The Data School blog between 2018 and July 2025, before our program was renamed to MIP’s Analytics Career Accelerator. References throughout this article to “The Data School” or “DS” all refer to what is now MIP’s Analytics Career Accelerator. The program, its people, and its commitment to launching outstanding analytics careers remain the same – just under a new name.

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For day 3 of Dashboard week, we analyzed the Victorian crime statistics data and built a Power BI dashboard. Initially, I was overwhelmed by the challenge since I had comparatively less experience with Power BI than Tableau. Then, I decided that it would be easier to handle the challenge in smaller pieces rather than trying to look at it as a whole. Let me walk you through the approach I took to navigate through this challenge:

  1. Choosing the dataset

The first step was to choose a dataset from the Victorian Crime Statistics Agency website. The website had different datasets about criminal incidents, victim reports, and family and alleged offender incidents. After looking at all the datasets, I decided to focus my analysis on the alleged offender incidents and the demographic distribution of the offenders.

  1. Analyze the data

Once I downloaded the Excel file, I found that it had a lot of sheets, and each sheet represented a different table. For simplicity, I used a table that contained all the necessary information. I also filtered out all the aggregated values from the table.

  1. Create the dashboard

Building a dashboard in Power BI is surprisingly straightforward due to its intuitive interface and drag-and-drop functionality. I wanted to visualize the data for the following areas:

  • Total incidents by sex
  • Breakdown of incidents by age group and sex
  • Total incidents by year and age group

I also wanted to include a tooltip to look at the trend of incidents by year, but I had to park that idea due to the shortage of time. I was able to implement it in Tableau but have yet to work it out in Power BI. Finally, I was able to create the following dashboard.

Using this dashboard, I found that:

  • Even though most of the alleged offenders were males, the number of incidents involving women aged 17 and under was slightly higher.
  • There were more alleged offender incidents in the year 2021.
  • There has been a significant increase in incidents for the age group of 10-14 years.

In my opinion, the Power BI challenge was quite interesting, and it was a good learning experience. I hope to learn more about Power BI in the upcoming days.


 Icon attribute: <a href="https://storyset.com/business">Business illustrations by Storyset</a>

 

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