I’ve created this visualisation based on a data set holding 326,000 records for 2016. It shows the aggregated number of 911 emergency calls in Montgomery Country, PA on a hourly base. So, if you have always wondered when police is getting the most emergency calls – here’s the answer. Emergency calls are clustered into three major categories:
- EMS (Emergency Medical Services)
The raw data was initially published on Kaggle.
Just click around. It’s a very first, minimum viable visualisation which is going to be part of an elaborate series on how to crunch data and create visualisations like the ones above without any background in Python, libraries such as Pandas, Matplotlib or other programming languages like R. After reviewing a huge number of data analysis projects on Kaggle, I felt tempted to challenge their data science community by showing an alternative route to exactly the same resulsts, but without the need to write a single line of code.
This series is targeting business users with no formal programming background, who want to learn the basics of cleansing, analysing and visualising large data sets using off the shelf software such as Microsoft Power BI.