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Dashboard Week – Day1 LA Crime

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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At the data school, I’ve learned much about creating graphics, analyzing data with Alteryx, and designing dashboards. Now, I aim to put all this knowledge into practice by analyzing new data comprehensively this week.

Step1: Who is the audience?

When starting to analyze new data, the audience for the dashboard is crucial. Understanding each column and what story to convey is key to knowing our audience.

In this case our audience are travelers, I explored unfamiliar columns:

DR_NO: Likely a unique identifier for crime reports, possibly a “Department Report Number.”

Date Rptd: Represents the date the crime was reported.

Part 1-2: May indicate the type of crime, categorized as Part 1 and Part 2 crimes in the UCR (Uniform Crime Reporting) system.

Mocodes: Refers to modus operandi codes, which represent the method of operation of the crime.

Step2: Cleaning data

In my Alteryx workflow, I transformed column formats and divided certain columns to extract more insights. This process refined the data, revealing additional valuable information for my analysis.

 

Step3: Create rough dashboards to examine correlations between data

In creating my dashboard, I compiled a series of straightforward bar charts. While this may seem like a simple process, it proved to be highly insightful. By arranging these charts in one view, I was able to make connections and discover intriguing relationships between various attributes.

Step4: Build Dashboard

Should my dashboard pique your interest, feel free to visit it by clicking on the link.

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