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Day 4 – Crime statistics

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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Day 4 arrived, and the weight of fatigue had undeniably settled in for this project. However, drawing upon the experience accrued over the past few days and fueled by a topic that truly ignites my passion—crime statistics—I managed to persevere unscathed.

The Topic:

Our dataset presented the crime data of San Francisco over the years, cataloging various crime categories and the corresponding suburbs where these crimes occurred.

The Approach:

Through extensive reading on San Francisco crime patterns, one region stood out prominently: Tenderloin, notorious for its prevalence in violent crimes. Leveraging commonly used crime statistics such as crimes per 100,000 people, my aim was to reconstruct the reports provided by the San Francisco Police Department with a focus on crime in the Tenderloin district.

The Design:

In line with San Francisco’s emblematic colors of black and gold, the dashboard embraced this theme under the title ‘Black Amongst the Gold.’

Final Thoughts:

Feedback received was laudably succinct and insightful, with particular appreciation directed towards the use of San Francisco’s colors and metrics, as endorsed by their police department. The design, while simple, was effective in delivering key insights.


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