Transformative Crime Data Dashboard: Insights & Decision Making

Transformative Crime Data Dashboard: Insights & Decision MakingTransformative Crime Data Dashboard: Insights & Decision Making

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Samuel Maina

• 20h

Crime Insights & Trends Analysis Dashboard

Project Overview This project involved the end-to-end processing of a large-scale crime dataset containing over 40,000 records. The goal was to transform raw, unstructured data into a high-impact, interactive dashboard to drive data-informed decision-making for police resource allocation and public safety awareness.

Technical Execution

Data Cleaning & Manipulation (SQL): Utilized complex SQL queries to clean and normalize a raw dataset of 40,000 rows. This involved handling null values, standardizing crime descriptions, and aggregating metrics by city, weapon type, and time of occurrence to ensure data integrity before visualization.

Analytical Modeling: Developed calculated fields and measures within the SQL environment to derive key performance indicators (KPIs), such as the average case duration and frequency of crime domains.

Data Visualization (Power BI): Constructed a comprehensive, interactive Power BI dashboard to visualize trends. Key features include:

Trend Analysis: A multi-year line chart tracking crime progression from 2020 to 2024.

Geospatial & Resource Analysis: A comparative analysis of crimes by city against police deployment levels to identify coverage gaps.

Demographic Insights: Integrated victim gender slicers and age-bracket segmentation to provide deeper social context to the raw crime numbers.

Results & Impact The final dashboard provides an intuitive interface for stakeholders to filter data by victim profile, time of day, and geographic location. This project demonstrates my ability to handle large datasets, perform rigorous data cleaning using SQL, and build professional-grade dashboards that turn complex information into actionable insights.

Data Analysis Database Development Data Modeling Microsoft Power BI MySQL

Rizki Arsyad

pro

• Jun 24

New exploration: Email Marketing Analytics Dashboard

Wanted to rethink how email marketers interact with their campaign data. Most tools feel like spreadsheets with extra steps, so I explored a cleaner approach: scannable metrics, inline campaign comparisons, and a visual system that gives data-dense views room to breathe.

contra.com/p/rsesWlEW-email-marketing-analytics-dashboard-concept

Shahriar Jubayer Eshat

• Jun 25

clean

Brian Waizer

• Jun 24

Developed an automated data analysis solution to process complex logistics and supply chain datasets to identify operational inefficiencies. By writing custom Python scripts to parse, clean, and analyze historical transfer data, I built an automated reporting workflow that surfaces key optimization opportunities for stakeholders. The final output provides clear, actionable insights into asset redistribution. Engineered robust, automated ETL pipelines and custom tracking workflows to manage and optimize decentralized assets across a large network of facilities. This project involved consolidating diverse, messy data streams from multiple warehouse locations into a single, cohesive source of truth. Built dynamic Power BI dashboards on top of this clean data to give executive leadership complete visibility into inventory health and demand forecasting. Architected and deployed a highly scalable backend solution designed to power internal business utilities and automation tools. This project utilized a modern, robust 6-microservice architecture to ensure high availability, efficient data processing, and seamless tool integration. Managed the entire deployment process across a hybrid cloud infrastructure to ensure stable performance and security.

Data Analysis AI Automation Scriptwriting Azure JavaScript Python

Liudmila Davydiuk

• Jun 24

Designed and built a full data pipeline — pulling from multiple sources, resolving inconsistencies, enriching with external variables, and delivering clean outputs through interactive Power BI dashboards stakeholders can actually act on. Designed and built a full data pipeline — pulling from multiple sources, resolving inconsistencies, enriching with external variables, and delivering clean outputs through interactive Power BI dashboards stakeholders can actually act on.

The starting point was a large, structurally complex dataset with inconsistent formats, missing values, and misaligned fields. The goal was a repeatable pipeline that transformed it into something reliable enough to drive real decisions.

The enrichment layer was the differentiator: layering in demographic and contextual variables from external sources to add depth that the raw data alone couldn't provide. The final output was a Power BI dashboard built for non-technical audiences — clear enough to act on, rigorous enough to hold up to scrutiny.

AI-assisted validation (Claude Enterprise) was used throughout to cross-check outputs and flag inconsistencies before delivery