Dorwin Liang
Data Analytics & Visualization Specialist
I Turn Complex Data into Clear Visual Stories
I Turn Complex Data into Clear Visual Stories
In this project, I set out to understand how the New York Police Department (NYPD) can make more informed budgeting and resource allocation decisions based on crime trends using NYPD arrests dataset. By analyzing NYPD arrests from 2006 to 2023, I identified key patterns in arrests, crime types, and demographic factors. Using Python for data cleaning and Tableau for visualization, I discovered many insights such as higher arrest rates on specific weekdays, the prevalence of drug-related crimes, and the significant involvement of the different age groups in criminal activities. These findings can help the NYPD strategically optimize resources to enhance public safety and efficiency.
Tools Used
This analysis explores dog bite cases in NYC from 2015 to 2022, driven by my concerns as a pet owner about the prevalence of unleashed dogs in parks. Using data from the NYC Department of Health and Mental Hygiene, I wanted to uncover trends and generate insights to help prevent these incidents. Through data cleaning in Excel and visualization in Tableau, I identified interesting key patterns that may not be so obvious at first. The insights generated can guide NYC authorities in implementing targeted awareness and safety measures, particularly focusing on high-risk breeds and areas.
Tools Used
The purpose of this project is to identify health variables that can predict heart disease in patients. I analyzed the UCI Heart Disease Dataset to identify key health variables linked to heart disease using machine learning models including logistic regression, KNN, and Naive Bayes models in R. Through this analysis, I went through the entire data analytics life cycle from data cleaning to generating insights for health professionals to aid in preventing potential heart diseases in high risk individuals.
Tools Used
