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Vehicle Crash Data Analysis: SQL, Python, Tableau.

Project Objective

This project aims to analyze vehicle crash data to identify patterns, risk factors, and potential safety improvements through advanced SQL data analysis. The primary objectives are:

  1. To investigate the relationship between environmental conditions (road surface, weather, lighting) and crash severity
  2. To identify temporal patterns in crash occurrences across different months and conditions
  3. To analyze the correlation between citations issued and crash outcomes
  4. To develop a comprehensive risk assessment model for different driving conditions
  5. To provide data-driven recommendations for improving road safety, particularly in adverse weather conditions

This analysis showcases SQL data manipulation and analysis techniques using MySQL, demonstrating proficiency in database design, query optimization, and statistical analysis. The findings from this project can potentially contribute to transportation safety policy and driver education programs in regions with similar climate challenges.

The repository contains all SQL and Python scripts used for data preparation, exploratory analysis, statistical evaluation, and the Tableau dashboard. It also includes documentation of methodologies, findings, and project insights.

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Skills Demonstrated in this Project

Technical Skills

  • SQL/MySQL:
    • Complex query writing and advanced database design
    • Query optimization through indexes and efficient JOIN operations
    • Development of stored procedures for reusable analysis routines
    • Data cleaning and transformation to prepare raw data for analysis
    • Statistical analysis using SQL to calculate metrics, trends, and correlations
  • Python:
    • Data analysis and visualization using libraries like Pandas, Matplotlib, and Seaborn
    • Data wrangling and manipulation for exploratory analysis
    • Development of automated scripts to streamline analytical workflows
    • Creation of interactive visualizations to complement Tableau outputs
  • Tableau:
    • Design and development of dynamic dashboards to visualize key insights
    • Integration of interactive elements for enhanced user engagement
    • Presentation of multi-dimensional data for storytelling and decision-making
  • Data Modeling:
    • Designing efficient data models, including relationships and appropriate schema for analysis
  • Data Visualization:
    • Clear representation of trends and insights through graphical plots and dashboards
    • Ensuring the visual narrative aligns with analytical goals
  • Project Documentation:
    • Comprehensive reporting of methodologies, findings, and recommendations
    • Structuring documentation for clarity and ease of understanding for stakeholders
  • Troubleshooting and Optimization:
    • Resolving compatibility issues across libraries and frameworks
    • Ensuring performance efficiency in the SQL scripts, Python environment, and Tableau workflows

Project Highlights

  • Integration of MySQL and Python for robust analysis and pre-visualization preparation
  • Use of Tableau to create visually appealing and interactive dashboards for data storytelling
  • Detailed documentation to support reproducibility and practical implementation of findings

Data Analysis Skills

  • Exploratory Data Analysis: Uncovering patterns and relationships in crash data
  • Cross-tabulation Analysis: Examining relationships between multiple variables
  • Time Series Analysis: Identifying temporal patterns in crash occurrences
  • Risk Factor Identification: Quantifying the impact of various conditions on crash outcomes
  • Data Aggregation: Summarizing and grouping data for meaningful insights
  • Key Performance Indicator Development: Creating metrics to measure safety factors

Project Management Skills

  • Data Pipeline Development: Creating a systematic workflow from raw data to insights
  • Documentation: Creating clear documentation of processes and findings
  • Problem Formulation: Defining clear analytical questions and approaches
  • Data Visualization Preparation: Structuring data for effective visualization
  • Research Methodology: Applying analytical approaches to answer specific questions
  • Results Interpretation: Drawing meaningful conclusions from data analysis

Domain Knowledge

  • Transportation Safety: Understanding crash data variables and their significance
  • Geographic Considerations: Analyzing data with awareness of Alaska's unique conditions
  • Regulatory Understanding: Working with citation and compliance data
  • Environmental Impact Analysis: Assessing how road and weather conditions affect safety

This project demonstrates a comprehensive skill set that combines technical database expertise with analytical thinking and domain-specific knowledge in transportation safety.