Projects

London Bike Rides

Tableau

Github: London-bike-sharing
Tableau Public: London Bike Usage

This project involves cleaning, transforming, and preparing the London bike-sharing dataset for visualization and analysis. Key steps include:

  1. Data Extraction: Extracted and loaded london_merged.csv from a ZIP file.
  2. Data Cleaning: Renamed columns for clarity, normalized humidity values, and mapped encoded values for seasons and weather to readable labels.
  3. Data Preparation: Transformed and saved the cleaned dataset as an Excel file (london_bikes_final.xlsx) for Tableau visualizations.

The analysis explores bike usage patterns based on weather, seasons, and other factors, enabling deeper insights into trends.

Volstock project

Python, SQL, AWS, Power BI

Github: Volstock-project

A data pipeline was developed to extract data from a live operational database, transform it into a star schema, and load it into a data warehouse on AWS.

Key features include:

  • Automated workflows for data ingestion, transformation, and loading.
  • Real-time updates to the warehouse within 30 minutes.
  • Secure and monitored processes with logging via AWS CloudWatch and email alerts for failures.
  • A BI dashboard to visualize insights from the warehouse.

The project highlights the use of Python, SQL, AWS, and data modelling to create a reliable and automated data platform.

Electric Vehicle Analysis

PostgreSQL, Python

Github: Electric-Vehicle-Population-Data

This project analyses electric vehicle (EV) population data using Python and PostgreSQL. The dataset includes details on EV manufacturers, models, ranges, and geographical distribution.

Key analyses include:

  • Manufacturer trends: Identifying the top EV manufacturers and their market share.
  • Range comparisons: Calculating average ranges for different vehicle types like BEVs and PHEVs.
  • Regional insights: Examining the distribution of EVs across counties.
  • Policy alignment: Analysing EV eligibility for clean alternative fuel programs.

The findings are visualised using Python libraries such as Matplotlib and Seaborn, and all insights are stored in a PostgreSQL database for efficient querying and reporting.

Optimising the EVolution

Power BI, Excel

Github: Optimising-the-EVolution

This project evaluated the feasibility of establishing an electric vehicle (EV) forecourt, comparing a holiday destination with a motorway. The analysis focused on forecasting EV demand, estimating daily customer volumes, optimising charger capacity to balance efficiency and cost, and calculating ROI to determine the payback period. The findings provide a data-driven foundation for strategic investment in EV charging hubs, supporting the growing demand for sustainable infrastructure.