GDP Data Analysis with Python

Project Overview

This project explores a GDP (nominal) per capita dataset using Python to demonstrate practical data analysis, cleaning, exploration, statistical analysis, and visualisation skills.

The analysis was completed as part of my Data Technician training and developed further as a portfolio project.

Tools and Technologies

• Python
• pandas
• NumPy
• Matplotlib
• Seaborn
• Jupyter Notebook / Google Colab

Analysis Performed

The notebook demonstrates:

• Loading and inspecting structured CSV data
• Exploring dataframe structure and summary statistics
• Identifying and handling missing values
• Filtering countries and geographic regions
• Comparing GDP values across countries and regions
• Grouping and aggregating data
• Calculating averages and other descriptive statistics
• Using NumPy for numerical analysis
• Creating charts to explore GDP distributions and relationships
• Investigating correlations between variables
• Identifying potential outliers
• Applying the IQR method to examine the effect of extreme values
• Comparing statistics before and after outlier treatment

Repository Contents

• gdp-data-analysis.ipynb - complete Python analysis with code, outputs, and visualisations
• GDP (nominal) per Capita.csv - dataset used in the analysis

Key Skills Demonstrated

This project demonstrates practical experience in data cleaning, exploratory data analysis, data transformation, statistical reasoning, visualisation, and communicating analytical findings using Python.

How to View the Project

Open gdp-data-analysis.ipynb directly in GitHub to view the complete analysis, including code cells, outputs, tables, and visualisations.

Author

Ragad Alfatih
Data Analyst | Business Intelligence | Bioinformatics