Work Experience

Learned the end-to-end data science workflow, including data preprocessing, analysis, modeling, evaluation, and visualization.

Practiced Python for data analysis, machine learning, and data visualization.

Built data visualizations to communicate insights and support decision-making in the education sector.

Learned cloud computing fundamentals for scalable data storage, processing, and deployment.

Explored the use of data science and cloud computing to support policy, monitoring, and government decision-making.

Processed large-scale spatial big data from satellite imagery to support geospatial analysis and land cover classification.

Developed an end-to-end machine learning workflow for rice phenology and land cover classification using Sentinel-2A satellite imagery.

Evaluated 3 models across 3 dataset scenarios with 3 spectral indices.

Achieved 87.26% accuracy and 83.61% Kappa score using Random Forest.

Integrated model outputs into a web-based application with interactive maps using the Google Earth Engine API.