Computational data science notebooks for development studies

How to cite this project:

Mendez C. (2025) Computational Data Science Notebooks for Development Studies. Zenodo https://doi.org/10.5281/zenodo.15250204

Contribute and provide feedback at https://github.com/cmg777/ds4ds

Basic statistics and econometrics

• Mendez C. (2024) Gapminder introduction to data science using Python
• Mendez C. (2025) Introduction to statistical differences and relationships using Python
• Mendez C. (2024) Descriptive statistics and multi-boundary mapping using Python
• Mendez C. (2025) Use regressions to explore relationships using Python

Economic growth and development

• Mendez C. (2025) Introduction to growth equations using Python
• Mendez C. & Leiva F. (2023) The Solow growth model and its convergence prediction using Python
• Mendez C. (2023) The Solow growth model and its convergence prediction using R
• Mendez C. (2021) Convergence clubs in labor productivity and its proximate sources using R

Exploratory data analysis

• Mendez C. (2024) Exploratory spatial data analysis of municipal development in Bolivia using Python

Causal inference

• Mendez C. (2025) Introduction to directed acyclical graphs (DAGs) using R
• Mendez C. (2024) Heterogeneous treatment effects via two-stage DID using R
• TBA

Machine learning

• TBA
• TBA

Spatial econometrics

• TBA
• TBA

Bayesian econometrics

• TBA
• TBA

Feature engineering and geocomputation

• Mendez C. (2025) Regional dynamics of luminosity-based GDP using Google Earth Engine
• TBA