• Managed DHS datasets from several Sub-Saharan African countries;
• Performed statistical analyses;
• Prepared detailed research reports, presented findings and coached several interns.

• Assisted in collecting, wrangling and organizing data from WHO for several Sub-Saharan African countries;
• Achieved statistical analyses to assist senior researchers in drawing meaningful research findings;
• Developed machine learning algorithm to assess the efficiency of health aid.

• Collected data and analyzed the evolution of the French car market over the last years;
• Applied time series analysis techniques to forecast the French car market in the upcoming years;
• Formulated actionable policy recommendations based on data-driven insights to guide future strategies and decisions in the French car market.

• Developed a data collection application for a survey data and data management;
• Applied the KE XU methodology as outlined by the WHO to analyze household health expenditures and identified households vulnerable to catastrophic health expenditures;
• Conducted an examination of the intricate interplay between poverty and health expenditures.

• Managed and performed data quality analysis on survey data from Niger;
• Estimated malnutrition rates using survey data from Niger;
• Interacted closely with the NIPN team to use statistical software and interpret results.

During my current PhD position, I have had the privilege of serving as a teacher assistant where I have been actively engaged in facilitating tutorial sessions for students in the fields of statistics and probabilities. My course load includes:

-Introduction to R Programming: this 8-hour course is designed for Health Economics students to develop essential data analysis and visualization skills using R and RStudio. We covered topics such as: * Fundamentals of R and RStudio (data types, importing data, basic manipulations); * Data wrangling with tidyverse (filtering, transforming, and combining datasets); * Data visualization with ggplot2 (creating and customizing professional graphics); * Real-world applications in Health Economics (COVID-19, blood storage, medical data analysis). 📚 Course materials are provided in an R Markdown book format here for interactive learning.

• Inferential statistics with Pr Anne Viallefont. We covered topics such as:
  • estimation (definition of an estimator, bias, variance, convergence, etc.);
  • confidence intervals and significance levels (for mean, variance, bias correction, etc.);
  • hypothesis testing (comparing two means, variances, or distributions, etc.).
• Probabilities and mathematics with Pr Marie Eliette Dury. We covered topics such as:
  • probability distributions and approximations (normal, Poisson, binomial, etc.);
  • matrix calculation (calculation of the determinant, eigenvalue, inverse, diagonalization, etc.);
  • numeric sequence (arithmetic and geometric sequence, recurrence equation, etc.).