valerieranaivo.github.io

Valérie Ranaivo

Data analyst

Data cleaning
Data processing
Data exploration
Data visualisation
Data predictive modelling
Data interpretation

About me

Turning raw data into insights and visualisations that help to uncover new learning is my passion, and what drives me forward! I hold a Bachelor’s-level diploma in Data analytics from OpenClassrooms / the National Economics and Statistics School of France and I have several years experience supporting non-profit organisations in their decision processes. This portfolio showcases a few projects I have worked on.

Poverty indicators - World Bank

Access to data: https://pip.worldbank.org/home
Description: This interactive dashboard provides a tool to visualise and compare populations in the world towards the $2.15 poverty line.
Technology: R, dplyr, ggplot2, shiny.
Poverty line analysis picture

Education indicators - UNESCO

Access to data: https://github.com/iiepdev/HackingEDPlanningV2-Challenge6?utm_source=pocket_mylist
Description: This project, part of the Hacking EDplanning Hackathon organised by IIEP-UNESCO, aimed at improving educational indicators from Latin American countries disaggregated by socio-economic variables.
Technology: Python, Pandas, Numpy, Matplotlib, Seaborn. EDHacking analysis picture

World poultry market analysis - FAO

Access to data: https://www.fao.org/faostat/en/#home | https://unstats.un.org/unsd/methodology/m49/overview/
Description: This project aimed to identify countries with the best export potential for poultry, using different clustering methods.
Technology: R, tidyverse, ggplot2, FactoMineR, factoextra, cluster, heatmaply. Poultry market analysis picture

Water access in the world - WHO and FAO

Access to data: https://www.fao.org/faostat/en/#home | https://www.who.int/data/gho/data/indicators
Description: This data visualisation project highlights the countries most affected by lack of water through a story built from dashboards and worksheets.
Technology: Tableau, SQL. Water access analysis picture

Sales analytics - Bookshop

Description: In this project, I measured KPIs to gauge sales performances and used inferential statistics to reveal correlations between different customer and product characteristics.
Technology: R, tidyverse, ggplot2, lubridate. Bookshop sales analytics picture

Overview of undernourishment - FAO data

Access to data: https://www.fao.org/faostat/en/#home
Description: This descriptive analysis uses data from 2017 on total population and population affected by undernourishment, as well as food availability and food aid values.
Technology: Python, Pandas, Numpy, Matplotlib.
Undernourishment analysis picture