Hello everyone! I’m Marine, a third-year McGill student majoring in Economics and International Development, and a PODS fellow! This Fall, I'll be entering my last semester of university. During my time at McGill, I've had limited exposure to hands-on data analysis experiences - skills are essential in academia and highly valued on the job market. In the PODS program, I saw an amazing opportunity to learn data science using R, and to put these freshly-acquired skills into practice during a policy-related internship.
In my second year, I became particularly interested in development economics. My coursework at McGill and internship experiences enabled me to further explore the intersections between my majors and strengthened my interest in development economics and public policy evaluation. In my classes, I learned about impact evaluation methods and was able to appreciate how data analysis techniques are at the core of development and public policy research and how rigorous findings can be used for effective policymaking.
Last summer, I interned with ELIMU Impact Evaluation Centre, a research NGO run by McGill Economics Professor Matthieu Chemin, based in a small town in rural Central Kenya. This internship provided me with the invaluable opportunity to be directly engaged in development research, specifically in project impact evaluation using randomized experiments.
The PODS boot camp in June taught me a lot in a short span of time. The training gave me solid foundations to be able to extract meaning from and interpret data and apply the findings to real world policy issues.
As part of the PODS program, I am currently completing an internship at Montreal Smart and Digital City, which recently became Montreal’s Urban Innovation Lab. The City of Montréal adopted an Open Data Policy in 2015 with open by default as a guiding principle, which presents compelling opportunities to perform policy-relevant analysis. The project I’m working on aims to help the city of Montréal have a better vision of social issues on its territory and make sure we respect privacy and the confidentiality of personal information when releasing new datasets. The analysis will be applied to housing sanitation, citizens requests (311, permits, fire departments), crime data, with cross-analysis with socio-demographic data from Statistics Canada. We may observe different trends or patterns in each of Montréal’s neighbourhoods - so I'm also learning techniques for working with geospatial data in R and recently started using QGIS, a Geographic Information System application.