Hello! My name is Saeesh. I’m currently going into the third year of my Bachelor of Arts degree, double majoring in Environment and Development and Urban Studies. I’m interested in environmental design, and the interplay between design decisions and policy choices. I am grateful to be one of the PODS fellows in the 2019 cohort.
My academic interests are quite interdisciplinary – I believe it is impossible to take a siloed approach to environmental policy and design. While pursuing my BA at McGill, I felt restricted by the limited exposure I had to quantitative disciplines. Being interested in a topic that is informed by many disciplines, not having quantitative skills often meant that my understanding of environmental issues was implicitly limited. It was difficult to have even a basic conceptual basis that could help me learn from - and be critical of - quantitative studies of environmental issues.
As data has increasingly come to permeate decision making in both policy and urban design, being a critical and responsible designer today necessitates data literacy. In aspiring to work in urban policy and design, I believe that I am responsible for being well-versed enough in data science to effectively judge its uses, and be critical of complex data-driven decisions. The PODS program has been perfect in helping me understand the skills and language of data science. Being a part of the program has not only meant that I have learned a variety of new skills, but also that I have learned how to further engage with the data science world on my own terms. This became quite apparent to me at my internship site, when my work required me to learn a variety of new and entirely unfamiliar skills in programming. The skills I learned during the Bootcamp, the support network through PODS and my whole cohort of fellows all helped me feel capable in exploring unfamiliar avenues in data science, and learn to use my existing knowledge as a basis for picking up new tools.
I am currently interning at Data Sciences (DS): a data, marketing and analytics firm that helps clients leverage data in creative ways to support their operations. My project is focused on evaluating approaches to accurately identify links between datasets. The goal is to generate a matching algorithm that can reduce the burden of duplicates in production, and help DS better serve its clients. At the time I started, the project’s scope was far outside my comfort zone. It required me to teach myself data management in SQL and Python, develop a framework for assessing algorithmic performance and find ways to communicate relevant information from within the detailed minutiae of person matching. The experience has been challenging and has pushed me to develop many new skills, as well as learn more about the complexities of bringing data to bear on decision making.
I am extremely grateful for the education and critical insight the PODS program has given me. I am also grateful for all the personal connections that I have made with the members of my talented cohort, as well as with other supporters of the PODS program. Given all that has suddenly become possible in a few short months, I am excited to see what the future now holds!
