University of Virginia sop essay
My fascination for data science began in my sophomore year, when I signed up as a research assistant for Professor Oindrila Dey. At the time, I had only taken the introductory course in programming, and had basic statistical and calculus knowledge. Through enthusiasm for my first assistantship, I learnt R and Tableau in two weeks and began producing various, insightful data visualizations, which were used in the final version of my professor's paper: "Is Electric Street Car a Sustainable Public Transport System in India? A Demand Side Analysis".
With this newfound passion, I wanted to gather more experience in data science, therefore after multiple interviews, I landed an internship at Jash Data Sciences. I developed an end-to-end web application, which combined a U-Net CNN model and locality sensitive hashing algorithm to provide similar jewellery designs given an input image. I also worked on designing algorithms to extract and store vital information from public notices in a secure, structured database for the local municipal corporation.
Having worked in the professional sector, I was determined to contribute my data analytical skills for social good. Hence, I secured my second internship with WageIndicator, a foundation based in Amsterdam, whose mission is to provide labour market transparency for all employers and employees. I led the Data Analysis team, comprising of 10 student interns, to create and derive meaningful insights from salary and minimum wage datasets spanning several countries, primarily using STATA and Tableau. Along with the interns, I communicated with experts in data analysis from Italy and Netherlands to deliver meaningful data stories, published on the organisation website. I was amazed by the reach of data, and worked on more academic-oriented data projects; such as modelling the HIV/AIDS epidemic until 2050. For my undergraduate thesis, I explore an unprecedented approach to train a deep learning model on weather images to forecast temperature.
Through the multiple projects I took, both academic and professional, I have enjoyed the interdisciplinary approaches used to provide solutions to real-world problems. I intend to further explore the confluence of machine learning and predictive modelling with other fields such as public policy, healthcare and politics. The University of Virginia's Masters in Data Science program is my first choice because of its comprehensive course outline. I believe my strong mathematical and statistical background has given me the tools to excel in courses such as STAT 6021 and SYS 6016. I am specifically interested in researching a problem in politics under the guidance of Professor Jonathan Krapko and even work on employing simulations with Professor Gerard Learmonth for the capstone projects.
Upon graduation from the University of Virginia, I will be a competent data scientist, and I intend to join government organisations and/or thinktanks where I am confident that I shall contribute by providing effective data-driven solutions to policy-making problems.
