I imagine myself working as a member of a medical research team in the future as a biostatistician. Most inspired by my readings of several alumni stories in the Biostatistics Program and other departments at the XXXX School of Public Health at the University of XXXX, I realize that I, too, crave this kind of passion, excitement, confidence, and commitment. The joy and expectation that I see in the smiles of the graduates. Hence, I am now applying to study for the MSc Degree in Biostatistics at the XXXX School at the University of XXXX, my first choice for continuing my education.
Born and raised in China and coming to Canada at 17, I have been here in Toronto for five years, two years of high school, and three years of university. I am a fourth-year student at the University of Toronto, majoring in Statistics with a minor in mathematics. I will be graduating and obtaining my Honours BS in June of 2021. Throughout the course of my undergraduate studies, I have increasingly become increasingly devoted to my analysis of Statistics. I began reading about it even in my free time. I could not be more dedicated to building a career in applied statistics because I see our field as unlocking the vast potential for a host of other areas. I consider the particularly rigorous and distinguished MSc program at the University of XXXX to be the best jumping-off place to launch my career.
Nothing excites me as much as the prospect of collaboration with other medical investigators to address and correct life-threatening issues in Public Health. Designing clinical trials and conducting medical experiments in the lab, I look forward to decades of giving my all to Biostatistics, analyzing data from all perspectives, and unlocking mysteries so that we can better fight the disease in question. Increasingly, I have fallen in love with the subject of statistics throughout my undergraduate studies. I enjoy using data to understand better, especially when iI how things are interconnected. I love it when the data confirms my thinking and especially appreciate it when it corrects it, steering me in the right direction on the right avenue of inquiry.
In preparation for graduate school, I took upper-level statistics courses, learning advanced statistical models, including Generalized Linear Model, the Generalized Linear Mixed Model, and Generalized Mixed Additive Model. I completed numerous projects in these courses, which helped me better understand the principal theoretical models driving statistics today. I could not be happier analyzing accurate, recent data and using models taught in class to find interesting results that help me understand the problem accurately, which is close to my intellectual interests. It may be a study of whether a certain kind of group is more likely to get a particular disease than others, for example, eventually using case-control studies, a study of a social problem in a specific demographical area, or a study of factors that affect an event, such, etc. Besides Frequentist methods, I also learned how to do Bayesian analysis using the NLA methodology. I appreciate theoretical statistics as the foundation of applied statistics. Some of the senior academic courses I took include STA347: Probability Theory, STA355: Theory of Statistical Practice, STA447: Stochastic Process, and STA452: Mathematical Statistics. Increasingly, such courses have given me a broad understanding of probability theory and statistics. I feel especially delighted when I see connections between different distributions and how to get one from the other.
I learned R and Python mostly on my own, along with SQL, Excel, Tableau, Stata, and SAS. A certified advanced SAS programmer, I also completed an internship in the summer of 2019, fu. I was immersed in SQL for the first several weeks, then watched Tableau lectures and read textbooks about machine learning and statistics. As a certified advanced SAS programmer, I also completed an internship in the summer of 2019, fully immersed in SQL for the first several weeks, then watched Tableau lectures and read One of the textbooks I studied, An Introduction to Statistical Learning with Applications in R, gave me my first extensive taste of classification algorithm algorithms when due to like Linear Discriminant Analysis, K-Nearest Neighbors, and Logistic Regression, increasing my understanding of machine learning and where its fullest potential lies. Most recently, I have been reading a book on Biostatistics named Fundamentals of Biostatistics by Bernard Rosner.
I ask for the opportunity to give my life to science for the good of us all in trying times when every soldier is needed on the front lines to keep death at bay and our loved ones safe from disease. I look forward to beginning as an entry-level researcher upon completing your program and spending the balance of my professional life in the laboratory.
Your time and consideration are appreciated, and I eagerly await a decision.