Overview:
Hi! I'm applying to MSc Data Science in the UK. I want to keep it below 500 words as much as possible due to word limit.
While doing a method validation for a pharmaceutical product in November 2020, I checked my phone and read that Deepmind has successfully solved the 50-year old protein folding problem. Naturally, the inner chemist and self-taught programmer in me were stunned and thrilled by the recent milestone the company has achieved. I had known the enormous challenge of protein folding when I was an undergraduate and understood the incredible number of configurations proteins can assume. Therefore, I thought that maybe the problem would remain unsolved for at least a couple of years more. However, I never imagined that machine learning could solve such a significant scientific problem ever since I started self-studying data science and programming in college during my spare time. That breakthrough served as my motivation to be more tenacious in pursuing a career in data science, knowing that it can be applied to various science disciplines and help solve obstacles that baffled scientists for ages.
As a scientist involved in the pharmaceutical industry, that milestone reignited my aspiration of applying data science in drug discovery and design. Discovering life-saving medicinal compounds still encounters significant issues that need solving despite our current technology. For that reason, I always find a way to improve my knowledge in data science to acquire the skills required for the problem. I am currently studying "Discrete Mathematics - An Open Introduction" by Oscar Levin and "Elementary Statistics - A Step by Step Approach" by Allan Bluman to familiarize myself with the mathematics used in the field. Data Structures and Algorithms is also one of my focuses, and I study the topic by reading "The Algorithm Design Manual" by Steven Skiena. In addition, I regularly solve online coding problems in HackerRank and Leetcode to practice the acquired skills. Finally, I participated in online courses and seminars like Kaggle's 30 days of ML and "Intro to Data Analysis with Python" by She Loves Data and ThoughtWorks. Participating in those activities allowed me to interact with the community and gain insight into the skills needed in the field. Intensively practicing those topics allowed me to gain the necessary knowledge and self-confidence to volunteer as the lead developer of a method validation software that calculates the results of method validation procedures using Python, various statistical methods, and data science packages.
Seeking credible learning materials and constantly practicing the skills allowed me to learn the basics and scratch the field's surface, giving me the essential background needed for the program. However, I believe that nothing can substitute professional guidance from experts. In addition, the application of data science in drug discovery requires extensive knowledge of different topics. Hence, my eagerness to seek higher education for supervision and to deepen my understanding, correct the bad habits I acquired, and fill in the gaps in my knowledge. Finally, I have grit, perseverance, and self-discipline, which I exercised during years of debugging software, solving coding problems, and attending online data science courses, making me an excellent candidate for the program.
Hi! I'm applying to MSc Data Science in the UK. I want to keep it below 500 words as much as possible due to word limit.
PERSONAL STATEMENT
While doing a method validation for a pharmaceutical product in November 2020, I checked my phone and read that Deepmind has successfully solved the 50-year old protein folding problem. Naturally, the inner chemist and self-taught programmer in me were stunned and thrilled by the recent milestone the company has achieved. I had known the enormous challenge of protein folding when I was an undergraduate and understood the incredible number of configurations proteins can assume. Therefore, I thought that maybe the problem would remain unsolved for at least a couple of years more. However, I never imagined that machine learning could solve such a significant scientific problem ever since I started self-studying data science and programming in college during my spare time. That breakthrough served as my motivation to be more tenacious in pursuing a career in data science, knowing that it can be applied to various science disciplines and help solve obstacles that baffled scientists for ages.
As a scientist involved in the pharmaceutical industry, that milestone reignited my aspiration of applying data science in drug discovery and design. Discovering life-saving medicinal compounds still encounters significant issues that need solving despite our current technology. For that reason, I always find a way to improve my knowledge in data science to acquire the skills required for the problem. I am currently studying "Discrete Mathematics - An Open Introduction" by Oscar Levin and "Elementary Statistics - A Step by Step Approach" by Allan Bluman to familiarize myself with the mathematics used in the field. Data Structures and Algorithms is also one of my focuses, and I study the topic by reading "The Algorithm Design Manual" by Steven Skiena. In addition, I regularly solve online coding problems in HackerRank and Leetcode to practice the acquired skills. Finally, I participated in online courses and seminars like Kaggle's 30 days of ML and "Intro to Data Analysis with Python" by She Loves Data and ThoughtWorks. Participating in those activities allowed me to interact with the community and gain insight into the skills needed in the field. Intensively practicing those topics allowed me to gain the necessary knowledge and self-confidence to volunteer as the lead developer of a method validation software that calculates the results of method validation procedures using Python, various statistical methods, and data science packages.
Seeking credible learning materials and constantly practicing the skills allowed me to learn the basics and scratch the field's surface, giving me the essential background needed for the program. However, I believe that nothing can substitute professional guidance from experts. In addition, the application of data science in drug discovery requires extensive knowledge of different topics. Hence, my eagerness to seek higher education for supervision and to deepen my understanding, correct the bad habits I acquired, and fill in the gaps in my knowledge. Finally, I have grit, perseverance, and self-discipline, which I exercised during years of debugging software, solving coding problems, and attending online data science courses, making me an excellent candidate for the program.
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