Faris Shamsur is currently studying Data Science at the University of Warwick. Faris completed his A-Levels at KYUEM and is graduating in 2022. This personal statement was part of his successful application to Warwick (Computer Science & Data Science) and UCL (Computer Science).
The first time I solved a Rubik’s cube, I felt accomplished for having organised a clutter of colors. Eager to solve it even faster with more efficient methods, I spent hours observing videos of computers seamlessly solving the cube. It was then that I became captivated by the speed and efficiency of computer algorithms and wanted to solve other problems with computers. To this day, a machine’s ability to solve problems with the least amount of effort is the most beautiful part of computing to me.
My A-Level subjects helped me better understand computer science. Through Physics, I learned to understand abstract ideas by first principles, and this helped me program a permutations calculator by defining the factorial using recursion in Python. Further Mathematics challenged me to think creatively about problem-solving, which helped me solve increasingly harder problems, especially when working out Project Euler questions. I was able to create an algorithm to find the sum of even-valued terms in the Fibonacci sequence up to four million. However, some of my algorithms took too long to load. I later understood that this can be explained through algorithmic complexity. It was fascinating to see and find more efficient, neater algorithms to solve the same problem.
Steve Lohr’s book “Data-ism” opened my eyes to the true capabilities of computing and data when they are applied to real-life situations, such as using big data analysis to shape decision-making in businesses. I saw the application of this during my job-shadowing programme with a data scientist at CIMB, one of the largest investment banks in Asia. There I learned that companies can analyse huge amounts of data and gain insights which might not be seen if humans were to analyse them without computers. More than commercial use, however, I believe that artificial intelligence should be used to solve global challenges, such as climate change. Hence, I am interested in applying AI to analyse real-world issues. With that in mind, I devoted my time to studying deep learning by reading Andrew Trask’s “Grokking Deep Learning” and enrolled myself in “Move 37” by the School of AI. Here, I discovered how a simple neural network works and was intrigued to see the application of mathematics in it. I also learned concepts in machine learning such as supervised, unsupervised, and reinforcement learning. To learn about the ethics of AI, I listened to Siraj Raval’s talk on the need for a secure and value-aligned artificial intelligence which would prevent an AI apocalypse. I independently learned to classify Fisher’s Iris data set, and with that knowledge, I was able to create a gender classifier, which is able to predict the gender of a person given their weight, height and shoe size. I used Scikit-Learn’s decision tree classifier to classify the data into target groups. Using the TextBlob library and the Twitter API, I programmed a Twitter sentiment analyser to scan tweets and evaluate how negative or positive the effect of a tweet is, from a range of -1 to 1. Even though the program has drawbacks such as being unable to analyse the context of the tweets, I’m still amazed that programmes are able to analyse human sentiments.
As the executive committee member on the student council at my secondary school, I worked on organising large events with my team and undertook other responsibilities such as ensuring student welfare. These experiences taught me how to become a communicative team player and leader. My first exposure to Mathematical Olympiad was when I represented my school in a Nationals competition, gathering 2nd runner up. I was also awarded the best overall student and the best academician award in my secondary school.
I believe my analytical skills, inquisitive nature and problem-solving skills have given me the foundation to become a computer scientist. The possibility of solving challenges through computing is why Computer Science excites me.
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