Personal Statement: Data Mining Using Piecewise Vector Quantization
My first encounter with computers was at the age of 6. My father was a businessman who had to use computers to manage his finances. However, in our small village, computers were considered to be a fancy machine afforded only by the rich and my access to it was highly restricted. This only piqued my interest further and was one of the first indicators that I definitely wanted to work in a field related to them. Since my primary school, I nurtured a deep love for Mathematics. As a result, I developed sharp analytical and computational skills that helped me in decomposing complex problems, recognizing patterns and creating intuitive algorithms as I progressed through my education. I realised that the field of engineering would be a perfect fit
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The primary motivation behind choosing this particular topic was to focus on privacy-preserving clustering that protects the underlying attribute values of objects subjected to clustering analysis. In doing so, the privacy of individuals’ data would be protected. In this age of data mining, we felt that working on data privacy was of paramount importance. We worked in a group of 3 on this and my role specifically was to design and implement the ‘K-means’ algorithm that clusters the data and forms the basis for the data transformation.
In addition to my thesis, I worked on a solo project which involved developing an inventory management system for my college library with a tracking system for the books that included features like automated status updates for books lent/returned and alerts for students when their loan period expired. This system is still in use at my college today and it counts among one of my proudest accomplishments.
Post my graduation, I joined Deloitte as Advisory Analyst in the Enterprise Risk Services division. Here, my responsibilities …show more content…
Another key point was that, we were exclusively focused on the requirements given by the client when we perform data analysis. While this helps with the deliverables, I am very interested in taking this to the next level by learning how to analyse a much larger number of parameters based not only on client requirements, but also at an abstract level. The aforementioned topics will be the primary focus of my Graduate
The primary motivation behind choosing this particular topic was to focus on privacy-preserving clustering that protects the underlying attribute values of objects subjected to clustering analysis. In doing so, the privacy of individuals’ data would be protected. In this age of data mining, we felt that working on data privacy was of paramount importance. We worked in a group of 3 on this and my role specifically was to design and implement the ‘K-means’ algorithm that clusters the data and forms the basis for the data transformation.
In addition to my thesis, I worked on a solo project which involved developing an inventory management system for my college library with a tracking system for the books that included features like automated status updates for books lent/returned and alerts for students when their loan period expired. This system is still in use at my college today and it counts among one of my proudest accomplishments.
Post my graduation, I joined Deloitte as Advisory Analyst in the Enterprise Risk Services division. Here, my responsibilities …show more content…
Another key point was that, we were exclusively focused on the requirements given by the client when we perform data analysis. While this helps with the deliverables, I am very interested in taking this to the next level by learning how to analyse a much larger number of parameters based not only on client requirements, but also at an abstract level. The aforementioned topics will be the primary focus of my Graduate