Career Goals Statement: Becoming Proficient in Programming Languages

Career Goals Statement
I can spend a lot of time and take up more than half the space in this statement talking about my academic achievements and how they make me an ideal candidate for the Masters of Science in Computational Finance (MSCF) program at (University). However, I believe my CV captures everything academic that I could reiterate here. In this statement, I would like to focus on the things that attract me to (University), why the MSCF program is a good fit for me, what matters to me, and where I will take the knowledge and skills I will acquire from (University).
One of the main reasons why (University)’s MSCF program stands out for me is the presence of a distinguished group of faculty members. True to what CMU’s website says, the faculty members are some of the most distinguished and a key part of what makes the MSCF program stand out. From the little information I have gathered, I have discovered that a majority, if not all, of the faculty members are involved in some of the most groundbreaking researches in the world. One particular faculty member, Professor Martin Larsson, stands out for me, and I believe we share a lot of similarities. From his profile, I have seen that the professor’s research touches on key areas in mathematical finance, specifically stochastic analysis. Currently, my future research goal is in the area of stochastic analysis and stochastic portfolio theory. With such an alignment, I believe that I stand a great chance of being groomed to attain my vision and become the person that I would like to become in the future.
The MSCF program is a good fit for me because of the solid theoretical and practical foundation that I have in the area of quantitative research at hedge funds. In my undergraduate stage, I was provided the opportunity to have a rigorous scientific background with regards to developing, analysis, and implementation of statistical models for computerized financial trading strategies. The encounter I had with mathematical finance led me further along a path of probability and statistics. I acquired knowledge in applied probability theory, including aspects not limited to Markov chains, stopping time, and the Brownian motion. Further, I also acquired knowledge and enhanced my skills in statistics, with specific areas of focus, including maximum likelihood estimation, unbiased estimation, and R language in statistical models and methods. The knowledge acquired above opened the door for further indulgence in Mathematical Finance where I was introduced to probabilistic and stochastic modelling for investment strategies. Some of the areas that stood out for me included risk-neutral measure, arbitrage pricing, and Black-Scholes formula. As outlined above, the foundation has indeed been laid for furtherance in the field of Mathematical Finance.
I would be doing this statement a disservice if I forget to mention the MSCF Ma...