SoP for data science master
Please help me review my SoP for data science. I think I might've made tons of grammatical errors.
Plus, could you give me some advice on the structure? Like I mentioned my exchange experience but
I also want to emphasize this part. However, I have no idea how to go further.
Thank you guys!
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My inner motivation and enthusiasm in data science came from one introductory class to information system where the professor exemplified the power of the combination between machine learning algorithms and big data. An online music app called Music Cloud was taken as the example, which implemented algorithms that analyzed customers' data and recommended music to them. The accuracy and efficiency of its recommender system shocked me so deeply that I aspired to dive into the field since then.
As an undergraduate, I was firstly admitted into the department of Mechanical Engineering, where I fortunately came into the introductory class to information system mentioned above. During this class, I built significant programming skills developing computer gaming software architecture with my two friends while some of my classmates are still struggling with the pointers. I was solely responsible for the animation of the design and partially responsible for the sound effect. My efforts in the project resulted in an A for this class, which helped me pass the selective major change exam and successfully entered the department of Information Engineering.
With more and more exposure to specific topics in data science, I decided to implement the algorithms on paper and solve real-world problems associated with industrial practices. Consequently, my first nation funded project The Transportation Info Collecting and Analysis System based on DJI UAV came into being in my sophomore year. Working with engineers from DJI and members of DJI club, I managed to complete the development of a program in Python that extracted and analyzed features from image data, making use of SIFT algorithm and SVM. Considering the optimization of SVM became the biggest problem finishing the project because of my ignorance of professional knowledge in statistics and convex optimization. I had to take MOOC courses like Andrew Ng's machine learning, as well as read the book PRML in order to reinforce my background. This experience showed the significance of comprehension of the principles of machine learning. I started to bury myself in the pile of papers and delve theories of machine learning algorithms.
After a whole year of enchantment in machine learning, I suddenly realized that not only the understanding of theoretical topics, but also practical applications are important. Fortunately, I was offered a precious opportunity to study computer science in UCSD for a whole quarter, where I observed computer science education in best quality, enthusiastic students and professors, countless state of art researches, close collaborations between academia and industry. As a result, I made up my mind to pursue further education in the USA.
During my exchange period, I worked as a research assistant under the supervision of Dr. Voytek Bradley. My main task was to find the relation between people's preferences in music genres and different weather types. Millions of lines of weather parameters were scrapped and clustered through KMeans into 6 different types, which were used subsequently as weather labels. Trend data about music genres from Google Trend were cleaned and imported as target parameters. The relationship of these two kinds of data were explored and modeled with the help of an RNN, which I implemented using Pytorch. This model showed its excellent accurate rate when making predictions on test sets. For implementation, and demonstration of this project, I received excellent feedbacks from the Supervisors.
Practical experience on campus was not enough to satisfy my pursuit in data science and thus I was looking forward to opportunities in industry. Currently, working as a software developer in National Instrument Shanghai, I'm helping my team ameliorate Data Plugins (used to read in large-scale data in different formats) for Diadem and modify corresponding products according to customers' feedbacks. Throughout my work, I find out Data Science plays a more and more important role in future marketing and business decision making, which consolidates my decision to pursue my career as a data analyst.
Looking back at past three years of my academic endeavors, I believe I would be the best candidate for the master program in Center for Data Science at NYU. Theoretical courses in statistical learning, data mining, deep learning as well as adequate internship both in academic and industry will be offered, which exactly fulfills my belief in data science: use theories to contribute real life. Besides, as a world famous city with diversified industries, NYC is full of opportunities for me to utilize cutting-edge technologies to create values for myself and people in the world.
I aspire to work as a data analyst, and New York University would enable me to realize aspirations and interests that exceed my existing career parameters. The guidance of NYU's remarkable faculty and the stimulating ethos of its research labs would enable me to build on my deep but interrelated interests, background and research intentions, offering me the foundation on which to conduct ground-breaking projects. I believe I would make an excellent contribution to the atmosphere at NYU and subsequently be a successful data analyst with my passion for data, leadership and communication skills polished through work.
