Personal Statement

As a big fan of martial arts fiction, I found the cinematic adaptation of the novel “Fights Break Sphere” really ridiculous. Keen on using data mining to predict the developing trends of business, I decided to adopt the method of text mining to study adaptation, with the aim to provide some feasible advice for the industry and to avoid the terrible adaptation of good novels. Inspired by this, I led a team to participate in the Innovation Creativity Entrepreneurship organized by XX. Starting with the text with audience comments, we used Crawler to obtain data and analyze “extreme concerns (high-risk points of adaptation)”. By applying text mining method, we predicted the audience responses to the adaptation direction. In the end, our team won the third prize. I was not only delighted by this experience of using historical data to offer enterprises decision-making analysis, but also aware of the necessity to further strengthen my mathematical and computing abilities. In the future, I aspire to master the cutting-edge data science methods to perform data mining and analysis for the industry. Hence, it is my sincere hope to pursue the Master of Data Science programme at The XX.

My undergraduate study in XX has laid a solid foundation in data science. I have made great efforts to improve my data mining ability. In the course of Business Intelligence, I learned various algorithms and principles of machine learning for the purpose of application. In the final course report, I applied Crawler to get Netease Cloud Music data of 140,000 different users and employed the thinking of SVD matrix decomposition to realize the recommendation algorithm of music system. In the end, I completed the code writing independently. This research report earned me 92 points and I won the praise and encouragement of my course teacher.

After learning a lot about machine learning and natural language processing, with my business background, I published my first paper under the inspiration of this teacher. In 2020 Big Data Analysis of Internet Finance course, I crawled the historical data of Eloan P2P platform, completed the sampling of unbalanced samples with the clustered BSL algorithm, improved the traditional SMOTE sampling method and integrated natural language processing to calculate WMD text similarity. Finally, I used eight machine learning algorithms for comparison, greatly reducing the time complexity and improving the accuracy of P2P default lending risk model. I was the only student who got 100 points in this course. The course paper was successfully published in Finance.

With the enhancement of my computing proficiency, I perceived the crucial role that mathematics played in tackling issues related to data science. Therefore, I focused on sharpening my mathematical skills in preparation for the future study in data science. In April 2020, I took the initiative to improve my mathematical expertise by participating in a research project presided by Associate Professor Yang. In the development of a stock decision-making trading system, I was mainly responsible for calculating the risk and fall of stocks with time series method. Based on the most fundamental statistical test results of the ARIMA model, I achieved the automatic selection of the difference order d, p and q. With the incremental learning combined with the time series, the prediction accuracy of the model was improved. In the meantime, we began to study the EVaR (Entropy Value-at-risk) portfolio optimization model based on uncertainty theory and apply mathematical methods to prove its properties and application algorithms. In 2020 Mathematical Contest in Modeling (MCM), I employed this theory and successfully completed the modeling contest.

In addition to academic and research experiences, I also actively scoped out many competitions and internships to enrich my practical experience. In May 2019, I signed up for “Zhong Qing Bei” Undergraduate Mathematical Contest in Modeling. I chose a model that combined gray system prediction and neural network algorithms to solve the problem of inaccurate predictions with small samples and use simulated annealing algorithms to complete the optimal selection of parameters. With 30 relatively developed cities as the learning goal, the model of the optimal number of urban high-speed trains was finally obtained. By virtue of the outstanding performance, I was awarded the national third prize in this contest. Four months later, I used machine learning methods like random forest to analyze the problem of brain drain rate in China Mobile and provided some suggestions for the company internal talent management. In March 2020, I engaged in data analysis at the Computer Network Information Centre, Chinese Academy of Sciences. The extensive use of Python and SQL enabled me to enhance the capability in modeling and cord writing of industrial data analysis. Besides, serving as a strategic consulting intern at KPMG, I got fully involved with feasibility analysis of a partial equity acquisition project. This internship exercised my ability to perceive and analyze the overall financial environment from a strategic perspective.

In the future, I embrace the career aspiration to become a data analyst. Although I have made every endeavour to build up my programming and mathematical techniques, it is quite essential for me with the business academic background to pursue the systematic and intensive postgraduate education in data science, especially the Master programme of The XX. From several internships, I come to realize that in the era of big data, the mastery of cloud computing, distributed system data storage, extraction and visualization is critical to a data analyst. Your modules like Cluster and cloud computing and Visualization and visual analytics will give me the opportunity to acquire relevant knowledge and skills. In addition, my ability in machine learning will be developed in an advanced level by attending your modules Computational intelligence and machine learning, Data mining techniques and Deep learning. With regard to my mathematical proficiency, the modules Statistical inference for data science, Advanced statistical modelling and Topics in applied discrete mathematics will deepen my understanding of modelling construction and cultivate my core creativity. In brief, I have the confidence that the solid theoretical and technical foundation in data science I will obtain at The XX will lay a contributory milestone for me to focus on data analysis of Internet companies in the future. In the long run, the postgraduate study at your prestigious university will be of great importance to my promotion as a manager who integrates business and technical capabilities and lead large-scale international business project teams. Thereupon, I would appreciate it if you could take my application into kind consideration.

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