With the emergence of the significant data era, new concepts such as artificial intelligence and machine learning are dazzling. "significant data" has become a buzzword everywhere. Thomas J. Sargent, the winner of the 2011 Nobel Prize in Economics, pointed out that artificial intelligence and significant data analysis relies on statistics. I agree. As a practical science, statistics will step forward to meet social needs. Therefore, new statistical theories, methods, and techniques will appear, keeping pace with significant data. Becoming a data analyst and contributing to the development of statistics will be my life-long career and personal pursuit.

Being a student majoring in statistics, I devoted myself to studying pure mathematics courses in my freshman year to develop a thorough understanding of essential mathematical concepts and theorems. Since my sophomore year, I started taking statistics courses, such as Multivariate Statistics, Regression Analysis, Stochastic Process, etc. Meanwhile, I grasped programming abilities in language R, Python, and C in lab courses and online courses. With my deeper digging in statistics, I enjoyed learning data more.

Moreover, the summer session at the University of Berkeley was enlightening for me. At Berkeley, I chose a Biostatistics course that enabled me to practice statistics methods in public health. I applied conditional probability to diagnostic testing, calculating the Sensitivity and Specificity of breast cancer. In my assignment, I also conducted a parametric hypothesis test and statistical inference in HPV status and age group. This summer experience strengthened my determination to study a Master of Statistical Practice in the United States. I see it as not only an amazing academic journey for my future career aspirations but also a precious culture exploration.

In addition to studying on campus, I actively took part in practices to apply the knowledge and skills of analyzing data. When I interned at CITIC Securities, I initiated research in Beijing Bed and Breakfast (BNB) industry. To explore the influence factors of price, I constructed an explanation model with R. Utilizing the ANOVA method on price and each factor, I gained 15 most relevant factors as independent variables. I practiced the Multiple Linear Regression model at first. However, the fitness result was unsatisfactory, visualized with the ggplot2 package. Thus, I carried out regression diagnostics. Using the Variance Inflation Factor (VIF) Method, I found a multi-collinearity of factors. To eliminate the multi-collinearity and improve my model, I turned to Principle Component Regression (PCR) and Ridge Regression methods, which explained the price well. Consequently, I found that distance to the business center and area of room influenced the price most.

In 2019 winter, I did research in the Key Laboratory of significant Data Mining and Knowledge Management at the Chinese Academy of Sciences. In the project of AI for face disease identification, we used Convolutional Network knowledge on biomedical image segmentation. To learn fast and contribute more to our team, I studied an online course called Machine Learning taught by Andrew Ng. Having learned Discriminant Analysis from a statistics lecture helped me understand classification problems in machine learning. One thing I'd accomplished was assigning two class labels to different pixels of images to differentiate the lesion areas from the background. These possessed images, as samples, were used to train the U-net architecture with Python.

Master of Science in Statistical Practice at [University Name] integrates data theory and method with practice and connects to different industries. It attracts me the most. Working in a variety of fields provides us a clearer goal in the study. Now, I am interning as a business data analyst at a unicorn company that aims to provide customized significant data solutions. To support the social media marketing of the L'Oréal team, I carried out social listening and analyzed the public sentiment on Weibo and WeChat, two dominant social media in China. Besides, through descriptive statistical analysis results, I compared the daily buzz of products and competitive products. This internship triggers my interest in the intersection of marketing and online media.

In the first two years after completing the MS in Statistical Practice program at [University Name], I plan to stay in this company as a business data analyst. I will play my strength in analyzing data and accumulate my working experience. Afterward, I aspire to work as a data analyst in Tencent, an internet company. In the long term, I aspire to establish my data studio in my hometown and lead a team to help local start-ups with strategic data solutions.

[University Name] is an open and cooperative place to inspire outstanding ones. I explored the Master of Science in Statistical Practice Program and found it suitable for me. Firstly, I like the practical-oriented curriculum covering theory, methods, and computing. Statistical Modeling and Machine Learning courses are valuable for work. Secondly, the semester-long practicum and summer internship in a variety of fields like online media and marketing can enable me to follow my path to an excellent data analyst. Thirdly, I expect to meet and greet the talents in mathematics and statistics through the MSSP Consulting and Statistics@Work. I am looking forward to working with people from different cultures and professions.

This is why I am applying for this program. I am ready.