录取学校：Johns Hopkins University MS in Computer Science
In episode of the television show, The Big Bang Theory, the four scientists turned off their lamps at home by controlling satellite signals. Someone asked why they did not just walk to the lamp and turn it off. Their answer was, “Because we can”. I think this is cool. I really like what the technology brings for us, and I hope I can become such a cool person.
The story highlight the changes technology can bring, especially computer technology has made it possible to achieve powerful features. “Simplicity is Good” is my motto, I really like the value computer technology bring to us by concise tools of programs and mathematics. In a umber of areas of computer science, Data Science is one area that I consider to be in line with this feature. We can harvest high value by mining plain and primitive data, the more data there are the broader the application field of data science will be. This motivated me explore the field of data mining further.
My interest in data technologies can also be traced to my internship experience in a communications company. My job involved conducting Big Data analysis on the consumer terminal and handling the optimization of big data visualization products. The product is designed to mine the data necessary for users from the TB-level data and create chart-based visual presentation. Among my tasks is to use Bitmap to optimize the process of performing a simple logical operation on a particular type of data. The bit-based operation is a common method for improving the calculation speed. As I completed my tasks, I pondered on some questions on data processing, such as how more complex operations can be optimized and how more complex data can be analyzed when decision-making is involved. I hope to find the answer in graduate study.
As a future graduate student, I look forward to improving my knowledge of statistics and anticipate gaining further skills understanding of algorithms, particularly the algorithms of machine learning, such has neural networks and reinforcement learning. As I progress in my master’s studies, I will gradually deepen my study of this field, from theory to algorithms to the realization of algorithms. In addition, I will upgrade my mastery of data mining and data modeling techniques through considerable practice and will ensure I review the utility of tools such as data mining tools and Matlab, and languages such as Python, C++ and Scala.
Data management also plays a significant role in data science. I will also seek to broaden my knowledge base in this area, and accumulate knowledge on the construction and architecture of distributed systems and understand the principles and approaches in distributed system design. In recent years, I have attempted to store data using relational and non-relational databases, hoping to learn further about the underlying principles and potential optimizations of different types of databases and different engine storage. Through further advanced studies, I hope to learn independent data processing or part of the process, identify performance bottlenecks, and solve the problem of performance optimization. I will also study about data visualization because the ultimate goal of data mining and analysis is to support decision-making. In the process of data presentation, the interaction of users with data management system and visual operation process is also worth discussing.
These goals explain my reasons for choosing to apply your Master's degree in Computer Science. The researchers conducted by the Machine Learning & Data Intensive Computing Group, Systems Group, Institute for Data Intensive Engineering and the Science and Data Management Systems Lab fit my interests very well. The research-based master's degree and the long-standing research capabilities of Johns Hopkins University can help me develop into a great data scientist and achieve my goals.
And I am qualified for the study of data science. As a software engineering major, I possess a strong background in computer which gives me an advantage in terms of familiarity with software architecture, statistical methods of software engineering, computer organization and structure. I have mastered efficient software construction methods that can be applied to defensive programming that deal with possible coding errors and exceptions in practical applications and enable high-quality software construction. I also possess a mastery of classical discrete and continuous random variables distribution function, mathematical expectations, variances, and other commonly used calculation methods of random variable digital features, variance analysis, and regression analysis methods.
Likewise, I am highly familiar with algorithms, which I gained through my involvement in an algorithmic design and code development project for two semesters. The project was later rated a provincial innovation project. Through the study of stock strategy, I summed up the rules of quantitative trading of stocks, set up stock quantitative trading platform and was responsible for the realization of stock strategy and back-testing of several algorithms. The Stock Strategy Analysis System is a web-based application system. The backend uses python programming language and Django framework, while the front end uses BootStrap framework and Ajax technology. My responsibilities included the design and implementation of the morphology strategy module. The module includes three strategies, a stock strategy named 3K5K, Glanpearl averages strategy, and gap strategy. My exploration of the stock strategy yielded two achievements. First, the 3K5K stock strategy can be started from the K-line mode, and the traditional experience-based stock manager's winning strategy is machine-oriented, which can be improved through more intuitive and detailed strategy. Second, combined with the direction of data mining, the stock strategy can systematically analyze the technical indicators of stocks and use relatively mature data analysis algorithms to draw valuable conclusions.
Working on a three-month software development project in the laboratory of Professor Zhang has given me considerable skills in mining techniques. The project team is dedicated to software process mining, which seeks to determine the typical behavior model of software development based on existing data mining methods. Comparing the different behavior patterns with the development results, the software development behavior model is set up and used to guide the enterprise software development process. I compiled the 2016 DevOps China Practice Report by analyzing the questionnaire from DevOps practitioners in China, reviewed several graduate-level papers, and published outstanding papers on data mining and software process analysis. The mining methods accumulated through this experience will be very helpful in my study of data mining and data analysis.
Another advantage I bring with me is my experience in product development in large enterprises. For a period of three months, I worked in Nanjing Huawei for the development of HA big data visualization products. As a Java engineer, I completed significant technical work that provided me with a thorough understanding and advanced skills in product development.
In addition to becoming an engineer, I also hope to become a data science artist. Thus, I will strive to pursue cutting-edge knowledge in data science and work in an enterprise that fosters respect for technology and encourages innovation. My plan also includes pursuing PhD studies after accumulating practical experience because I need the ability to serve in the colleges through the accumulation of learning, work and research. Graduate education in Johns Hopkins University will greatly help me achieve these goals.