Studying at Carnegie Mellon University is interesting and challenging. I have worked with guys who have brilliant ideas and strong execution skills. During working and playing with these guys, I have imbibed the spirit of collaboration forstered among the students. CMU is a place where there are many guys who share the passion for technology and innovation as I do.
All courses at CMU are interesting. Based on my interest, I concentrate myself on the software development combined with data analysis. In the course Cloud Computing, I have learned the Hadoop framework and related tools such as MapReduce and HBase. I really like this framework because it is simple and elegant for computation-consuming tasks. Moreover, I have further understanding of computer after taking the essential course 15-213 Introduction to Computer Systems. In this course, we started from binary code to the network programming. I did some cool things like a proxy. In a word, technical courses at CMU are very interesting. I began to think like an engineer: make things work with limited resource.
In the first semester @ CMU, I have finished 11 academic projects concerning web, mobile devices, computer systems and data analysis. I selected 3 projects to share with you. If you are interested in the technical detail, we can discuss in private.
This web application provides search prediction based on the phrases of 6,000 books in the Gutenberg Project. Just like Google Instant, it provides word suggestions based on the conditional probability calculated from history data. This application can provide suggestions for phrases with length less than six words.
This web service provides customized four types queries to the tweet dataset provided by Professor Sakr on the course Cloud Computing. It is a real distributed system which provides query services.
This project is the final project for the course Data Warehousing. Our team used the data related to online advertisement given by Professor Li. Our team proposed six business questions related to the data. Then, we designed and implemented a data warehouse on Microsoft SQL Server. Finally, we programmed SQL scripts and an OLAP cube to answer our business questions.