I'm currently a senior at the University of Michigan - Ann Arbor (ranked 17th in the US News World Rankings).
My majors are Data Science and Statistics, and I have a minor in Business. As a graduating senior, I plan to pursue a graduate degree after graduation. I've already been admitted to universities such as the University of Pennsylvania, Cornell University, Brown University, and the USC Marshall School of Business.
I initially only had Statistics as my major. I chose it because I prefer applied mathematics over theory, and statistics is an excellent discipline that applies mathematics to various industries. When I first entered college, I didn't know what I wanted to do in the future, so I selected a major that was relatively versatile.
Gradually, during my coursework and internships, I discovered a strong interest in data. The process of organizing chaotic data into actionable spreadsheets and extracting valuable information from a mass of numbers fascinated me and gave me a sense of purpose. Especially during my internship in the market research department of a company during my freshman summer vacation, watching the analysts identify key points from the data I organized and then provide their analysis and suggestions gave me a new understanding of the value that data can bring. That's when I decided to add Data Science as my second major and applied for a minor in business school because I hope that in the future, I can also extract business value from vast amounts of data like those analysts.
Later, I gradually realized that Data Science wasn't exactly what I had envisioned. Firstly, I had to take quite a number of CS courses because data scientists need to be highly technical and possess a lot of hardcore skills.
Regarding coding, I have a love-hate relationship with it. I love it because this trendy discipline has opened up a whole new world for me. However, I hate it when I get stuck because I often don't know how to communicate my needs to the computer... Fortunately, I managed to get through it all and learned a great deal in the process. At the very least, I now have a relatively comprehensive understanding of CS. In the future, if I need to use other programming languages in my work or study, I should be able to pick them up quickly.
Speaking of Data Science, the most popular aspect of this discipline at the moment is probably machine learning (from Baidu Baike: It specifically studies how a computer can simulate or replicate human learning behavior to acquire new knowledge or skills and reorganize existing knowledge structures to continuously improve its performance). In our classes, we mainly focused on basic modeling, building various models based on existing data in the hope of making predictions about the future. However, I didn't have the opportunity to explore more advanced models during my undergraduate studies. This is one of the main reasons why I decided to pursue a graduate degree, aiming to delve deeper into this field.
I actually didn't decide to pursue a graduate degree until the end of August. Before that, I had always planned to start working after graduation. But when I realized that my current knowledge was far from sufficient, I quickly changed my plan and started preparing for the GRE.
Looking back on this still-unfinished application season, I consider myself fortunate. Everything has gone relatively smoothly, and the professors I've encountered have all been very nice. Of course, this is also closely related to my four years of hard work at college. At least I don't have to worry about my GPA.
If I had to say which part of the application is the most important, it's really hard to tell. But having experienced the undergraduate application process as well, I feel that the graduate application process has less of an element of mystery. As long as your background and capabilities meet the requirements of the program, you generally have a good chance of getting in. Unlike undergraduate applications, which seem to rely more on the so-called "fit" with the school (this is what I mean by mystery. Can the admissions officers really tell from my application materials whether I'm a good fit for the school?)
Undoubtedly, the most challenging part of the application season for me was the interviews. While most people are excited when they receive an interview invitation, I was mostly nervous and lacked confidence. However, through continuous practice over the past few months, I could feel myself improving and becoming more confident. So, if I could relive the application season or even my entire undergraduate experience, I definitely wouldn't avoid interviews because I've realized that I'm not as hopeless as I thought.
For now, my plan for the future is to join the company where I interned during my junior summer vacation in Shanghai after graduating from graduate school. The reason is that during the interview for the return offer with the partner, our boss shared his vision for the future development of the department. It was all about how to make the best use of the most cutting-edge data analysis tools and methods in the big data era to create value for the company and its customers. This is precisely what a data scientist does. Therefore, I'm extremely looking forward to returning to inject new energy and bring more possibilities to our young team after completing my studies.
Here are my courses, shared with you all.
Statistics:
Prerequisites: Calculus, Linear algebra, introductory programming (C++), introductory statistics
Program Core Requirement: Introduction to Probability and Statistics, Theoretical Statistics, Applied Regression Analysis, Statistical Programming (R)Other higher-level courses: Bayesian Analysis, Data Mining, etc.Capstone: Statistical writing
Data Science:
Prerequisites: Calculus, Linear algebra, Introductory programming(C++)
Program Core Requirements: Discrete Mathematics, Programming and Elementary Data Structures, Data Structures and Algorithms, Introduction to Probability and Statistics, Applied Regression Analysis
Additional required courses: Machine learning, Data management, etc.
The editor also wants to share some great news!!
One day after writing this article, the author received a hot-off-the-press offer from MIT!
We also wish her all the best in achieving her goals on an even higher platform.
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