Bachelor of Chemistry, Fudan University.
Graduate Students:
Master of Data Science, University of Pennsylvania.
toefl:
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My undergraduate degree was in chemistry, which is far from data science and computer science.
The daily study of the undergraduate is occupied by various chemical theory laboratory classes, and many times they are either doing experiments or writing long experimental reports.
But in the process of learning, I found that I was not as good at chemical experiments as other friends (yes, I am a bit of a handicap party).
I also worked on reactions and columns in the lab, but I gave up on chemistry for some other reason.
Therefore, for various reasons, I decided to study abroad for a master's degree in another major at the beginning of my junior year.
But at that time, it was not particularly certain what major to change to (finance, financial engineering, computer science, etc. were all popular projects applied for at that time), soI had a variety of experimental experiences, such as an internship at a bank during the summer vacation, a Minor in Data Science, and various lectures on studying abroad applications. But it turns out that interest really matters!
I would like to thank my alma mater for providing me with the resources and platform to systematically study another major, which made me deeply attracted to the field of data science.
Perhaps because I have been studying science and engineering, I am very interested in various models and algorithms of machine learning, and I also enjoy the process of coding
Therefore, I decided to apply for the program in the field of Data Science and Computer Science.
The preparation of the application is mainly divided into two aspects, one is to improve the background before the application season, and the other is to prepare the materials required for the application, school selection, etc.
I know that if I want to apply for a data science computer science program with a non-academic background, I have to put in more effort than others.
So my preparation before the application season is divided into three main parts:
1) GPA2) Supplement the expertise in DS CS and accumulate project experience.
Language scores. The first is GPA, which has to be said in the application for master's programsGPA is really important
When applying for a change of major, the study of the original major cannot be slackened in the slightest.
This is because admissions officers often consider the following two points for applicants to master's programs:
1) Whether the applicant has the ability to successfully graduate from the program;
2) Whether the applicant can successfully find a job after graduation to improve the employment rate of the project.
A high GPA will make admissions officers think that the applicant has the ability to successfully complete the master's program.
And for an applied major such as Data Science, if you have a certain understanding and interest in the application of Data Science in undergraduate majors, or because this application has aroused your interest in Data Science and prompted you to study this major in depth, then the motivation to apply will be more convincing.
In other words, if you don't learn well in your major, professors will be very skeptical about your ability to learn well in other majors.
Supplementing DS CS expertise is one of the most special aspects of the preparation for a change of major.
Of course, everyone has a different way to learn this part of the course.
The most straightforward thing is to be like meTake DS CS related courses directly at our schoolFor example, algorithm classes, machine learning classes, and various math classes, etc.
Or you can take advantage of the opportunity to go abroad to take these courses at a school abroadThis is a more recommended way, because in recent years, the teachers who have recruited students have paid more and more attention to the experience of studying abroad, if they have the experience of studying abroad and have achieved a good grade, then this will be a big plus for the application.
In addition, you can also learn some online courses outside of class as a small supplement to your knowledge.
In addition, research projects or internet internships are also very helpful.
From the students I have met in the same program, most of them have done impressive research projects, had good internships, or both.
The experience of these experiences, the things learned, the perceptions will become a valuable part of the text, or the essence of the whole story.
The practical content of these internships can quickly reflect your professional and academic abilities in the CV.
For research projects, it would be a big plus if one or two articles could be published, and it didn't matter much if there were no articles.
For internships, I personally recommend going to a foreign company for an internshipThe reason is that the professors in the admissions team of American colleges and universities must be more familiar with and understand American companies.
The third point is to get an ideal language score.
This is a hurdle that every student who applies to study abroad has to overcome.
Since I had a vague idea of studying abroad in my freshman year, and the average English of undergraduate students is very high.
So I have been learning English without interruption (after all, it is more difficult to get an A in some English classes than in a language test.)
However, because my own English foundation is not bad (I basically memorized the vocabulary of level 4 and 6 in high school), I have always been prepared for a long stream.
In the months before the exam, I spent two hours a day brushing up on the questions and memorizing words, and the Buddhists did a good job in the language score.
The most important thing is the time of application season.
The first is the collection of school project information, and the list of school selection is listed in a hierarchical manner: sprint ideal minimum project.
Of course, there are many factors to consider when choosing a project, such as:
1) The length of the project, whether it is conducive to finding a local summer internship (ignore it if you are determined to find a job in China);
2) The overall professional strength of the school and the difficulty of its application, which requires a keen judgment by gathering various information from the school** forum.
3) The geographical location of the school, whether it is urban or rural, whether it is conducive to finding internships, the price level, etc.
4) Whether you like the courses offered is more important for those who apply for data science, because different schools have different data science focuses, some are more mathematics and statistics, some are more computer-oriented, and some are more business-oriented.
5) Are you friendly to changing majors, or do you prefer students with a professional background?
Different projects have different preferences for this point, and you need to find out in advance.
In addition, as the number of applicants for DS CS increases, the competition becomes more and more fierce, so it is more prudent to choose more guaranteed schools.
When it comes to preparing application materials, it's crucial to have an attractive essay and a professional CV and strong letters of recommendation, because admissions teachers will basically get to know the applicant comprehensively from these three materials.
If some hard indicators (GPA, language scores) can determine whether you will be eliminated or not, then these three materials can determine whether you can be admitted or not.
In terms of preparing documents, it is important not to use only one document to apply for all the programs, which means that you need to make corresponding changes according to the characteristics of each school project, and some places even need to be overhauled.
For students who are mixed with CS DS, the focus of essay writing in these two majors should be different.
The essay will be as if telling a story about oneself, and the applicant needs to tell the story completely, logically, and in a way that touches people's hearts. And if the essay can reflect a person's story, feelings and ideals, then CV more reflects your professional ability and academic background.
Therefore, a CV needs to be very professional from format to content.
The teacher who provides the recommendation letter can be an industry leader encountered during the internship, a teacher who has taken classes in school, or a tutor of the research group, etcThe teacher who writes the letter of recommendation must have a very basic understanding and intersection of the applicant.
When preparing these three materials, I need to ask myself all the time: why should they admit me, my greatest strength is **, and how can I stand out from the applicants.
I have to say that the application is a protracted battle, which is not only a test of ability, but also a test of mentality.
Take myself as an example, in my junior year, I studied the professional courses of the Department of Chemistry during the day, took the computer and math classes of minor at night, the class schedule was almost full, and there were many experimental reports to write on the weekends.
The most devastating thing is the period of the final quarter, which lasts for a week to take the minor things, and then the general education exams and the exams of the major, sometimes one course after another, and sometimes I have stayed up all night before some exams, and I need to ensure that most of the grades can get A.
Of course, after the cruel junior year, my ability to resist pressure and time management has made a qualitative leap, which has also made me have a calm mind in my graduate life.
In the end, I chose UPENN's DS major, which was an ideal offer for me.
So let me introduce you to some data science majors
Data science is a discipline with a wide range of applications in data centers, from literature and art, to basic science, to policy and management, to solving engineering problems.
Therefore, data science is a discipline that is friendly to changing majors.
Take chemistry as an exampleMany of the chemistry directions I came into contact with in my undergraduate are closely related to data science, such as cheminformatics, computational chemistry, etc., and I have also done some projects in the field of chemistry that require machine learning to solve problems.
Of course, data science requires a lot of mathematical and statistical knowledge and strong coding skills.
Nowadays, there are many job requirements in data science in the Internet industry, such as algorithm engineers, data mining engineers, data development engineers, and so on.
With the booming industry and the increasing number of schools offering data science programs, this is also great news for applicants