New york University and Columbia University are both located in new york, so I will briefly introduce and compare the DS projects of these two universities for students, hoping to bring help and reference to the project.
Before introducing the two projects separately, first of all, let us introduce the professional direction of data science, so that students can have an overall understanding. Although American universities only started to offer a large number of DS courses in recent years, the concept of data science actually existed for a long time. DS is a comprehensive subject, which combines statistics, algorithm, distributed system, machine learning, database, NLP and visualization. The purpose is to train professionals who can meet the current needs of data analysis and processing, so curriculum design is often more practical, precisely because most ds programs have no plans to train doctors. The main purpose of this project is to train professionals with the abilities of statistics, finance and computer science. NYU and Columbia's DS project are the same. Below I will introduce the DS programs of the two universities to the students respectively.
Introduction to the DS project of Columbia University First, I would like to introduce the DS project of Columbia University to you. The duration of the DS program at Columbia University is one and a half years, one * * *, which requires 30 credits. Students don't need to write papers before graduation. These basic requirements are easy to find in official website, Columbia, so I won't repeat them here. Students who are interested can go to official website to find relevant information by themselves. The content of the DS project of Columbia University is mainly composed of CS and statistics. Some students in CS mainly need to learn about algorithms and parallel computing systems, while statistics mainly learn about machine learning and statistical inference. Among the 30-credit courses, there are 6 compulsory courses. In order to let the students know more about the content of project learning, my classmates and I briefly introduce several courses in the compulsory courses. The first course is the algorithm of data science. The main content of this course is algorithm, and python is used to write code in this course. This course contains a lot of content, but the pace of class is very slow, mainly because the DS program of Columbia University does not require strict professional background when enrolling students, and many students have poor algorithm foundation. Slowing down the course can enable all students to complete the project. Therefore, this course is a good choice for those students with weak foundation in algorithm and programming.
The next course is statistical inference and modeling. The content of this course is much higher. The course is mainly divided into two parts, generally focusing on mathematical statistics, hypothesis testing and other contents, and the rest mainly focusing on the introduction knowledge of machine learning. The homework of this course is reasonable, and students are required to write basic machine learning algorithms in R, and this course requires students to have a certain foundation in probability theory and linear algebra. Students who have taken relevant professional courses in domestic mathematics departments or engineering majors can basically meet the needs of learning. The content of machine learning is also very basic, which mainly paves the way for students to follow up the course of machine learning and is also an introductory course. The content design of the compulsory course Probability Theory is also very basic, because many domestic students will learn a lot of knowledge related to Probability Theory during their undergraduate years, and many students will choose to give up this course. The compulsory courses are finished, and the following are optional courses. In my opinion, this is also the advantage of the DS program at Columbia University, because there are no restrictions on students' elective courses. You can choose all the courses of Columbia University according to your own needs, so that students can make full use of Columbia University's educational resources to meet their future development needs. However, it should be noted that the number of applicants for many popular courses in Columbia University is very large, and the waitlist list of some courses is surprisingly long, so everyone must make plans and communicate with advisor before choosing courses.
Generally speaking, the core courses in the DS project of Columbia University are not very difficult, and the amount of study is moderate. This project is suitable for students with weak foundation but good math background. But students should not think that this project is water, because the elective courses in the project are very free, and students can choose some core courses to complete the study of professional knowledge, so this project can meet the learning needs of different students. Project employment completed the project curriculum design. Next, I will introduce the employment situation of the project to you, which I believe is also the most concerned part of many students. Columbia University has CPT, but students can only use it after 9 months of enrollment. Before that, students can only do some work on campus. But students don't have to worry, because there is no shortage of internship and employment opportunities in new york. In addition, schools and colleges will provide students with a lot of resources. Students can see a lot of recruitment information on the school website, and advisor will often push recruitment-related emails to students, which is still very satisfactory. In my opinion, the most important task for students in peacetime is to learn the core professional courses in the project and improve their abilities. Because universities and programs only provide a platform for us to screen resumes when applying for jobs. In order to finally find the job you want, your own strength is the key. When our own strength is excellent, finding a job will naturally come. Most of the students in the project are data scientists in a small startup company, but some students will work in the investment banking field, but that kind of work requires a lot of machine learning, so I suggest that students must lay a good foundation for machine learning before employment, which can greatly enhance our competitiveness in the employment process.
Project Introduction After introducing the DS project of Columbia University, let me introduce the DS project of NYU to the students. This project belongs to courant, which ranks first in applied mathematics, so the teachers and academic resources of the project are very strong. In addition, due to the influence of courant, the reputation and recognition of the project are still very good. The program usually takes two years to graduate, and students need to complete 36 credits, including 6 compulsory courses and 6 elective courses. NYU's DS project curriculum design is very mature. It is not simply to let students learn knowledge in statistics, cs and other fields, but to train a data scientist step by step, so that students can obviously feel their growth in their usual study. More importantly, after studying here, we will have a deeper understanding of data science, which is very helpful for students' development after graduation. Course-related In this project, students need to study intro ds, stats, machine learning, big data and other courses in the first year. These are some basic courses, which are not very difficult, and can help those students who apply for majors to complete the introduction. Those students with relevant professional backgrounds will be very relaxed when studying these courses. Although the contents of stats and machine learning are very basic, the knowledge learned in the course is more theoretical, and students will still gain a lot in the process of learning. In the second year, students will also take the course of statistical inference, and then complete the final project. Stats inference is a very difficult course, and many students will have a little difficulty in learning it, but everyone can also gain a lot. In my opinion, the required courses of NYU DS project are of high quality, such as machine learning, which is recognized as a sacred lesson. Although big data is a bit pitted, it is harmless. Next is the elective part. NYU's elective courses are almost the same as Columbia University in freedom. Students can also choose all courses, such as stern and CS. Each student can design courses according to his own needs, which is very flexible. Moreover, compared with Columbia University, NYU is not so competitive in the choice of elective courses, and students can basically choose their favorite courses, which is also very good.
Here, I would like to introduce some elective courses to you. Deep learning and NLP are very good choices. The content of these courses is very hard-core, and 80% of the content requires students to learn by themselves, so it is very challenging, but it is really interesting to learn. In addition, there are some elective courses such as advanced programming and text mining, which are somewhat watery, but students can also choose according to their own needs. In addition, although NYU's DS program normally graduates in two years, students can choose more courses each semester and graduate early, but the usual academic pressure will be great, so everyone must be prepared before doing so. In addition, students of NYU DS program can apply for doctoral studies, and students with this idea will have the opportunity to continue their doctoral studies as long as they communicate with professors more. Because the course design of the project contains a lot of content, if you find yourself interested in a certain research field in the usual study process, it is better to apply for doctoral studies, because the requirements of enterprises for DS-related talents are getting stricter and stricter, and doctoral studies will be more competitive in job hunting. In my opinion, this is also one of the advantages of NYU project. Finally, let's talk about the employment situation of NYU DS project. In my opinion, NYU is also diligent in employment. The college basically invites enterprises to make information conferences every week, and there are often job fairs in the department. Although the scale of enterprises coming here is uneven, at least it can provide more employment opportunities for students. In addition, the employment center will also provide many job-seeking services for students in peacetime. Teachers will help students revise their resumes and have mock interviews, which is very helpful for job hunting. In addition, NYU's alumni resources are also very rich, and students can use their contacts to find many good job opportunities.
The above is my comparison of the two projects. Generally speaking, the two projects are very good, but relatively speaking, Columbia University is better than NYU in statistics and cs, especially in machine learning and NLP, and there are many great people in Columbia University, and the alumni resources and learning atmosphere are better. In addition, the recognition of Columbia University is much stronger in China, but in the United States, it is generally believed that new york University is as excellent as Columbia University, so the impact on students who want to work in the United States is not great. However, in my opinion, there is never the best project, only the one that suits me best, so I just share it for your reference. Students need to analyze and judge according to their actual situation and development needs, and find out the most suitable project for them. Finally, I wish all students can find a suitable project, successfully complete the application and gain a bright future.