With the strong rise of the Internet industry, renowned companies at home and abroad are increasingly valuing talent in data statistics, mining, and processing. Leading universities are also adding data science disciplines one after another.
Driven by this trend, Yale University has added a specialized M.S. in Statistics and Data Science this year.
Program Introduction
Previously, Yale only offered a standalone M.A. in Statistics. Compared to that program, the new M.S. not only provides teaching in traditional statistics areas such as probability theory, stochastic processes, asymptotics, and information theory, but also enables students to delve into popular fields like machine learning, data analysis, statistical computing, and pattern recognition, with more hands-on opportunities.
According to the course description on Yale's official website, students must complete 12 courses within one and a half to two years. Even if admitted, to smoothly attend classes, students must satisfy the five course-related requirements shown in the figure below. Only after meeting these requirements will the program director approve students' course registration at the beginning of each semester.
.png?x-oss-process=style/opt-webp)
Standardized Test Application Requirements
To apply for this rigorous program, students need to submit GRE scores, and international students must submit TOEFL scores (unless they have at least three years of undergraduate study experience in an English-speaking country). Yale does not provide score cutoffs, but its official website states that lower standardized test scores will reduce the probability of admission.
Admissions Data
Since the M.S. program only began enrolling this fall, there is no acceptance rate data yet. However, the website provides M.A. program data for reference: in 2017, the M.A. program had 350 applicants, with 35 receiving offers (an acceptance rate of about 10%).
Recommended Data Science Master's Programs
Besides Yale's new program, we have compiled other renowned data science programs that excel in curriculum, faculty, and research development, ranking among the best.
Note: Listed in no particular order
Harvard University
Master of Science in Data Science
The program is under Harvard's School of Engineering and Applied Sciences, aiming to cultivate top talent in data modeling, machine learning, big data analysis management, and optimization. Students must complete 12 courses in at least three semesters, with four core courses as follows:
.png?x-oss-process=style/opt-webp)
Partial Application Requirements
All applicants must submit GRE scores; international students must submit TOEFL scores (IELTS not accepted).
Prerequisites include calculus, linear algebra, probability, and statistics. Applicants must be proficient in at least one programming language (e.g., Python or R) and have basic knowledge of computer science.
Admissions Data
In 2018, the program had 1,400 applicants and admitted 70 (an acceptance rate of about 5%).
.png?x-oss-process=style/opt-webp)
New York University
Master of Data Science
NYU's Master of Data Science requires students to complete 36 credits over two years. Founded in 2013 by the renowned computer scientist and convolutional neural network pioneer Professor Yann LeCun, the program aims to lead students to apply their data knowledge to real-world fields in industry, academia, and government, uniting theory and practice.
Partial Application Requirements
All applicants must submit GRE scores; international students must submit TOEFL (100+) or IELTS scores. The average GRE scores provided by the school are as follows:
.png?x-oss-process=style/opt-webp)
Prerequisites include calculus, linear algebra, statistics, and computer science. Applicants should ideally be proficient in programming languages (e.g., Python or R) and have an understanding of computationally-oriented economics.
Stanford University
Master of Science in Data Science
Stanford's M.S. in Data Science is jointly offered by the Department of Statistics and the Institute for Computational and Mathematical Engineering, thus placing great emphasis on mathematics and computer programming. The program also offers various interdisciplinary electives, such as computer graphics and human neuroimaging methods. The diverse course options also provide a solid foundation for pursuing a Ph.D.
Situated in the heart of Silicon Valley, Stanford provides students with additional computing resources, such as access to Amazon's EC2 platform for large-scale computing. Naturally, this prime location also enables the program to supply numerous data science talents to the many tech companies in Silicon Valley.
Standardized Test Application Requirements
All applicants must submit GRE scores; international students must submit TOEFL scores (IELTS not accepted).
Duke University
Master in Interdisciplinary Data Science
This two-year program is dedicated to cultivating a new generation of leaders who can use computational strategies to inspire innovation and insight and are adept at quantitative thinking. It aims to train students to become data scientists capable of contributing to any field, promoting better use of data through interdisciplinary training and team-based scientific experimentation. The program is small, admitting 25-35 students annually.
Partial Application Requirements
All applicants must submit GRE scores; international students must submit TOEFL (90+) or IELTS (7+) scores.
Prerequisites include calculus, linear algebra, and statistics, but a background in mathematics or computer science is not mandatory.
Get more "Top U.S. Data Science Master's Programs"
Please follow the "CheersYou（清柚教育）（清柚留学）" WeChat Official Account
Reply "Data Science" in the backend
to receive them for free
.png?x-oss-process=style/opt-webp)
