TOFEL:100分
2 Years
学年学费
$2,212.00/1 Credit
奖学政策
提供奖学金
暂无
专业排名
暂无
招生人数
暂无
Successful applicants to the MSDS come from many different undergraduate backgrounds, including degrees in Statistics, Computer Science, Mathematics, Engineering, Economics, Business, Biology, Physics and Psychology. In the 2017 intake cycle, the average GPA was 3.69. Our students’ transcripts usually include As and Bs (only), and we expect stronger grades in more relevant subject matter (see below) from those coming from less selective institutions. Regardless of degree, we require specific and substantial knowledge of certain mathematical competencies, and some training in programming and basic computer science.
To be considered for the program, you will be required to have completed the following (or equivalents):
Calculus I: limits, derivatives, series, integrals, etc.
Linear Algebra
Intro to Computer Science (or an equivalent “CS-101” programming course): We have no set requirements as regards specific languages, but we generally expect serious academic and/or professional experience with Python and R at a minimum.
One of Calculus II, Probability, Statistics, or an advanced physics, engineering, or econometrics course with heavy mathematical content
Preference is given to applicants with prior exposure to machine learning, computational statistics, data mining, large-scale scientific computing, operations research (either in an academic or professional context), as well as to applicants with significantly more mathematical and/or computer science training than the minimum requirements listed above.
GRE scores
TOEFL or IELTS; however, TOEFL is preferred (Required for all applicants whose native language is not English and who have not received a university degree in an English-speaking country)
Official college transcripts
Three letters of recommendation (we prefer all letters on letterhead)
Statement of Academic Purpose
For more information, visit http://gsas.nyu.edu/admissions/gsas-application-resource-center/2017-programs–requirements–and-deadlines/data-science.html.
Below, we provide more details about our expectations.
Educational Prerequisites
Successful applicants to the MSDS come from many different undergraduate backgrounds, including degrees in Statistics, Computer Science, Mathematics, Engineering, Economics, Business, Biology, Physics and Psychology. In the 2017 intake cycle, the average GPA was 3.69. Our students’ transcripts usually include As and Bs (only), and we expect stronger grades in more relevant subject matter (see below) from those coming from less selective institutions. Regardless of degree, we require specific and substantial knowledge of certain mathematical competencies, and some training in programming and basic computer science.
To be considered for the program, you will be required to have completed the following (or equivalents):
Calculus I: limits, derivatives, series, integrals, etc.
Linear Algebra
Intro to Computer Science (or an equivalent “CS-101” programming course): We have no set requirements as regards specific languages, but we generally expect serious academic and/or professional experience with Python and R at a minimum.
One of Calculus II, Probability, Statistics, or an advanced physics, engineering, or econometrics course with heavy mathematical content
Preference is given to applicants with prior exposure to machine learning, computational statistics, data mining, large-scale scientific computing, operations research (either in an academic or professional context), as well as to applicants with significantly more mathematical and/or computer science training than the minimum requirements listed above.
Work Experience
Many of our students join us directly from undergraduate, but we also very much welcome evidence of relevant work experience—and clear employment goals once the MSDS is completed—in data science. Past experience and career aspiration goals can be related to commercial industry, government, academia or some other sector.
Standardized Tests
We require that students submit standardized tests scores for the GRE. There are no exceptions: we do not accept “out of date” scores; nor do we accept scores of other, similar tests; nor do we allow waivers (regardless of previous educational attainment or circumstance).
We require that students submit standardized tests scores for the GRE. There are no exceptions: we do not accept “out of date” scores; nor do we accept scores of other, similar tests; nor do we allow waivers (regardless of previous educational attainment or circumstance).
We wish to emphasize that we have no set minimums for the GREs, and we consider the totality of an application when making a decision about admission. Nonetheless, to the extent that it is helpful to give applicants a sense of the “ball-park”, what follows are the averages for the current cohort of MSDS students:
Average GRE Quantitative: 167.58
Average GRE Verbal: 157.36
Average GRE Writing: 3.65
We also require evidence of proficiency with English as a second language for certain students who must provide it. For those students, we generally require a TOEFL score of at least 100 overall (and have strong preferences for better scores), and per university guidelines, will not admit those falling below that threshold.
Letters of Recommendation
Recommendations for admitted students are invariably excellent, with references holding applicants in the highest esteem relative to other students or employees with whom they have interacted in the past several years. References from professors or employers who can comment directly and in a detailed way on the applicant’s case, aptitude for, and attitude to data science projects are treated with the most weight. We prefer all letters on letterhead.
截止申请时间:
1月22日
The Master of Science in Data Science is a highly-selective program for students with a strong background in mathematics, computer science, and applied statistics. The degree focuses on the development of new methods for data science.
We live in the “Age of the Petabyte,” soon to become “The Age of the Exabyte.” Our networked world is generating a deluge of data that no human, or group of humans, can process fast enough. This data deluge has the potential to transform the way business, government, science, and healthcare are carried out. But too few possess the skills needed to use automated analytical tools and cut through the noise to create knowledge from big data.
A new discipline has emerged to address the need for professionals and researchers to deal with the “data tidal wave.” Its object is to provide the underlying theory and methods of the data revolution. This emergent discipline is known by several names. We call it “data science,” and we have created the world’s first MS degree program devoted to it.