M.S. in Data Science (GSAS - CDS)
所属信息
基本信息
项目时长
2 years项目学分
36 credits学费估算
$2,074/credit course申请截止日期
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秋季
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常规
1月22日 by 5PM ET
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其他
申请信息
托福/GRE Code
2596/2596【Required】申请费
$130成绩单寄送要求
网申上传
推荐信要求
3 Letters of recommendation are expected to be on letterhead.
文书要求
In a concisely written statement, please describe your past and present work as it relates to your intended field of study, your educational objectives, and your career goals. In addition, please include your intellectual and professional reasons for choosing your field of study and why your studies/research can best be done at the Graduate School of Arts and Science at NYU.
The statement should not exceed two double-spaced pages.
The statement should not exceed two double-spaced pages.
Prerequisite
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 2021 intake cycle, the average GPA was 3.87. 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, e.g. MOOCs certification or course credit):
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/or 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.
To be considered for the program, you will be required to have completed the following (or equivalents, e.g. MOOCs certification or course credit):
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/or 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.
适宜学生
Average GRE Verbal: 159.3 (80th percentile)
Average GRE Quantitative: 167.4 (90th percentile)
Average GRE Analytical: 4.14 (61st percentile)
Average TOEFL (where required): 111
Average GRE Quantitative: 167.4 (90th percentile)
Average GRE Analytical: 4.14 (61st percentile)
Average TOEFL (where required): 111
