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自然科学

数据科学硕士

 学校:  

纽约大学

   硕士生项目

MS in Data Science

标准考试成绩要求

TOFEL:100

学年学制

2 Years

学年学费

$2,212.00/1 Credit

奖学政策

提供奖学金

所在校区

暂无

专业排名

暂无

招生人数

暂无

录取要求

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.

申请材料清单

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

专业介绍

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.

课程设置

  • DS-GA 1001 Introduction to Data Science
  • DS-GA 1002 Probability and Statistics for Data Science
  • DS-GA 1003 Machine Learning
  • DS-GA 1004 Big Data
  • DS-GA 1006 Capstone Project and Presentation
  • DS-GA 1005 Inference and Representation
  • DS-GA 1008 Deep Learning
  • DS-GA 1011 Natural Language Processing with Representation Learning
  • DS-GA 1012 Natural Language Understanding and Computational Semantics
  • DS-GA 1013 Optimization-based Data Analysis
  • Optimization and Computational Linear Algebra
  • CI-GA 1170 Fundamental Algorithms
  • CSCI-GA 2433 Database Systems
  • STAT-GB 2302 Forecast Time Series Data
  • MATH-GA 2751 Risk & Portfolio Management with Econometrics
  • CSI-GA 2566 Foundations of Machine Learning
  • STAT-GB 3383 Frequency Domain Time Series
  • BIOL-GA 1127/CSCI-GA 2520 Bioinformatics & Genomes
  • STAT-GB 2301 Regression & Multivariate Data Analysis
  • INFO-GB 3391 Research Seminar: Data Science
  • CSCI-GA 3033 Special Topics Computer Science: Statistical Natural Language Processing
  • Spring 2018 LING-GA 3340.001 Topic: Deep Learning in Semantics
  • Spring 2018 APSTA-GE 2062 Ethics of Data Science
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