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Master of Science in Financial Engineering - Technology and Algorithmic Finance

纽约大学工学院

NYU Tandon School of Engineering

US美国

  • 学历文凭

    学历文凭

    Masters Degree

  • 专业院系

    专业院系

    Finance and Risk Engineering

  • 开学时间

    开学时间

  • 课程时长

    课程时长

  • 课程学费

    课程学费

    汇率提示

国际学生入学条件

To be considered for admission into the MS in Financial Engineering program, students must have a Bachelor's Degree from an accredited institution and proven proficiency in:

Linear Algebra
Probability Theory
Multivariable Calculus (Advanced)
Applied Statistics
Computer Programming
Applicants must submit official transcripts from each institution attended as well as GRE test scores.
Official Transcripts
Resume
Statement of Purpose
1-minute video
2 Letters Of Recommendation
GRE is only required for students who have studied for fewer than 4 years as undergraduates in the US.
The minimum GPA requirement is a 3.0 out of a 4.0 scale.
IELTS (Academic) - An overall band of 7.0
TOEFL (Internet-based test) - Minimum score of 90
Duolingo English Test - 125
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  • 雅思总分:7
  • 托福网考总分:90
  • 托福笔试总分:160
  • 其他语言考试:Pearson PTE Academics - 65

CRICOS代码:
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课程简介

Graduates of Technology and Algorithmic Finance are actively involved in the development and implementation of the entire spectrum of algorithmic trading strategies, software applications, databases and networks used in modern financial services firms. The techniques it applies bridge computer science and finance to prepare graduate to participate in large-scale and mission-critical projects. Applications include high frequency finance, behavioral finance, agent-based modeling and algorithmic trading and portfolio management. Upon graduation, students of Technology and Algorithmic Finance will have developed software projects ranging from behavioral models to bespoke derivative valuations to financial trading, information management and tools and financial platforms. Students would be familiar with the use and role of technology in front, middle, and back offices, common trading strategies and how to implement and back-test them, and how to create new models and build new useful tools quickly.
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