返回统计DS专业申请方案

康奈尔大学
Applied Statistics and Data Science

  • 该项目是为那些有志于在商业、工业、政府或科学研究领域从事专业工作的人开设的

  • 应用统计学硕士课程提供严格的现代数据分析技能培训，这些技能在任何职业领域都很受欢迎

  • 目前，康奈尔大学是唯一提供这种课程的常春藤联盟大学

  • MPS项目有另外一个硕士课程，即数据科学，比统计学硕士更加强调计算机科学的应用，例如，高性能计算、数据库等


项目时长：一年全日制 （全日制）

项目授课地点：美国 纽约州 伊萨卡
申请要求


学术


  • 申请人必须持有定量导向流的学士学位，并且可以来自多个领域；例如，统计、数学、工程、物理、农业、生物、社会或计算机科学

  • 申请人必须有充足的数学课程：两个学期的微积分；一种基本的非微积分统计；一门矩阵代数课程


TOEFL


listening 15, writing 20, reading 20, speaking 22

GRE


需要递交

IELTS


总分：7+

其他要求




申请流程

第1步：准备好申请文件

  • 推荐信
两封推荐信，可以增加一份企业推荐信


  • SOP

Introduce yourself and your academic interests

  · Provide simple background information on your area of interest and how it became of particular interest to you.
 
  · Here you can also share with them how and why you decided to pursue a graduate degree in this field.

Describe your academic background, preparation, and training
 
  · Discuss the skills you have learned from academic, lab, or research experiences (e.g., undergraduate coursework, research opportunities, scholarly writings, jobs in the field, presentations, etc.). Whenever possible, give specific examples and illustrate the points you are making, don’t just simply tell them.
 
  · Talk about the research you conducted – project title or focus, research mentor, your specific role, what you learned, and the outcome. If there were challenges, don’t be afraid to mention what you learned from them. This shows persistence and resilience in the face of adversity– these are also things they are looking for!
 
  · List important papers or thesis project you completed, as well as anything scholarly beyond your academic degree requirements.
 
  · Share relevant work or internship experience as related to the field you are applying to.

Show them you are making an informed decision
 
  · Indicate what you would like to study in graduate school in enough detail to convince the faculty that you understand the scope of research in the discipline and are aware of research trends.
 
  · Show them that you have thoroughly researched the program, its faculty, and research focus areas and why you are applying to this program specifically. This will help you write a more informed essay that is relatable to the faculty who will be reviewing your application.
 
  · Describe why you are a good fit for the program and why the program is a good fit for you.
 
  · If there are specific faculty you are interested in working with, check the program’s ASOP instructions and determine how best to mention this in your essay. Some programs require you to name a professor(s) with whom you would like to work.
 
  · Are there any aspects of the program that are of particular interest to you (immersion program, opportunities for collaboration with others outside of the institution, research centers associated with the program, etc.)?
 
  · Include information that is important to you outside of the program – supportive environment for first-year students, access to amazing literary resources, opportunities to participate in professional/career development programming, etc.
 
  · Professional goals – you may wish to outline what you plan to do after you complete the program as a way of underscoring the importance of your choice to pursue graduate study.
 
  · Share any extracurricular opportunities you have had that show leadership, ability to work with a diverse group of people, teaching skills, etc.
 
  · Research degree applicants should identify specific faculty members whose research interests align with your own interests.


  • Resume

第2步：需要的其他文件

  • 扫描并上传每所就读大学的学校颁发的成绩单，如果是三年制学位需要第三方机构认证成绩单
  • 官方学位证书（申请时如未毕业可不提供）
  • 需要提交 GRE 成绩
  • 提交英语语言能力成绩
  • 护照复印件
  • 本科和/或研究生学位的记录 

第3步：在线填表申请
第4步：递交申请费

核心课程：

STSCI 5030: Linear Models with Matrices (4 credits)

STSCI 5080: Probability Models and Inference (4 credits)

STSCI 5953: MPS Career Management (1 credit)

STSCI 5954: Project Development & Professional Communication (2 credits)

STSCI 5999: Applied Statistics MPS Data Analysis Project (4 credits)

附加必修课程

STSCI 5045: Python Programming and its Applications in Statistics (4 credits)

STSCI 5060: Database Management and SAS High Performance Computing with DBMS (4 credits)

STSCI 5065: Big Data Management and Analysis (3 credits)

统计科学选修课

STSCI 5010: Applied Statistical Computation with SAS (4 credits)

STSCI 5040: R Programming for Data Science (4 credits)

STSCI 5045: Python Programming and its Applications in Statistics (4 credits)

STSCI 5060: Database Management and SAS High Performance Computing with DBMS (4 credits)

STSCI 5065: Big Data Management and Analysis (3 credits)

STSCI 5090: Theory of Statistics (4 credits)

STSCI 5100: Statistical Sampling (4 credits)

STSCI 5140: Applied Design (4 credits)

STSCI 5160: Categorical Data (3 credits)

STSCI 5550: Applied Time Series Analysis (4 credits)

STSCI 5600: Integrated Ethics in Data Science (2 credits)

STSCI 5630: Operations Research Tools for Financial Engineering (4 credits)

STSCI 5640: Statistics for Financial Engineering (4 credits)

STSCI 5740: Data Mining and Machine Learning (4 credits), forbidden overlap with CS 5780 or ORIE 5740

STSCI 5750: Understanding Machine Learning (4 credits)

STSCI 5780: Bayesian Data Analysis: Principles and Practice (4 credits)

STSCI 6070: Functional Data Analysis (3 credits)

STSCI 6520: Computationally Intensive Statistical Methods (4 credits)

STSCI 6780: Bayesian Statistics and Data Analysis (3 credits)

其他批准的 MPS 选修课

AEM 7100: Econometrics I (3 credits)

BTRY 6381: Bioinformatics Programming (3 credits)

BTRY 6830: Quantitative Genomics and Genetics (4 credits)

BTRY 6840: Computational Genetics and Genomics (4 credits)

CS 5780: Machine Learning (4 credits)

CS 5786: Machine Learning for Data Science (4 credits)

ORIE 5510: Introduction to Engineering Stochastic Processes I (4 credits)

ORIE 5580: Simulation Modeling & Analysis (4 credits)

ORIE 5581: Monte Carlo Simulation (2 credits)

ORIE 5600: Financial Engineering with Stochastic Calculus I (4 credits)

ORIE 5610: Financial Engineering with Stochastic Calculus II (4 credits)

ORIE 5741: Learning with Big Messy Data (4 credits)

ORIE 6500: Applied Stochastic Processes (4 credits)

ORIE 6741: Bayesian Machine Learning (3 credits)

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