康奈尔大学的应用统计学专业是开设在统计与数据科学学院下为期一年的硕士课程，有核心课程、深入统计分析项目和选修课程的三个课程组成部分，康奈尔大学应用统计硕士专业为学生提供了坚实的理论统计基础，是唯一一所提供此类课程的常春藤盟校，并且该专业是STEM项目。该专业有Two Options：

1.选项I侧重于统计分析技术。

2.选项II，数据科学，它和统计技术一样，比选项I更强调计算机科学，例如高性能计算、数据库、中间件和脚本。

Core Required Courses

STSCI 5030: Linear Models with Matrices (4 credits)

STSCI 5080: Probability Models and Inference (4 credits)

STSCI 5953: MPS Professional Development (1 credit)

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

Additional Required Courses for Option II

STSCI 4060: Python Programming and its Applications in Statistics (3 credits)

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

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

Statistical Science Electives

Option I students must take at least 12 credit hours and Option II students at least 4 credits of Statistical Science electives from this list. Option II students cannot use STSCI 4060, 5060, or 5065 as a statistical science elective since these courses are required as core option II courses. 

STSCI 3100: Statistical Sampling (4 credits)

STSCI 4520: Statistical Computing (4 credits)

STSCI 4060: Python Programming and its Applications in Statistics (3 credits)

STSCI 4100: Multivariate Analysis (4 credits)

STSCI 4110: Categorical Data (4 credits)

STSCI 4140: Applied Design (4 credits)

STSCI 4270: Survival Analysis (3 credits)

STSCI 4550: Applied Time Series Analysis (4 credits)

STSCI 4600: Statistics for Risk Modeling (3 credits)

STSCI 4630: Operations Research Tools for Financial Engineering (3 credits)

STSCI 4740: Data Mining and Machine Learning (4 credits)

STSCI 4780: Bayesian Data Analysis: Principles and Practice

STSCI 5640: Statistics for Financial Engineering (4 credits)

STSCI 5010: Applied Statistical Computation with SAS (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 6070: Functional Data Analysis (3 credits)

STSCI 6520: Computationally Intensive Statistical Methods (4 credits)

Other Approved MPS Electives

AEM 7100: Econometrics I (3 credits)

BTRY 3090: Theory of Interest (3 credits)

BTRY 4830: Quantitative Genomics and Genetics (4 credits)

BTRY 4840: Computational Genetics and Genomics (4 credits)

BTRY 6381: Bioinformatics Programming (3 credits)

CS 4780: Machine Learning (4 credits)

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

MATH 4740: Stochastic Processes (4 credits)

ORIE 3120: Practical Tools for Operations Research, Machine Learning, and Data Science (4 credits)

ORIE 4630: Operations Research Tools for Financial Engineering (3 credits)

ORIE 4741: Learning with Big Messy Data (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 5640: Statistics for Financial Engineering (4 credits)

ORIE 6500: Applied Stochastic Processes (4 credits)

ORIE 6741: Bayesian Machine Learning (3 credits)

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

A statement of purpose (including your desired option of the MPS program).

Two academic letters of recommendation. You may also submit a professional reference as the third letter of recommendation if you have one.

Complete transcripts from all undergraduate and graduate level studies undertaken at all schools.

English Language Proficiency Requirement:

A resume, which is optional but does assist the admissions committee with evaluating your background.

MPS Early Admit Option: If you are a Cornell undergraduate applying for the MPS Early Admit Option, please download the petition form, and then upload the completed form to the online application system along with your other application materials. Please note that the Early Admit Option is open only to Cornell undergraduates.

The deadline for Fall admissions is February 1.

该专业应用比较广泛，工业工程师、数学家、运筹学分析师、定量分析师、数据科学家、研究科学家或统计学家等，可以到企业、事业单位和经济、管理部门从事统计调查、统计信息管理、数量分析等开发、应用和管理工作，或在科研、教育部门从事研究和教学工作。

1.该专业需要申请人本科学位，并且需要有数学背景：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。

2.TOEFL各项最低分要求为Speaking 22+；Reading 20+；Listening 15+；Writing 20+/IELTS 7.0+

3.GPA至少3.0+，最好3.5+

4.要求提交GRE，不接受GMAT，但没有最低分数要求