康奈尔大学应用统计学硕士(MPS)怎么样？

　　康奈尔大学应用统计学硕士 (MPS)怎么样？

　　一)项目简介：

　　项目全称：The Master of Professional Studies (MPS) in Applied Statistics

　　学制时长：两个学期(1年)

　　所在学院：统计与数据科学系

　　未来适用职业：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。

　　专业性质：符合STEM计划

　　学费：60,286美元(合计38.5万人民币)

　　学分要求：30个学分

　　二)康奈尔大学应用统计学硕士申请适用人群

　　官网说明：具备生物和计算机科学背景等相关专业背景学生适合申请，以及修读完微积分等先修课程的学生也可以申请。

　　“The program is intended for students with a quantitatively-oriented Bachelor's degree in the agricultural, biological, computer, engineering, mathematical, physical, social, or statistical sciences. Our application is open to any major as long as students meet the minimum mathematical background necessary to keep up with course work. These are: two semesters of calculus, one semester of elementary non-calculus based statistics, a course in matrix algebra, and familiarity with standard computing tools (e.g., spreadsheets). To meet these prerequisite requirements, courses must be from an accredited college or university and be listed on an official transcript. However, space in the MPS program is limited and preference is given to applicants with more than the minimal mathematical background.”

　　三)就读康奈尔大学应用统计学硕士是否需要参加考试?

　　语言基础考试：托福及雅思

　　TOEFL单项阅读和写作不低于20分，说话部分不低于22分;IELTS不低于7分

　　入学考试：GRE，不接受GMAT

　　这里虽然没有最低分数线，但是定量分数建议不要低于165分。

　　四)康奈尔大学应用统计学课程安排

　　核心课程：

　　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)

　　其他选修课程 II：

　　STSCI 5045: 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)

　　统计选修课程：

　　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 5045, 5060, or 5065 as a statistical science elective since these courses are required as core option II courses.

　　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 (3 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 (4 credits)

　　STSCI 5550: Applied Time Series Analysis (4 credits)

　　STSCI 5600: Statistics for Risk Modeling (3 credits)

　　STSCI 5630: Operations Research Tools for Financial Engineering (3 credits)

　　STSCI 5640: Statistics for Financial Engineering (4 credits)

　　STSCI 5740: Data Mining and Machine Learning (4 credits)

　　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)

　　其他获批准的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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