康奈爾大學應用統(tǒng)計學碩士(MPS)怎么樣
2023-06-04 15:56:25 來源：中國教育在線
隨著人們經(jīng)濟水平的提高，對于很多家庭來說，留學不再是一個可望而不可及的事情，許多人都想要留學，那其中康奈爾大學應用統(tǒng)計學碩士(MPS)怎么樣？針對這個問題，下面中國教育在線小編就來和大家分享一下。
一)項目簡介：
項目全稱：The Master of Professional Studies (MPS) in Applied Statistics
學制時長：兩個學期(1年)
所在學院：統(tǒng)計與數(shù)據(jù)科學系
未來適用職業(yè)：工業(yè)工程師、數(shù)學家、運籌學分析師、定量分析師、數(shù)據(jù)科學家、研究科學家或統(tǒng)計學家。
專業(yè)性質：符合STEM計劃
學費：60,286美元(合計38.5萬人民幣)
學分要求：30個學分
二)康奈爾大學應用統(tǒng)計學碩士申請適用人群
官網(wǎng)說明：具備生物和計算機科學背景等相關專業(yè)背景學生適合申請，以及修讀完微積分等先修課程的學生也可以申請。
“The program is intended for students with a quantitatively-oriented Bachelors 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)就讀康奈爾大學應用統(tǒng)計學碩士是否需要參加考試?
語言基礎考試：托福及雅思
TOEFL單項閱讀和寫作不低于20分，說話部分不低于22分;IELTS不低于7分
入學考試：GRE，不接受GMAT
這里雖然沒有最低分數(shù)線，但是定量分數(shù)建議不要低于165分。
四)康奈爾大學應用統(tǒng)計學課程安排
核心課程：
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)
其他選修課程 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)
統(tǒng)計選修課程：
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)
其他獲批準的MPS選修課：
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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