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　　康奈爾大學(xué)應(yīng)用統(tǒng)計(jì)學(xué)碩士 (MPS)怎么樣？

　　一)項(xiàng)目簡(jiǎn)介：

　　項(xiàng)目全稱：The Master of Professional Studies (MPS) in Applied Statistics

　　學(xué)制時(shí)長(zhǎng)：兩個(gè)學(xué)期(1年)

　　所在學(xué)院：統(tǒng)計(jì)與數(shù)據(jù)科學(xué)系

　　未來(lái)適用職業(yè)：工業(yè)工程師、數(shù)學(xué)家、運(yùn)籌學(xué)分析師、定量分析師、數(shù)據(jù)科學(xué)家、研究科學(xué)家或統(tǒng)計(jì)學(xué)家。

　　專業(yè)性質(zhì)：符合STEM計(jì)劃

　　學(xué)費(fèi)：60,286美元(合計(jì)38.5萬(wàn)人民幣)

　　學(xué)分要求：30個(gè)學(xué)分

　　二)康奈爾大學(xué)應(yīng)用統(tǒng)計(jì)學(xué)碩士申請(qǐng)適用人群

　　官網(wǎng)說(shuō)明：具備生物和計(jì)算機(jī)科學(xué)背景等相關(guān)專業(yè)背景學(xué)生適合申請(qǐng)，以及修讀完微積分等先修課程的學(xué)生也可以申請(qǐng)。

　　“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)就讀康奈爾大學(xué)應(yīng)用統(tǒng)計(jì)學(xué)碩士是否需要參加考試?

　　語(yǔ)言基礎(chǔ)考試：托福及雅思

　　TOEFL單項(xiàng)閱讀和寫作不低于20分，說(shuō)話部分不低于22分;IELTS不低于7分

　　入學(xué)考試：GRE，不接受GMAT

　　這里雖然沒(méi)有最低分?jǐn)?shù)線，但是定量分?jǐn)?shù)建議不要低于165分。

　　四)康奈爾大學(xué)應(yīng)用統(tǒng)計(jì)學(xué)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(jì)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(zhǔn)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)

　　以上是關(guān)于康奈爾大學(xué)應(yīng)用統(tǒng)計(jì)學(xué)碩士怎么樣完整介紹，如果您對(duì)美國(guó)留學(xué)感興趣，歡迎您在線咨詢托普仕留學(xué)老師，托普仕留學(xué)專注美國(guó)前30高校申請(qǐng)，助力國(guó)內(nèi)學(xué)子順利獲得美國(guó)藤校入讀資格。