康奈尔大学Applied Statistics and Data Science+ 查看更多
康奈尔大学
Applied Statistics and Data Science
+ 查看更多
- 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
申请流程
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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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