康奈尔大学数据分析硕士项目介绍及申请解析

来源:SOHU  [  作者:西读君   ]  责编:王强  |  太阳城亚洲

原标题:康奈尔大学数据分析硕士项目介绍及申请解析

康奈尔大学

Cornell University康奈尔大学

地理位置:Ithaca, NY

Master of Engineering-OperationsResearch and Information Engineering

康奈尔大学的工程学院设有运筹与信息工程专业,其中一个方向为数据分析(Data Analytics),Ithaca主校区和Cornell Tech科技园校区都有招生。每年秋季入学,学制一年,需要修30学分,本专业下设七个方向。需要申请者本科修读过微积分、概率与统计,掌握C、C++或Java相关技术。

申请要点

◇ 接受GRE,不接受GMAT

◇ TOEFL最低100,单项最低分数:写作20,听力15,阅读20,口语22;IELTS最低7.0

◇ 无需工作经验

◇ 截止日期:

Ithaca校区:12月1日

Cornell Tech校区:12月5日,1月18日, 4月15日◇ 学费:$52,612课程设置核心课程:从以下三个模块的课程列表中修满12学分以上

Optimization Modeling Courses:

Revenue Optimization and Marketplace Design

Nonlinear Optimization

Discrete Models

Intro to Game Theory

Spreadsheet-Based Modeling and Data Analysis

Inventory Management

Optimization I

Optimization II

Topics in Linear Optimization

Optimization Modeling in Finance

Stochastic Modeling Courses:

Revenue Optimization and Marketplace Design

Intro to Financial Engineering

OR Tools for Financial Engineering

Inventory Management

Service System Modeling and Design

Prob and Stat II

Simulation Modeling and Analysis

Monte Carlo Simulation

Monte Carlo Methods in FE

Stochastic Processes

Intro to Engineering Stochastic Processes II

Financial Engineering with Stochastic Calculus I

Financial Engineering with Stochastic Calculus II

Credit Risk: Modeling, Valuation, and Mgmt

Quantitative Methods of Financial Risk Mgmt

Data Science and Statistical Modeling Courses:

OR Tools for Financial Engineering

Statistical Data Mining I

Learning with Big Messy Data

Information Theory, Probabilistic Modeling, & DeepLearning with Scientific& Financial Applications

Applied Time Series Analysis

Statistics for Financial Engineering

Machine Learning for Intelligent Systems

Machine Learning for Data Science

Linear Models with Matrices

Theory of Statistics

Data Mining and Machine Learning

Bayesian Data Analysis: Principles and Practice

Data Analytics

方向选修课程:从Data Science and Statistical Modeling列表中选修两门课从以下Data Analytics领域选修课中选修一门课程:

Algorithms for Markets

Introduction to Database Systems

Computational Techniques for Analyzing Clinical Data

Data Driven Analytics

Revenue Management

Web Scraping & Data Mining for Hospitality Dec Making

Real Estate Statistical Modeling

Visual Data Analytics for the Web

Marketing Research

Data-driven Marketing

Strategy and Tactics of Pricing

Spreadsheet-Based Modeling and Data Analysis

Python Programming and its Applications in Statistics

Applied Design

Multivariate Analysis

Categorical Data

Nonparametric Inference and Sequential Analysis

Databases and Statistical Computing

Big Data Management and Analysis

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