芝加哥大学
统计学硕士
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统计系以其高素质的师资力量而获得全世界的广泛认可。该项目除了统计学方法论，机器学习，生物统计学和概率论方面的专业知识外，学院还积极从事跨学科研究领域，例如遗传学，数学金融和计量经济学，环境统计学，计算神经科学，计算化学，机器学习和模式识别，科学计算，调查方法等

芝加哥大学课程之所以与众不同，是因为他们将理论上的卓越与对应用学科领域进行了独特的融合。学校将硕士生与博士完全融为一体，并对学生进行同样的高标准教育

该项目的统计硕士课程包括几个组成部分，他们包括：

~通过研讨会和特殊课程接触研究最前沿的统计方法

~该系为大学其他系的研究人员提供咨询服务。在这里，学生将成为一名顾问，作为定量专家来解决其他系重要的统计学问题。通常，两到四个研究生在一名教师的监督下作为一个团队一起工作。团队将通过向学生顾问小组介绍他们的分析来分享他们的经验

~学生通过与老师一起选择的关于自己的硕士论文主题并有机会深入研究统计学问题

~所有硕士课程的学生还将在课程结束时就其硕士论文进行研讨会。学校鼓励学生积极参加课程和研讨会

~该课程可根据学生的背景和学习计划在一到两个学年内完成

项目时长：两年全日制（全日制）

项目授课地点：美国伊利诺伊州芝加哥

申请要求

学术	申请人必须持有四年制学士学位或任何领域的认可学院或大学的同等学历申请人必须完成以下先修课程：Calculus through the Jacobians and Multivariate Intervals, linear/Matrix Algebra, and Elementary Probability or Statistics
TOEFL	总分：90+
GRE	必须递交；定量部分超过90%可以增加申请成功率。
IELTS	总分：7+
其他要求

申请流程

第1步：准备好申请文件

推荐信

三封推荐信：

Applicants should select referees who know their work well and will write a frank and detailed letter of appraisal of them and of their likely success in the graduate statistics program

Candidate Statement

This short essay provides an opportunity to tell university about the interest in statistics, their goals and what will you accomplish during their studies

Resume

第2步：需要的其他文件

扫描并上传每所就读大学的学校颁发的成绩单
官方学位证书（申请时如未毕业可不提供）
强制提交 GRE 成绩
提交英语语言能力成绩
护照复印件
本科和/或研究生学位的记录

第3步：在线填表申请

第4步：递交申请费

核心课程：

Pathways in Data Science

Data Science in Quantitative Finance and Risk Management

Statistical Methods and Applications

Reading and Research: Statistics

Introduction to Data Science I

Introduction to Data Science I

Collaborative Learning in STAT 22000

Elementary Statistics

Statistical Methods and Applications

Statistical Methods and Applications

Statistical Methods and Applications

Applied Regression Analysis

Epidemiology and Population Health

Statistical Models and Methods I

Statistical Models and Methods I

Numerical Linear Algebra

Statistical Theory and Methods I

Statistical Theory and Methods I

Statistical Theory and Methods Ia

Introduction to Mathematical Probability

Introduction to Mathematical Probability

Time Dependent Data

Introduction to Statistical Genetics

Introduction to Causality with Machine Learning

Mathematical Foundations of Machine Learning

Mathematical Foundations of Machine Learning

Statistical Theory and Methods Ia

Distribution Theory

Mathematical Computation I: Matrix Computation Course

Modern Applied Optimization

Numerical Methods for Stochastic Differential Equations

Inverse Problems and Data Assimilation

Introduction to Stochastic Processes I

Mathematical Introduction to Topological Insulators

Applied Dynamical Systems

Applied Linear Algebra

Applied Analysis

Probability and Statistics

Sample Surveys

Time-Series Analysis for Forecasting and Model Building

Time Dependent Data

Applied Linear Statistical Methods

Introduction to Clinical Trials

Introduction to Statistical Genetics

Statistical Applications

Machine Learning

Topics in Deep Learning: Generative Models

Scientific Computing with Python

Brownian Motion and Stochastic Calculus

Master's Seminar

Reading and Research: Statistics

Topics in Distribution-free Inference

Consulting in Statistics