芝加哥大学统计学硕士+ 查看更多
芝加哥大学
统计学硕士
+ 查看更多
- 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
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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
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
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