加州大学伯克利分校统计学硕士+ 查看更多
加州大学伯克利分校
统计学硕士
+ 查看更多
- 该课程旨在为学生提供统计技能为学生将来进入该行业领域的职业做准备
- 该项目重点是解决企业遇到的统计问题,而不是准备攻读博士学位
- 该计划是为全日制学生设计的,在两个学期内完成(秋季和春季)。经过导师批准,学生可以多留一个学期,方便参加暑期实习
- 在第一学期,所有学生将参加概率、理论统计和统计计算方面的研究生强化课程
- 在第二学期,学生将学习现代应用统计学的高级课程、一门选修课和一门Capstone course。在Capstone course中,学生将组建一个团队,解决一个真实世界的问题,并进行一个实质性的数据分析项目。
项目时长:一年全日制 (全日制)
项目授课地点:美国 加利福尼亚州 旧金山
申请要求
申请流程
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核心课程:
· STAT 201A Introduction to Probability at an Advanced Level
Distributions in probability and statistics, central limit theorem, Poisson processes, modes of convergence, transformations involving random variables.
· STAT 201B Introduction to Statistics at an Advanced Level
Estimation, confidence intervals, hypothesis testing, linear models, large sample theory, categorical models, decision theory.
· STAT 230 Modern applied statistics
Distributions in probability and statistics, central limit theorem, Poisson processes, modes of convergence, transformations involving random variables.
· STAT 201B Introduction to Statistics at an Advanced Level
Estimation, confidence intervals, hypothesis testing, linear models, large sample theory, categorical models, decision theory.
· STAT 230 Modern applied statistics
选修课:
Intro to Computer Vision and Computational Photography
Statistical Learning Theory
Randomness and Computation
Machine Learning
Designing, Visualizing and Understanding Deep Neural Networks
Urban Informatics and Visualization
Optimization Models in Engineering
Convex Optimization and Approximation
Hierarchical Statistical Models in EnvSci2
Applied Data Science with Venture Applications
Mathematical Programming (optimization)
Applied Stochastic Processes
Learning and Optimization
Applications in Data Analysis
Applied Machine Learning
Data Mining and Analytics
Applied Natural Language Processing
Natural Language Processing
Machine Learning in Education
Advanced Matrix Computation
Fundamentals of Business
Statistical Analysis of Categorical Data
Causal Inference 1
Stochastic Processes
Sampling Surveys
Introduction to Time Series
Modern Statistical Prediction and Machine Learning
The Design and Analysis of Experiments
Experimental Design
Analysis of Time Series
Special Topics
Statistical Models: Theory and Application
Causal Inference
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