MS in Data Science
该项目由计算机科学与工程系、统计系、信息学院和生物统计系共同拥有，由文理学院负责行政管理。数据科学硕士结合四个部门的教育优势，旨在为每个学生接受统计技能和计算技能培训，该课程的毕业生具备高级水平的数据和分析能力。
数据科学通常被视为 (1) 计算机和信息科学 (2) 统计科学和 (3) 领域专业知识的融合。这三个支柱并不对称：前两个共同代表数据科学中使用的核心方法论和技术，而第三个支柱是应用该方法论的应用领域。在该项目中，核心数据科学培训侧重于前两个支柱，以及应用技能解决应用领域问题的实践。
通过数据科学硕士，所有学生都将能够：识别相关数据集，将适当的统计和计算工具应用于数据集以回答个人、组织或政府机构提出的问题，设计和评估适合数据的分析程序，以及在多计算机环境中的大型异构数据集上有效地实现这些。
核心课程
MATH 403: Introuction to Discrete Mathematics
EECS 402: Programming for Scientists an Engineers
EECS 403: Data Structures for Scientists an Engineers
EECS 409: Data Science Colloquium
1 of the following
l BIOSTATS 601: Probability an Distribution
l STATS 425: Introuction to Probability
l STATS 510: Probability an Distribution
1 of the following
l BIOSTATS 602: Biostatistical Inference
l STATS 426: Introuction to Theoretical Statistics
l STATS 511: Statistical Inference
Expertise in Data Management an Manipulation
1 of the following
l EECS 484: Database Management Systems
l EECS 584: Avance Database Systems
1 of the following
l EECS 485: Web Systems
l EECS 486: Information Retrieval an Web Search
l EECS 549/SI 650: Information Retrieval
l SI 618: Data Manipulation Analysis
l STATS 507: Data Science Analytics using Python
Expertise in Data Science Techniques
1 of the following:
l BIOSTAT 650: Applie Statistics I: Linear Regression
l STATS 500: Statistical Learning I: Linear Regression
l STATS 513: Regression an Data Analysis
1 from the following:
l STATS 415: Data Mining an Statistical Learning
l STATS 503: Statistical Learning II: Multivariate Analysis
l EECS 505: Computational Data Science an ML
l EECS 545: Machine Learning
l EECS 476: Data Mining
l EECS 576: Avance Data Mining
l SI 670: Applie Machine Learning
l SI 671: Data Mining: Methos an Applications
l BIOSTAT 626: Machine Learning for Health Sciences
Capstone
l STATS 504: Principles an Practices in Effective Statistical Consulting
l STATS 750: Directe Reaing
l EECS 599: Directe Stuy
l SI 599-00X: Computational Social Science
l SI 691: Inepenent Stuy
l SI 699-004: Big Data Analytics
l BIOSTAT 610: Reaing in Biostatistics
l BIOSTAT 629: Case Stuies for Health Big Data
l BIOSTAT 698: Moern Statistical Methos in Epiemiologic Stuies
l BIOSTAT 699: Analysis of Biostatistical Investigations
申请信息
申请截止日期 2022年2月1日
先修课程要求
l 2个学期的大学微积分
l 1学期线性或矩阵代数
l 1门计算机基础课程
申请材料
l 申请费 90美元
l 成绩单（需要4年制本科，澳洲需要是4年制honor学位才可以）
l 托福：最低84分
l GRE 不要求，审理时也不会考虑
l 推荐信 2封
l Statement of Purpose
The Statement of Purpose gives you an opportunity to tell us about yourself an your reasons for wishing to pursue a grauate egree in Data Science at Michigan. You shoul tell us about your backgroun, motivating influences that arouse your interest in Data Science, an your career goals an objectives. If you are currently enrolle in a grauate program elsewhere, you shoul tell us why you wish to change institutions or egree programs.
l Personal Statment
How have your backgroun an life experiences, incluing cultural, geographical, financial, eucational or other opportunities or challenges, motivate your ecision to pursue a grauate egree at the University of Michigan? For example, if you grew up in a community where eucational, cultural, or other opportunities were either plentiful or especially lacking, you might iscuss the impact this ha on your evelopment an interests. This shoul be a iscussion of the journey that has le to your ecision to seek a grauate egree. The statement will be rea by the faculty members of the amissions committee.
