数据分析硕士选哪个学校比较好_催药购买

发布时间：2025-11-09 12:25:50 来源：不能自已网 作者：探索

2) Applied Analytics

比如：MS in Applied Analytics, Graziadio Business School, Pepperdine University

3) Predictive Analytics

• M.S. in Applied and Computational Mathematics & Statistics: Specialty in
Predictive Analytics, The Division of Science, University of Notre Dame

4) Government Analytics

• M.S. in Government Analytics, Krieger School of Arts and Sciences, JHU

3. Special Data Science:

1) Data Science for Public Policy

Master in Data Science for Public Policy, School of Public Policy, Georgetown U

2) Health Data Science

M.S. in Health Data Science, Department of Biostatistics, Harvard

MS in Health Data Science, Center for Health Outcomes Research, Saint Louis

3) Interdisciplinary Data Science

• Master in Interdisciplinary Data Science, Information Initiative, Duke

• M.S. in Data Analytics and Visualization, The Katz School, Yeshiva U

4. 分支项目：Statistics, EE, CS等

即设置在上述专业的一个分支方向

• MS in Statistics Data Science Track, Department of Statistics, Stanford

• MENG in OR and Information Engineering, DS Track, Cornell Tech

• Master of Information Systems Management Concentration in Business Intelligence &
Data Analytics, Heinz College, CMU

• M.S. in Computational Data Science, Language Technologies Institute, School of
Computer Science, CMU

• Master of Electrical Engineering, Data Science Track, Rice

总结一下，上述项目的分析具体情况、Brown,选个学校催药购买 NYU, WPI, UCB, UVA等众多学校都开设独立的DS项目

2. Analytics/Data Analytics

包括：

1) Analytics/Data Analytics

• MS in Analytics, Continuing Liberal and Professional Studies, U of Chicago

• MS in Analytics, College of Engineering, NWU, USC

此外，对申请者的比较要求等等细节问题这里就不写了，从设置上大致可以分为四类：

1. 单独的数据硕士稀幼十七岁Data Science项目

• SM in Data Science, Institute for Applied Computational Science, Harvard University

• MS in Data Science, Data Science Institute, Columbia University

• MSE in Data Science, College of Engineering, UPenn

此外，详情打我电话咨询吧

Data Science Master Program具体开设的数据硕士情况我做了简单总结并且配上了几个项目做例子，其他我没列上去的分析项目、DS项目录取难度、选个学校DS开设情况相对复杂，比较

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