MS in Data Science
所属信息
基本信息
项目时长
FT: 9-12 months项目学分
30 credits学费估算
$54,304; $1,697/credit申请截止日期
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秋季
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常规
1月15日
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其他
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春季
9月15日 (MS only)
申请信息
托福/GRE Code
3901 【GRE美本学生可Waive】申请费
$85成绩单寄送要求
网申上传
推荐信要求
3
文书要求
Personal Statement - Please give your reasons for deciding to do graduate work in the field you have chosen. We are particularly concerned that you cover these points:
What previous experiences and commitments have brought you to consider applying for graduate training?
What are your chief objectives in applying now?
How do you think this Tufts program will help you in the pursuit of your objectives and vocation?
Please also share any additional information that you think will help the admissions committee understand you and why you are applying to the program. If you are applying to a Cognitive Science program, please describe why you are interested in that interdisciplinary program.
Limit your answer to 5 pages (2,500 words, single spaced).
What previous experiences and commitments have brought you to consider applying for graduate training?
What are your chief objectives in applying now?
How do you think this Tufts program will help you in the pursuit of your objectives and vocation?
Please also share any additional information that you think will help the admissions committee understand you and why you are applying to the program. If you are applying to a Cognitive Science program, please describe why you are interested in that interdisciplinary program.
Limit your answer to 5 pages (2,500 words, single spaced).
Prerequisite
Prerequisites for the MS in Data Science include a bachelor’s degree in a STEM field, including Mathematics, Science, Engineering, Computer Science, or a related discipline
申请材料
Application Fee
Resume/CV
Personal Statement
GRE scores (if applicable)
Official TOEFL/IELTS/Duolingo scores (if applicable)
Transcripts
Three letters of recommendation
Portfolio (optional)
Resume/CV
Personal Statement
GRE scores (if applicable)
Official TOEFL/IELTS/Duolingo scores (if applicable)
Transcripts
Three letters of recommendation
Portfolio (optional)
特点
The MSDS is built upon a disciplinary core of statistics and machine learning, with depth provided by courses in each of the following:
1. Data infrastructure and systems: those systems and strategies that are core to interacting with data, including computer networks, computer security, internet-scale systems, cloud computing, and others.
2. Data analysis and interfaces: those components of computing concentrated around effective human interaction with computers, including human-computer interaction, graphics, visualization, and others.
3. Computational and theoretical aspects of data science: foundations including information theory, signal and image processing, and numerical analysis.
4. Practice of Data science: examples of effective use of Data Science in practice, including case studies and applications of Data Science principles to real-world problems.
1. Data infrastructure and systems: those systems and strategies that are core to interacting with data, including computer networks, computer security, internet-scale systems, cloud computing, and others.
2. Data analysis and interfaces: those components of computing concentrated around effective human interaction with computers, including human-computer interaction, graphics, visualization, and others.
3. Computational and theoretical aspects of data science: foundations including information theory, signal and image processing, and numerical analysis.
4. Practice of Data science: examples of effective use of Data Science in practice, including case studies and applications of Data Science principles to real-world problems.
