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美国-杜克

杜克大学
跨学科数据科学硕士


简 介

杜克大学的跨学科数据科学硕士 (MIDS)将严格的计算和技术培训与批判性思维、团队合作、沟通和协作领导方面的领域知识相结合,培养出可以为任何领域增加价值的数据科学家

· 杜克大学聘请了世界一流的具有丰富经验的数据科学领域授课教授

· 采取小班授课的方式,可以关注到每一位学生的专业知识学习及职业领域发展
 
· 杜克是一所综合性院校,学校为数据科学的学生提供了丰富的跨学科选修课程
 
· 为了学以致用,在课程结束之后,我们的Capstone Projects项目将为学生带来真实的职场体验,方便学生学以致用


项目时长:四个学期

项目授课地点:美国 北卡罗莱纳州  达勒姆
申请要求

学术


  • 申请人必须具有学士学位或认可机构的认可同等学历

  • 申请人应对数据分析充满热情,并具有一定的定量背景


TOEFL


总分:90+

GRE


不强制

IELTS


总分:7+

其他要求


录制一段视频


申请流程

第1步:准备好申请文件

  • 推荐信
三封推荐信,尽可能是学术推荐信

  • SOP

Your purposes and objectives in pursuing graduate study

Your special interests and plans

Your strengths and weaknesses in your chosen field

Any research projects or any independent research in which you have actively participated and how they have influenced your career choice and desire to pursue graduate studies

Any particular reasons you may have for applying to Duke (e.g. you would like to work with a specific faculty member)

  • ESSAY

As part of your online application, you must upload a one-page, single spaced, essay on leadership and teamwork below. The essay should address:

(i) your most significant leadership experiences

(ii) conflicts and disagreements on teams you have been on either as a leader or team member, and how you helped resolve them


  • Resume

This document should summarize your education, academic achievements, work history, and professional accomplishments. It may also include a list of skills, publications, research experiences, and other credentials that demonstrate your preparedness for graduate study.

第2步:需要的其他文件

  • 扫描并上传每所就读大学的学校颁发的成绩单
  • 官方学位证书(申请时如未毕业可不提供)
  • 不强制提交 GRE 成绩
  • 提交英语语言能力成绩
  • 护照复印件
  • 本科和/或研究生学位的记录

第3步:在线填表申请
4步:递交申请费

GT备考

MIDS课程:


  • Unifying Data Science (IDS 701)

This course is focused on how to answer questions effectively using quantitative data. By the end of the course, students will be able to recognize different types of questions (e.g. descriptive, causal, and predictive questions), have an understanding of what methodological approaches are most appropriate for answering each type of question, be able to design and critically evaluate data analysis plans, and understand how to tailor their presentation of results to different audiences.

  • Modeling and Representation of Data (IDS 702)

Statistical models are necessary for analyzing the type of multivariate (often large) datasets that are usually encountered in data science. In this course, you will learn the general work flow for building statistical models and using them to answer inferential questions. You will learn several parametric models such as generalized linear models, models for multilevel data and time series models.

  • Introduction to Natural Language Processing (IDS 703)

Introduction to the rich opportunities for using textual data produced by websites, social media platforms, digitization of administrative and historical records, and new monitoring technologies to gain insights and make decisions.

  • Data Science Ethics (IDS 704)

Data Science tools are not morally neutral. This course is designed to help students think explicitly about their social responsibility as data scientists and the impact on the world of what they are building and analyzing.

  • Practicing Machine Learning (IDS 705)

Automating prediction and decision-making based on data and past experience. Students will learn how and when to apply supervised, unsupervised, and reinforcement learning techniques, and how to evaluate performance. Common pitfalls such as overfitting and data leakage will be explored and how they can be avoided.

  • Data Engineering Systems (IDS 706)

Data Engineering Systems is a course about data and how to manage and build systems. Divided into two halves, part 1 focuses on Relational Databases. These systems are the most common type of database used today and are found in applications ranging from holding cell phone contact lists (both Android and iOS use SQLlite3 internally) to managing every aspect of a large bank or insurance company.

  • Data Logic, Visualization, and Storytelling (IDS 707)

Principles of communicating the implications of a data analysis.
Students will cultivate the ability to think critically and skeptically about the questions they need to answer in a data project and the strategies they are using to answer them. Students will learn the principles behind effective data visualization and how to implement them in real analyses using Tableau software.

  • Data Science Dialogues (IDS 791)

A series of discussions that give students snapshots of data science projects from practitioners and researchers.

  • Capstone Project (IDS 798)

MIDS students join Capstone partnerships their second year and during that year make substantial contributions to these real, complex projects. Project teams can be as large as necessary and can include multiple faculty, postdocs, graduate and undergraduate students, and other staff. Although students work collaboratively, each MIDS student must achieve a specific outcome of interest for the outside party and give a final presentation.

  • MIDS Workshops (IDS 898)

A series of workshops to gain soft skills such as interviewing, negotiating, and networking.


选修课:

· Practicing Data Science (IDS 720)

This course will provide students with extensive hands-on experience manipulating real (often messy, error ridden, and poorly documented) data using the a range of bread-and-butter data science tools (like the command line, git, python (especially numpy and pandas), jupyter notebooks, and more). The goal of these exercises is to ensure students are comfortable working with data in most any form.


· Data Analysis at Scale in Cloud (IDS 721)


Data Analysis at Scale in the Cloud is a project based course with extensive hands-on assignments. This course is designed to give students a comprehensive view of cloud computing including Big Data and Machine Learning. A variety of learning resources will be used including interactive labs on Cloud Platforms (Google, AWS, Azure).


Popular Electives (Sample)


  • Intro to Deep Learning (COMPSCI 675D)

  • Statistical Computation (STA 663L)

  • Probability (MATH 730)

  • Theory & Algorithm Machine Learning (ECE 687D)

  • Machine Learning and Imaging (BME 548L)

  • Foundations of GIS and Geospatial Analysis (ENVIRON 559)

  • Introduction to Social Networks (SOCIOL 728)

  • Machine Learning for FinTech (FINTECH 540)

  • Research Tech Translation (I&E 710)

  • Mathematical Finance (MATH 780)

  • Social Networks & Pol Interdependence (POLSCI 634)

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