排名：
U.S. NEWS 46
QS 385
THE 201–250
学生数量：30013
研究生数量：13711
国际生数量：5938
（占比31.60%）
东北大学（简称NEU）是美国顶尖私立研究型大学，也是全美规模最大的私立大学，位于美国东北部马萨诸塞州波士顿市的芬威文化区，拥有73亩的校园，是一片坐落在波士顿中心地带的绿洲，距离芬威公园、购物中心、咖啡厅、交响乐厅和艺术博物馆都只有几分钟的路程。东北大学工程、临床医学、生物医学、药剂学、国际商务等专业均位列全美前50，其本科由八大学院70个专业组成，研究生由九大学院组成，除此之外还在夏洛特以及西雅图开设了两个授课校区，热门专业为工程、商科、物理治疗、药剂学、计算机科学、护理学、新闻学、市场学和电机工程等。特别的是，东北大学的就业服务位列全美第1，理工科毕业生在美国东北部公司的受欢迎程度仅次于麻省理工学院与哈佛大学。
知名校友
兹·斯通，杰夫·克拉克，肖恩·范宁
区域及类型
Urban / 大学 私立混合
热门专业
Business, Management, Marketing, and Related Support Services, Engineering, Computer and Information Sciences and Support Services, Biological and Biomedical Sciences, Health Professions and Related Programs, Social Sciences, Communication, Journalism, and Related Programs, Visual and Performing Arts, Psychology, Mathematics and Statistics
提供学位
Bachelor's, Master's, Post-master's certificate, Doctorate - professional practice, Doctorate - research/scholarship
学费估算
$69,289
本科申请信息
基本信息
本科录取率
7%本科生数量
17432TOEFL Code
3665学期制度
semester入学成绩
SAT录取均分
1495ACT录取均分
34GPA录取均分
4SAT要求
Neither SAT nor ACTTOEFL要求
最低：92，网考成绩：Required of someIELTS要求
Required of some面试要求
Neither required/recommendedCommon App 要求
Yes转学相关
转学所需最低学分
12转学最低GPA
N/A转学接受学期
Fall, Spring转学生比例
5.00%转学录取率
30.00%申请截止日期
-
秋季
-
常规
Jan. 1 -
提早
Nov. 1 -
转学生
4.1
-
春季
-
转学生
10.1
国际生
Fall:Jan. 1SAT/ACT提交
Jan. 1学院及研究生项目
学院项目
Doctor of Education--Online with Residency
Master of Science in Human Resources Management
M.S. in Global Studies and International Relations
M.S. in Commerce and Economic Development
M.S. in Nonprofit Management
Master of Professional Studies in Informatics
M.S. in Corporate and Organizational Communication
Master in Regulatory Affairs for Drugs Biologics and Medical Devices
MPS in Analytics
Master of Professional Studies in Digital Media Connect
M.S. in Project Management
Digital Media MA
学院信息
-
录取率
42.2%
录取平均成绩
-
GRE
V 149.00 /Q 162.0 /A 3.00 平均本科GPA
3.26
语言要求
-
托福
最低79.00 平均97.00 -
雅思
最低6.00
学院项目
Master of Science in Robotics
M.S. in Data Analytics Engineering
M.S. in Electrical and Computer Engineering
M.S. in Physics
Master of Science in Civil Engineering
M.S. in ECE
Master of Science in Mechanical Engineerin
M.S. in Environmental Engineering
Master of Science in Bioengineering
M.S. in Industrial Engineering
Master of Science in Information Systems
学院信息
-
录取率
35.3% -
就业率
42.20% -
平均起薪
87,773
录取平均成绩
-
GRE
V 156.00 /Q 158.0 /A 3.70 GMAT
633.0平均本科GPA
3.34
语言要求
-
托福
最低100.00 平均102.00 -
雅思
最低7.00
学院项目
Master of Science in Environmental Science & Policy
Master of Science in Mechanical Engineering
Master of Science Program in Mathematics
M.S. in Applied Mathematics
Master of Science in Bioinformatics
学院项目
M.S. in Data Science
基本信息
学分
32项目时长
2学费估算
$24,489GMAT Code
暂无托福/GRE Code
3665申请截止日期
秋季
早申请
5月1日
春季
常规
10月15日
申请信息
TOEFL
100.0GRE
V 150.0 / Q 155.0 / A 4.0Prerequisite
Placement Exams Each incoming masters student, regardless of his or her background, takes two placement exams administered one week prior to the beginning of the semester. The two exams cover fundamentals of computer science and programming skills and basic statistics, probability, and linear algebra. If the student does not get a B or above in a part of the placement exam, then the student must take the corresponding introductory course. Introduction to Programming for Data Science (DS 5010) The introductory course on fundamentals of programming and data structures covers data structures (lists, arrays, trees, hash tables, etc.), program design, programming practices, testing, debugging, maintainability, data collection techniques, and data cleaning and preprocessing. This course will have a class project where the students will use the concepts they learn to collect data from the web, clean, and preprocess and ready for analysis. Introduction to Linear Algebra and Probability for Data Science (DS 5020) The introductory course on basics of statistics, probability, and linear algebra covers random variables, frequency distributions, measures of central tendency, measures of dispersion, moments of a distribution, discrete and continuous probability distributions, chain rule, Bayes' rule, correlation theory, basic sampling, matrix operations, trace of a matrix, norms, linear independence and ranks, inverse of a matrix, orthogonal matrices, range and null space of a matrix, the determinant of a matrix, positive semidefinite matrices, eigenvalues and eigenvectors.项目简介
The Master of Science in Data Science curriculum requires five core courses that jointly represent the essential technical skills in data science. Two courses in algorithms and data processing examine foundational concepts and languages, focusing on data representation, storage, manipulation, and query, as well as large-scale computing and optimization. Two core courses in machine learning and data mining introduce concepts on data modeling, representation, uncovering associations, and making predictions. The capstone course presents a holistic view of data science. Through experiential learning, students are exposed to the real-world challenges of implementing data science techniques to solve meaningful problems and effectively communicate with data. The courses are tailored toward technically or mathematically trained students. The five core courses include: Two core courses in algorithms and data processing Two core courses in machine learning and data mining One core course in information visualization Three elective courses are drawn from a selection of courses across Northeastern. Learning Outcomes Students who complete the MS degree will be able to: Collect data from numerous sources (databases, files, XML, JSON, CSV, and Web APIs) and integrate them into a form in which the data is fit for analysis Use R and Python to explore data, produce summary statistics, perform statistical analyses; use standard data mining and machine-learning models for effective analysis Select, plan, and implement storage, search, and retrieval components of large-scale structure and unstructured repositories Retrieve data for analysis, which requires knowledge of standard retrieval mechanisms such as SQL and XPath, but also retrieval of unstructured information such as text, image, and a variety of alternate formats Match the methodological principles and limitations of machine learning and data mining methods to specific applied problems and communicate the applicability and the advantages/disadvantages of the methods in the specific problem to nondata experts Carry out the full data analysis workflow, including unsupervised class discovery, supervised class comparison, and supervised class prediction; Summarize, interpret, and communicate the analysis of results Organize visualization of data for analysis, understanding, and communication; choose appropriate visualization method for a given data type using effective design and human perception principle Develop methods for modeling, analyzing, and reasoning about data arising in one or more application domains such as social science, health informatics, web and social media, climate informatics, urban informatics, geographical information systems, business analytics, bioinformatics, complex networks, public health, and game design Manage, process, analyze, and visualize data at scale. This outcome allows students to handle data where the conventional information technology fail.
Master of Science in Computer Systems Engineering
M.S. in Computer Science
