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master of science in data science


the rigorous curriculum focuses on the fundamentals of computer science, statistics, and applied mathematics, while incorporating real-world examples. with options to study online and on-site in state-of-the-art facilities at the johns hopkins applied physics laboratory, students learn from practicing engineers and data scientists. graduates are prepared to succeed in specialized jobs involving everything from the data pipeline and storage, to statistical analysis and eliciting the story the data tells.


upon completing the degree program, students will:


  1. effectively and competitively respond to the growing demand for data scientists.


  1. balance both the theory and practice of applied mathematics and computer science to analyze and handle large-scale data sets.


  1. describe and transform information to discover relationships and insights into complex data sets.


  1. create models using formal techniques and methodologies of abstraction that can be automated to solve real-world problems.



you must meet the general admission requirements that pertain to all master's degree candidates.


your prior education must include the following prerequisites: ﹙1﹚ three semesters or five quarters of calculus, which includes multivariate calculus; ﹙2﹚ one semester/term of advanced math ﹙discrete mathematics is strongly preferred but linear algebra and differential equations will be accepted﹚; ﹙3﹚ one semester/term of java or python ﹙c++ will be accepted but the student must be at least also somewhat knowledgeable in java or python﹚; and ﹙4﹚ one semester/term of data structures. linear algebra or differential equations will be accepted in lieu of discrete mathematics. a grade of b or better must have been earned in each of the prerequisite courses.

您的本科教育必须包括以下先决条件:(1)上过三个学期或五个季度的微积分,其中包括多元微积分;(2)一学期的高数(最好是离散数学,但可以接受线性代数和微分方程式);(3)一个学期的java或python(可以接受c ++,但学生必须至少还具有一定的java或python知识);(4)一学期的数据结构。可以使用线性代数或微分方程代替离散数学。每个先决课程必须达到b-或更好的成绩。

if your prior education does not include the prerequisites listed above, you may still be admitted under provisional status, followed by full admission once you have completed the missing prerequisites. missing prerequisites may be completed with johns hopkins engineering or at another regionally accredited institution.

如果您的本科教育不包括上面列出的先决条件,则您仍然可以临时身份被录取,一旦您完成缺少的前提条件,便可以被完全录取。缺少先决条件的人员可能会与johns hopkins engineering或其他地区认可的机构共同完成。


a minimum score of 600 ﹙paper-based﹚, 250 ﹙computer-based﹚, or 104 ﹙web-based﹚ is required on the toefl. for the ielts, an overall band score of at least 7.0 is required. the admissions office requires official copies of all results.



prerequisite courses 先决课程

introduction to python python简介

intro to programming using java java编程简介

data structures 数据结构

discrete mathematics 离散数学

general applied mathematics 通用应用数学

multivariable calculus and complex analysis 多变量微积分和复杂分析

introduction to ordinary and partial differential equations 常微分方程和偏微分方程介绍

linear algebra 线性代数

applicants whose prior education does not include the prerequisites listed under admission requirements may still be admitted under provisional status, followed by full admission once they have completed the missing prerequisites. all prerequisite courses are available at johns hopkins engineering. these courses do not count toward the degree or certificate requirements.

先前的教育未包括入学要求中列出的先决条件的申请人仍可以临时身份被录取,一旦完成缺少的先决条件,则可以被完全录取。所有先修课程都可以在johns hopkins engineering获得。这些课程不计入学位或证书要求。

foundation courses


statistical methods and data analysis 统计方法与数据分析

algorithms for data science 数据科学算法

these required foundation courses must be taken or waived before all other courses in their respective programs.


required courses 必修课程

principles of database systems or introduction to machine learning 数据库系统原理或机器学习导论

data visualization 数据可视化

introduction to optimization * or computational statistics 优化或计算统计简介

statistical models and regression 统计模型和回归

data science 数据科学

electives 选修课

select one 以下课程选一

large-scale database systems 大型数据库系统

advanced machine learning 高级机器学习

semantic natural language processing 语义自然语言处理

big data processing using hadoop 使用hadoop进行大数据处理

select one 以下课程选一

introductory stochastic differential equations with applications 随机差分微分方程及其应用

probability and stochastic process i 概率与随机过程i

probability and stochastic process ii 概率与随机过程ii

theory of statistics i 统计理论i

theory of statistics ii 统计理论ii

queuing theory with applications to computer science 排队论及其在计算机科学中的应用

data mining 数据挖掘

game theory 博弈论

stochastic optimization & control 随机优化与控制

modeling, simulation, and monte carlo 建模,仿真和蒙特卡洛

additional selections 额外选修课

probabilistic graphical models 概率图形模型

applied topology 应用拓扑

graph analytics 图形分析

social media analytics 社交媒体分析

cloud computing 云计算

artificial intelligence 人工智能

neural networks 神经网络

introduction to machine learning 机器学习简介

applied game theory 应用博弈论

queuing theory with applications to computer science 排队论及其在计算机科学中的应用

game theory 博弈论

real analysis 真实分析

matrix theory 矩阵论

computational methods 计算方法

discrete hybrid optimization 离散混合优化

mathematical methods for signal processing 信号处理的数学方法

introduction to operations research: probabilistic models 运筹学概论:概率模型

monte carlo methods 蒙特卡洛方法

graph theory 图形论

neural networks 神经网络

mathematics of finance 金融数学

mathematics of risk, options, and financial derivatives 风险,期权和金融衍生工具数学

design and analysis of experiments 实验设计和分析

multivariate statistics and stochastic analysis 多元统计与随机分析

bayesian statistics 贝叶斯统计

cryptography 密码学

applied topology 应用拓扑

computational complexity and approximation 计算复杂度和近似值

probabilistic graphical models 概率图形模型

time series analysis 时间序列分析

advanced differential equations: partial differential equations 高级微分方程:偏微分方程

advanced differential equations: nonlinear differential equations and dynamical systems 高级微分方程:非线性微分方程和动力学系统

theory of probability 概率论

independent study 自主学习

capstone project in data science 数据科学实案项目

independent study in data science i 数据科学i自主学习

independent study in data science ii 数据科学ii自主学习


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