约翰霍普金斯大学的数据科学硕士专业 21世纪最热门的学科!
近些年，随着数据运营所带来得利好日益明显，很多机构或者企业开始重视海量数据背后所蕴藏的无限“财富”，从而对数据科学人才的需求量与日俱增，国内目前鲜少有大学开设此专业，而美国很多大学都开设了数据科学专业，其中最出色的就是约翰霍普金斯大学，下面，小编就带大家深入了解约翰霍普金斯大学的数据科学硕士专业，希望对大家有所帮助：
Data Science, Master of Science
数据科学项目平衡理论和应用，使你可以促进你的职业生涯长期。
严格的课程集中于计算机科学、统计学和应用数学的基础，同时结合了现实世界的例子。通过向实习工程师和数据科学家学习，毕业生可以胜任从数据管道、存储到统计分析以及引出数据故事等各种专业工作。
课程设置：
Prerequisite Courses 1
EN.605.101 Introduction to Python
EN.605.201 Intro to Programming Using Java
EN.605.202 Data Structures
EN.605.203 Discrete Mathematics
EN.605.206 Introduction to Programming Using Python
EN.625.201 General Applied Mathematics
EN.625.250 Multivariable Calculus and Complex Analysis
EN.625.251 Introduction to Ordinary and Partial Differential Equations
EN.625.252 Linear Algebra
Foundation Courses
EN.685.621 Algorithms for Data Science 2
EN.625.603 Statistical Methods and Data Analysis 2
Required Courses
EN.685.648 Data Science
EN.605.662 Data Visualization
EN.625.661 Statistical Models and Regression
EN.605.641 Principles of Database Systems
or EN.605.649 Introduction to Machine Learning
EN.625.615 Introduction to Optimization 3
or EN.625.664 Computational Statistics
Select one EN.625.xxx course from Electives or Additional Selections
Electives
Select one of the following:
EN.605.741 Large-Scale Database Systems
EN.605.746 Advanced Machine Learning
EN.605.748 Semantic Natural Language Processing
EN.605.788 Big Data Processing Using Hadoop
Select one of the following:
EN.625.714 Introductory Stochastic Differential Equations with Applications
EN.625.721 Probability and Stochastic Process I
EN.625.722 Probability and Stochastic Process II
EN.625.725 Theory Of Statistics I
EN.625.726 Theory of Statistics II
EN.625.734 Queuing Theory with Applications to Computer Science
EN.625.740 Data Mining
EN.625.741 Game Theory
EN.625.743 Stochastic Optimization & Control
EN.625.744 Modeling, Simulation, and Monte Carlo
Additional Selections
EN.605.625 Probabilistic Graphical Models
EN.605.628 Applied Topology
EN.605.632 Graph Analytics
EN.605.633 Social Media Analytics
EN.605.635 Cloud Computing
EN.605.645 Artificial Intelligence
EN.605.647 Neural Networks
EN.605.649 Introduction to Machine Learning
EN.605.724 Applied Game Theory
EN.605.725 Queuing Theory with Applications to Computer Science
EN.605.726 Game Theory
EN.625.601 Real Analysis
EN.625.609 Matrix Theory
EN.625.611 Computational Methods
EN.625.618 Discrete Hybrid Optimization
EN.625.620 Mathematical Methods for Signal Processing
EN.625.623 Introduction to Operations Research: Probabilistic Models
EN.625.633 Monte Carlo Methods
EN.625.636 Graph Theory
EN.625.638 Neural Networks
EN.625.641 Mathematics of Finance
EN.625.642 Mathematics of Risk, Options, and Financial Derivatives
EN.625.662 Design and Analysis of Experiments
EN.625.663 Multivariate Statistics and Stochastic Analysis
EN.625.665 Bayesian Statistics
EN.625.680 Cryptography
EN.625.687 Applied Topology
EN.625.690 Computational Complexity and Approximation
EN.625.692 Probabilistic Graphical Models
EN.625.695 Time Series Analysis
EN.625.717 Advanced Differential Equations: Partial Differential Equations
EN.625.718 Advanced Differential Equations: Nonlinear Differential Equations and Dynamical Systems
EN.625.728 Theory of Probability
Independent Study
EN.685.795 Capstone Project in Data Science
EN.685.801 Independent Study in Data Science I
EN.685.802 Independent Study in Data Science II
入学要求：
三个学期或四分之五的微积分课程，包括多元微积分;
一学期/学期的高等数学(离散数学优先，线性代数和微分方程也可以);
一学期/学期的Java或Python (c++也可以，但学生必须至少对Java或Python有一定的了解);
一学期/学期数据结构
线性代数或微分方程将被接受代替离散数学。必须在每门预备课程中取得B -或更高的成绩。申请人之前的教育不包括上述所列的先决条件，仍然可以在临时状态下注册，然后一旦他们完成了缺失的先决条件，完全录取状态。缺少先决条件可能与约翰霍普金斯工程(所有先决条件可用)或在另一个地区认可的机构完成。申请人在本科后半部分的学习中，平均成绩至少为3.0(满分为4.0)(B级或以上)。如果他们希望他们的学术和专业背景被考虑，申请人可以提交详细的résumé。
除了微积分的要求外，还提供了计算机科学和数学的本科课程。
综上所述，以上讲的就是关于约翰霍普金斯大学数据科学硕士专业的相关问题介绍，希望能给各位赴美留学的学子们指点迷津。近年来，赴美留学一直是广大学生最热门的话题，同时，很多学生对于签证的办理、院校的选择、就业的前景、学习的费用等诸多问题困扰不断，别担心，IDP留学专家可以为你排忧解难，同时，更多关于赴美留学的相关资讯在等着你，绝对让你“浏览”忘返。在此，衷心祝愿各位学子们能够顺利奔赴自己心目中理想的学校并且学业有成!
