约翰霍普金斯大学数据科学硕士专业培养下一代数据科学的领导者!
随着网络的普及，每时每刻都会有大量的数据被产生和存储下来，如何将这些数据变成有价值的商业信息则成为了各个公司竞争的核心之力，从而诞生了数据科学专业，为了顺应时代的需求，约翰霍普金斯大学就开设了数据科学硕士专业，下面，就随小编来看看吧，希望对大家有所帮助：
MSE in Data Science
数据科学硕士学位将提供应用数学、统计学和计算机科学的培训，作为理解和欣赏现有数据科学工具的基础。该项目旨在通过强调掌握将真实世界的数据驱动问题转化为数学问题所需的技能，并通过使用各种科学工具来解决这些问题，从而培养下一代数据科学的领导者。
课程设置：
数据科学导论(必修)
EN.553.636 Introduction to Data Science
核心区域
统计学
秋季和春季学期
EN.553.630 Introduction to Statistics.
秋季学期
EN.553.632 Bayesian Statistics
EN.553.730 Statistical Theory I
EN.601.677 Causal Inference
EN.553.613 Applied Statistics and Data Analysis
春季学期
EN.553.731 Statistical Theory II
EN.553.738 High-Dimensional Approximation, Probability, and Statistical Learning
EN.553.733 Advanced Topic in Bayesian Analysis
EN.553.739 Statistical Pattern Recognition Theory & Methods
EN.570.654 Geostatistics: Understanding Spatial Data
EN.553.639 Time Series Analysis
机器学习
秋季与春季学期
EN.601.675 Machine Learning
秋季学期
EN.520.612 Machine Learning for Signal Processing
EN.520.637 Foundations of Reinforcement Learning
EN.520.647 Information Theory
EN.520.651 Random Signal Analysis
EN.525.724 Introduction to Pattern Recognition (online)
EN.553.740 Machine Learning I
EN.580.709 Sparse Representations in Computer Vision and Machine Learning
EN.601.634 Randomized and Big Data Algorithms
EN.601.677 Causal Inference
EN.601.682 Machine Learning: Deep Learning
EN.601.780 Unsupervised Learning: Big Data to Low-Dimensional Representations
EN.601.674 Machine Learning: Learning Theory
春季学期
EN.520.638 Deep Learning
EN.520.648 Compressed Sensing and Sparse Recovery
EN.520.666 Information Extraction
EN.535.741 Optimal Control and Reinforcement Learning (online)
EN.553.602 Research and Design in Applied Mathematics: Data Mining
EN.553.738 High-Dimensional Approximation, Probability, and Statistical Learning
EN.553.741 Machine Learning II
EN.601.676 Machine Learning: Data to Models
EN.625.692 Probabilistic Graphical Models (online)
量化
秋季学期
EN.553.761 Nonlinear Optimization I
EN.553.665 Introduction to Convexity
EN.520.618 Modern Convex Optimization
春季学期
EN.553.762 Nonlinear Optimization II
EN.553.763 Stochastic Search and Optimization
EN.601.681 Machine Learning: Optimization
EN.553.766 Combinatorial Optimization
计算机
秋季与春季学期
EN.601.633 Introduction to Algorithms
秋季学期
EN.553.688 Computing for Applied Mathematics
EN.601.620 Parallel Programming
EN.601.647 Computational Genomics: Sequences
春季学期
EN.601.646 Sketching and Indexing for Sequences
EN.520.617 Computation for Engineers
选修课
计算医学
秋季与春季学期
AS.410.633 Introduction to Bioinformatics (online)
AS.410.635 Bioinformatics: Tools for Genome Analysis (online)
EN.605.620 Algorithms for Bioinformatics (cannot be taken with EN.605.621)
EN.605.621 Foundations of Algorithms (cannot be taken with EN.605.620)
秋季学期
AS.410.671 Gene Expression Data Analysis and Visualization (online)
EN.605.653 Computational Genomics
春季学期
EN.553.650 Computational Molecular Medicine (offered spring)
EN.520.659 Machine Learning for Medical Applications
计算机视觉
秋季与春季学期
EN.601.661 Computer Vision
EN.520.614 Image Processing and Analysis
秋季学期
EN.520.646 Wavelets & Filter Banks
EN.520.665 Machine Perception
春季学期
EN.601.783 Vision as Bayesian Inference
EN.520.623 Medical Image Analysis
EN.553.693 Mathematical Image Analysis
EN.520.615 Image Processing and Analysis II
EN.525.733 Deep Learning for Computer Vision (online)
金融数学
秋季学期
EN.553.627 Stochastic Processes and Applications to Finance I
EN.553.641 Equity Markets and Quantitative Trading
EN.553.642 Investment Science
EN.553.644 Introduction to Financial Derivatives
EN.553.646 Risk Measurement and Management in Financial Markets
EN.553.649 Advanced Equity Derivatives
春季学期
EN.553.628 Stochastic Processes and Applications to Finance II
EN.553.645 Interest Rate and Credit Derivatives
EN.553.753 Commodity Markets and Trade Finance
数学与数据科学
秋季学期
EN.553.633 Monte Carlo Methods
EN.553.792 Matrix Analysis and Linear Algebra
EN.601.634 Randomized and Big Data Algorithms
语言与言语
秋季学期
EN.601.665 Natural Language Processing
春季学期
EN.520.666 Information Extraction
EN.520.680 Speech and Auditory Processing by Humans and Machines
EN.601.769 Events Semantics in Theory and Practice
额外课程
EN.520.650 Machine Intelligence
EN.580.691 Learning, Estimation and Control
EN.601.615 Databases
EN.601.663 Algorithms for Sensor-Based Robotics
Data Science Capstone Experience
EN.553.806 Capstone Experience
申请条件：
学生必须完成学士学位，最好是工程、数学、计算机科学或其他科学专业。此外，候选人应至少完成微积分(通过多元微积分)，线性代数，微分方程，概率，计算机编程(如c++或Python)的本科水平的课程，最好辅之以统计学课程和至少一门证明写作课程。
语言要求：
托福成绩不低于100分(网考)或600分(纸考)，雅思成绩达到7分
约翰斯·霍普金斯大学
约翰斯·霍普金斯大学(The Johns Hopkins University)，简称Hopkins或JHU，成立于1876年，是一所世界顶级的著名私立大学，美国第一所研究型大学，也是北美顶尖大学学术联盟美国大学协会(AAU)的14所创始校之一。美国国家科学基金会连续33年将该校列为全美科研经费开支最高的大学。截止目前，学校的教员与职工共有37人获得过诺贝尔奖。
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!
