里海大学今年新开了数据科学专业，里海大学综合排名前50，数据科学也是现在的热门申请专业，申请明年入学数据科学专业的学生又多了一个选择。那么里海大学世界排名多少？里海大学新增数据科学专业怎么样？一起看看吧！
里海大学(Lehigh University)位于美国宾州伯利恒市， 地处华盛顿特区2小时生活圈范围内，工业发达的伯利恒市是宾州的第八大城市。理海大学是爱国者联盟创始成员，该联盟包括美国西点军校、麻省理工学院、美国海军学院和乔治敦大学在内的其他12个精英学府。里海大学多年前在美国便早已与芝加哥大学，西北大学，塔夫斯大学和西点军校等共同作为隐藏的常春藤(Hidden Ivies)之一被人熟知。
2022年USNEWS：49
里海大学新增数据科学专业怎么样？
2022年2月15日，里海大学推出了数据科学硕士：https://engineering.lehigh.edu/news/article/introducing-lehighs-new-ms-data-science-program
里海大学的MSDS将以混合(面对面和远程)的形式进行教学，以适应全日制学生--包括那些想在12个月内完成30个学分的硕士学位的学生--以及已经在职业道路上想兼职学习的专业人士。
而这个开设在工程学院的项目，和里海大学的文理学院、健康学院、商学院和教育学院都有着深度的融合。就读这个项目的学生，在学习到数据科学的方法和手段后，有机会参与来自上述学院的项目制的工作，将所学知识应用在自己感兴趣的领域。
必修课
Introduction to Data Science
The computational analysis of data to extract knowledge and insight. Exploration and manipulation of data. Introduction to data collection and cleaning, reproducibility, code and data management, statistical inference, modeling, ethics, and visualization.
数据科学简介
对数据进行计算分析以提取知识和洞察力。探索和操作数据。介绍数据收集和清理，可重复性，代码和数据管理，统计推理，建模，道德和可视化。
Optimization and Mathematical Foundations for Data Science
This course briefly reviews mathematical structures, linear modeling and matrix computation, and probabilistic thinking and modeling, and covers optimization with an eye towards the algorithms and techniques most commonly used in data analysis.
数据科学的优化和数学基础
本课程简要回顾了数学结构，线性建模和矩阵计算，以及概率思维和建模，并涵盖了优化，着眼于数据分析中常用的算法和技术。
Algorithms and Software Foundations for Data Science
A data scientist needs to study foundational computer science topics and be able to develop software. Topics include discrete structures, algorithm design, programming concepts and data structures, tools and environments, and scaling for big data.
数据科学的算法和软件基础
数据科学家需要研究基础的计算机科学课题，并能够开发软件。主题包括离散结构，算法设计，编程概念和数据结构，工具和环境，以及大数据的扩展。
Data Management for Big Data
Data management here is more than traditional data management and must include (distributed) systems supporting volume and velocity attributed to big data (SQL, NoSQL, Hadoop, Spark, etc.). This also covers data collection, cleaning, provenance, structuring and transforming data.
大数据的数据管理
这里的数据管理比传统的数据管理更多，须包括支持归于大数据的数量和速度的(分布式)系统(SQL, NoSQL, Hadoop, Spark, etc.) 。这也涵盖了数据收集、清理、来源、结构化和转换数据。
Accelerated Computing for Machine Learning
This course provides an introduction to hard- ware architectures and parallel computing systems that facilitate high speed machine learning. This would cover Graphics Processing Unit (GPU) versus Computer Processing Unit (CPU), hardware architecture of parallel computers, memory allocation and data parallelism, multidimensional kernel configuration, kernel-based parallel programming, principles and patterns of parallel algorithms, application of parallel computing to machine learning.
加速机器学习的计算
本课程提供了一个介绍硬软件架构和并行计算系统，促进高速机器学习。这将涵盖图形处理单元(GPU)与计算机处理单元(CPU)，并行计算机的硬件架构，内存分配和数据并行，多维内核配置，基于内核的并行编程，并行算法的原理和模式，并行计算在机器学习的应用。
Introduction to Statistical Modeling
This course introduces statistical analysis of data, linear models, building on the introductory courses. Other topics include exploratory data analysis, graphical data analysis, estimation and hypothesis testing, Bayesian methods, simulation and resampling, linear, multivariate and generalized linear models, algorithmic modeling, clustering, model selection and performance evaluation.
统计建模简介
本课程介绍数据的统计分析，线性模型，建立在入门课程的基础上。其他主题包括探索性数据分析，图形数据分析，估计和假设检验，贝叶斯方法，模拟和再抽样，线性，多变量和广义线性模型，算法建模，聚类，模型选择和性能评估。
Statistical and Machine Learning
Covers common machine learning methods, algorithmic analysis of models for scalability and implementation, data transformations (including dimension reduction, smoothing, aggregation), supervised and unsupervised learning, and ensemble methods.
统计和机器学习
包括常见的机器学习方法，模型的可扩展性和实施的算法分析，数据转换(包括降维，平滑，聚合)，监督和无监督学习，以及集合方法。
Ethics in Data Science
Legal and ethical considerations including privacy, reproducibility, bias, and fairness that are central to data science efforts, as well as ethical principles in information and technology research. This course raises the issues in real-world contexts and develops methods to ameliorate the problems.
数据科学的伦理
法律和伦理方面的考虑，包括隐私，可重复性，偏见和公平，这是数据科学工作的核心，也是信息和技术研究中的伦理原则。本课程在现实世界的背景下提出这些问题，并制定方法来改善这些问题。
选修课
Artificial Intelligence: Theory and Practice
Structural Bioinformatics
Bioinformatics: Issues and Algorithms
Image Processing and Graphics
Natural Language Processing
Semantic Web Topics
WWW Search Engines
Data Mining
Principles of Mobile Computing
Principles and Practice of Parallel Computing
Biomedical Image Computing and Modeling
Introduction to Data Networks
Advanced Computer Architecture
Accelerated Computing for Deep Learning
Machine Learning and Statistical Decision Making
Introduction to Online and Reinforcement Learning
Cryptography and Network Security
Systems Identification
Logistics and Supply Chain Management
Simulation
Time Series Analysis
Design of Experiments
Nonlinear Optimization
Optimization Methods in Machine Learning
Optimization Algorithms and Software
Mining of Large Datasets
以上就是里海大学世界排名多少，里海大学新增数据科学专业怎么样的介绍，希望对您后续申请里海大学数据科学专业有所帮助!若您对里海大学感兴趣，想了解它的其他专业信息，或者申请情况，欢迎咨询我们!
