香港留学 香港科技大学大数据技术硕士

字典2017-04-28 香港留学香港科技大学大数据技术专业

  香港科技大学,为东亚研究型大学协会、环太平洋大学联盟、亚洲大学联盟、中俄工科大学联盟重要成员,并获AACSB和EQUIS双重认证,是一所亚洲顶尖、国际知名的研究型大学。该校以科技和商业管理为主、人文及社会科学并重,尤以商科和工科见长。大数据技术是一门新兴的科技,广泛运用在各个行业,尤其是商业推广。下面出国留学网小编为你介绍香港科技大学的大数据技术理学硕士课程

  Big Data Technology

  Big data is poised to change the way enterprises function and a society operates, and is changing the way science and engineering is conducted. The MSc program in Big Data Technology jointly offered by the Departments of Computer Science and Engineering and Mathematics integrates different disciplines together to allow students to know all the important aspects of the big data and how it is used in the real world.

  大数据技术

  大数据将改变企业功能和社会运作的方式,也改变了科学和工程学的面貌。大数据技术理学硕士课程由计算机科学部门与工程数学部门的不同学科整合在一起,让学生知道大数据所有的重要方面知识和如何在现实世界中使用它。

  Program Objectives

  The program is aimed at educating students about big data and issues related to big data. The students are expected to be familiar with the workflow of big data systems and social and societal implications of big data systems.

  The program helps to integrate different disciplines together and students in this program will learn the major components of big data:

  big data infrastructure

  big data integration

  big data storage

  big data modeling and management

  big data computing systems big data analytic and mining systems

  big data security, policy and social implications, as well as human factors

  big data applications in various fields (data science)

  课程目标

  该项目旨在教育学生关于大数据和与大数据相关的问题。学生们应该熟悉大数据系统的工作流程和大数据系统的社交和社会影响。

  该计划有助于整合不同学科在一起,学生在这个项目将学习大数据的主要部分:

  大数据基础设施

  大数据集成

  大数据存储

  大数据建模和管理

  大数据分析和挖掘系统

  大数据安全、政策和社会影响,以及人为因素

  大数据在各个领域的应用(数据科学)

  Admission Requirements

  Target Students

  The target students for this MSc are graduates of computer / mathematics-related Bachelor's programs and working professionals in industry, government or other institutions. Fresh graduates who have good academic record are also our targets.

  这个理学硕士课程的目标学生是计算机/数学相关专业学士学位毕业生或者工作在企业,政府,其它机构的在职人员。

  Admission Requirement

  Applicants must possess a bachelor’s degree in Computer Engineering, Computer Science, Mathematics or a related field from a recognized university or tertiary institution. Applicants with a bachelor’s degree in other disciplines must have relevant working experience in IT and Mathematics related fields.

  入学要求

  申请者必须拥有国际公认大学或者高等教育机构的计算机工程,计算机科学,数序或者相关专业的学士学位。其它专业的申请者必须拥有IT和数学相关行业的工作经历

  Medium of Instruction

  All in-class lectures and materials are in English.

  教学语言

  所有课堂讲座和材料都是英文的

  English Requirements

  All in-class lectures and materials are in English. Evidence of English ability is not required from applicants graduated from a university where English is the medium of teaching (includes all universities in Hong Kong and most universities from North America, United Kingdom, Australia, Singapore, New Zealand etc). Applicants from Universities where English is not the medium of instruction, please provide an English proficiency proof in the application. Some recommended scores are as follows:

  英语要求

  所有课堂讲座和材料都是英文的。从英语教学大学毕业的申请者不需要提供英语能力的证明(包括所有在香港的大学和大部分北美,英国澳大利亚新加坡新西兰等国的大学)。从非英语教学大学毕业的申请者需要在申请中提供英语水平证明。一些推荐的分数标准如下:

 

Test of English as a Foreign Language (TOEFL) International English Language Testing System (IELTS)
Paper-based test (PBT) score ≥ 550
Internet-based test (iBT) score ≥ 80
Overall score ≥ 6.0 
All sub-scores ≥ 5.5
HKUST Institution code is 0170 and
the department code is 01
Please mail to Office of Postgraduate Studies (Postgraduate Outreach and Admissions Team)

非英语为母语者的英语能力考试 (托福) 国际英语语言测试系统(雅思)
卷面考试 ≥ 550
网络考试 (新托福) ≥ 80
总分 ≥ 6.0 
每门s ≥ 5.5
香港科技大学代码: 0170 
学院代码 :01
请见官网,咨询研究生办公室
 

  Note: TOEFL and IELTS scores are valid for two years from the test date.

  注意:托福和雅思分数在考完两年内有效。

  Credit Transfer

  Credit transfer may be granted to students in recognition of studies completed elsewhere. Application must be made to the program office within the first term after admission. All credit transfer must be approved by the Program Director and are subject to the normal university, school, and program requirement on credit transfer. A maximum of 9 credits may be transferred.

  学分转移

  学分转移可以通过学生在其它地方的学习认证取得。申请者必须在录取后的第一个学期向项目办公室申请。所有的学分转移都必须经过项目主任的批准并就学分转移要求上报师范大学,学院和项目。最多可以转移9个学分。

  Curriculum

  Students must complete 30 credits of coursework, with 12 credits of core courses and 18 credits of elective courses. Students shall take ten 3-credit taught courses or eight to nine 3-credit taught courses plus independent project(s) offered from the program. Each course listed below carries 3 credits. Subject to the approval from the program director, students may take a maximum of 6 credits of CSIT courses offered by the MSc in Information Technology program as partial fulfillment to meet the graduation requirement of the program.

  课程

  学生必须完成30个学分的课程,包括12学分的核心课程和18学分的选修课程。学生将参与10门3个学分的授课课程或者8到9门3个学分的授课课程加上独立的课题项目。下列每个课程有3个学分。经项目主任批准,学生可以最多参加由信息技术硕士课程提供的6个学分的CSIT课程,作为达到课程毕业要求的补充。

  Core Courses

  MSBD 5001 Foundations of Data Analytics

  MSBD 5002 Data Mining and Knowledge Discovery (Co-Listing with CSIT 5210)

  MSBD 5003 Big Data Computing

  MSBD 5004 Mathematical Methods for Data Analysis

  核心课程

  数据分析基础

  数据挖掘和知识发现

  大数据计算

  数据分析计算法

  Elective Courses

  MSBD 5005 Data Visualization

  MSBD 5006 Quantitative Analysis of Financial Time Series (Co-Listing with MAFS 5130)

  MSBD 5007 Optimization and Matrix Computation

  MSBD 5008 Introduction to Social Computing

  MSBD 5009 Parallel Programming

  MSBD 5010 Image Processing and Analysis

  MSBD 5011 Advanced Statistics: Theory and Applications

  MSBD 5012 Machine Learning

  MSBD 5013 Statistical Prediction

  MSBD 5014 Independent Project

  *Courses are offered subject to needs and availability.

  选修课程

  数据视觉化

  金融时间序列量化分析

  优化和矩阵计算

  社会计算概论

  平行编程

  图像处理和分析

  高级统计:理论和应用

  机器学习

  数据预测

  独立课题项目

  课程根据需要的要求提供。

  Tentative Course Offering List:

  试验型课程列表:

  2016秋季

  MSBD5001

  MSBD5002

  MSBD5005

  MSBD5006

  MSBD5007

  MSBD5009

  MSBD5013

  2017春季

  MSBD5003

  MSBD5004

  MSBD5008

  MSBD5010

  MSBD5011

  MSBD5012

  *Courses are offered subject to needs and availability.

  课程根据需要的要求提供。

  Course Assessment and Graduation Requirements

  Regular attendance of courses is expected. Courses are assessed according to the grading scheme used for postgraduate courses. Students in the program must complete the program with a graduation grade average (GGA) of 2.850 (of a 4-point scale) or above as required for all postgraduate students at HKUST. If a student fails to meet the graduation grade average requirement, the student is required to repeat or substitute the course(s) at a per credit fee.

  课程评定和毕业要求

  定期出席课程。课程评定根据研究生课程分级评定方案做出。该课程的学生必须达到所有香港科技大学学生都需要达到的GGA2.850以上才能毕业。

  Course Description

  课程介

  MSBD 5001 Foundations of Data Analytics [3 credits]

  This course will provide fundamental techniques for data analytics, including data collection, data extraction, data integration and data cleansing. The students will learn how to manage and optimize the analytics value chain, including collecting and extracting the suitable values, selecting the right data processing processes, integrating the data from various resources, data governance, security and privacy for Big Data applications.

  数据分析基础(3学分)

  本课程将提供基础技术数据分析,包括数据收集、数据提取、数据集成和数据清理。学生将学习如何管理和优化价值链分析,包括收集和提取合适的值,选择正确的数据处理流程,整合各种资源的数据,以及数据治理、大数据应用程序的安全性和私密性。

  MSBD 5002 Data Mining and Knowledge Discovery [3 credits]

  [Co-list with CSIT5210] Data mining has recently emerged as a major field of research and applications. Aimed at extracting useful and interesting knowledge from large data repositories such as databases and the Web, data mining integrates techniques from the fields of database, statistics and AI.

  数据挖掘和知识发现(3学分)

  数据挖掘最近成为一个主要的研究和运用领域。它旨在从大量数据存储设备,如库数据库和网络等中,提取有用的和有趣的知识。数据挖掘包括数据库,,统计和人工智能方面的技术。

  MSBD 5003 Big Data Computing [3 credits]

  Big data systems, including Cloud Computing and parallel data processing frameworks, emerge as enabling technologies in managing and mining the massive amount of data across hundreds or even thousands of commodity servers in datacenters. This course exposes students to both the theory and hands-on experience of this new technology. The course will cover the following topics. (1) Basic concepts of Cloud Computing and production Cloud services; (2) MapReduce - the de facto datacenter-scale programming abstraction - and its open source implementation of Hadoop. (3) Apache Spark - a new generation parallel processing framework - and its infrastructure, programming model, cluster deployment, tuning and debugging, as well as a number of specialized data processing systems built on top of Spark.

  大数据计算(3学分)

  大数据系统,包括云计算和并行数据处理框架。现在它已经成为一项支持企业管理储存在数据中心的成百或上千的商品服务数据的技术。本课程使学生能接触到这项新技术的理论和实践经验。课程将涵盖以下主题。(1)云计算的基本概念和生产云服务;(2)MapReduce -数据中心规模的编程抽象和Hadoop的开源实现。(3)Spark工具——新一代并行处理框架及其基础设施、编程模型,集群部署,调优和调试,以及一些建立在Spark之上的专门的数据处理系统。

  MSBD 5004 Mathematical Methods for Data Analysis [3 credits]

  This course will introduce mathematical formulations and computational methods (convex/non-convex optimization) to exploit structures contained in the data. Moreover, specific computational methods (Randomized computational methods) will be explored for big data analysis.

  数据分析运算法(3学分)

  本课程将介绍数学公式和计算方法(凸/非凸优化)利用结构中包含的数据。此外,大数据分析将探索具体计算方法(随机计算方法)。

  MSBD 5005 Data Visualization [3 credits]

  This course will introduce visualization techniques for data from everyday life, social media, business, scientific computing, medical imaging, etc. The topics include human visual system and perception, visual design principles, open- source visualization tools and systems, visualization techniques for CT/MRI data, computational fluid dynamics, graphs and networks, time-series data, text and documents, Twitter data, and spatio-temporal data.

  数据可视化(3学分)

  本课程将介绍可视化技术为数据从日常生活、社交媒体、商业、科学计算、医学成像等主题包括人类视觉系统和感知、视觉设计原则,开放源代码可视化工具和系统,为CT / MRI数据可视化技术,计算流体动力学,图形和网络,时间序列数据,文本和文档,Twitter数据和时空数据。

  MSBD 5006 Quantitative Analysis of Financial Time Series [3 credits]

  [Co-list with MAFS5130] Analysis of asset returns: autocorrelation, predictability and prediction. Volatility models: GARCH-type models, long range dependence. High frequency data analysis: transactions data, duration. Markov switching and threshold models. Multivariate time series: cointegration models and vector GARCH model.

  金融时间序列量化分析(3学分)

  资产回报率分析:自相关、可预测性和预测。波动率模型:GARCH-type模型,长期依赖。马尔可夫切换和阈值模型。多元时间序列:协整模型和向量GARCH模型。

  MSBD 5007 Optimization and Matrix Computation [3 credits]

  The course will introduce basic techniques about optimization, including unconstrained optimization and constrained optimization, and matrix computation, including matrix analysis, linear systems, orthogonalization and least squares and eigenvalue problems.

  优化和矩阵计算

  课程将介绍关于优化的基本技术,包括无约束最优化和约束优化,和矩阵计算,包括矩阵分析、线性系统、正交化和最小二乘和特征值问题。

  MSBD 5008 Introduction to Social Computing [3 credits]

  This course is an introduction to social information network analysis and engineering. Students will learn both mathematical and programming knowledge for analyzing the structures and dynamics of typical social information networks (e.g. Facebook, Twitter, and MSN). They will also learn how social metrics can be used to improve computer system design as people are the networks. It will cover topics such as small world phenomenon; contagion, tipping and influence in networks; models of network formation and evolution; the web graph and PageRank; social graphs and community detection; measuring centrality; greedy routing and navigations in networks; introduction to game theory and strategic behavior; social engineering; and principles of computer system design.

  社会计算概论(3学分)

  本课程介绍社交信息网络的分析和工程。学生将学习数学和编程知识分析典型的社交信息网络的结构和动力学(例如Facebook、Twitter和MSN)。他们还将学习如何使用社交指标根据网络上的个人改善计算机系统设计。它将涵盖的话题,如小世界现象;网络传播,建议,影响;;网络形成和演化的模型;网络图和排名页;社交网和社交小组搜索;测量中心量;贪婪路由和网络导航;介绍博弈论和战略行为;社会工程,计算机系统设计的原则。

  MSBD 5009 Parallel Programming [3 credits]

  Introduction to parallel computer architectures; principles of parallel algorithm design; shared-memory programming models; message passing programming models used for cluster computing; data-parallel programming models for GPUs; case studies of parallel algorithms, systems, and applications; hands-on experience with writing parallel programs for tasks of interest.

  并行编程(3学分)

  介绍并行计算机体系结构、并行算法的设计原则;共享内存编程模型;用于集群计算的消息传递编程模型;对GPU数据并行处理的编程模型;并行算法,系统和应用程序的案例研究;对感兴趣的任务编写并行程序的实践经验。

  MSBD 5010 Image Processing and Analysis [3 credits]

  This course will introduce the basic techniques for image data processing and analysis. Topics include image processing and analysis in spatial and frequency domains, image restoration and compression, image segmentation and registration, morphological image processing, representation and description, feature description, face recognition, iris recognition, fingerprint recognition, image analysis topics, such as medical image analysis.

  图像处理和分析(3学分)

  本课程将介绍基本的图像数据处理和分析的技术。主题包括图像处理和分析在空间和频率域,图像恢复和压缩,图像分割和登记、形态学图像处理、表示和描述、功能描述、人脸识别、虹膜识别、指纹识别、图像分析主题,如医学图像分析。

  MSBD 5011 Advanced Statistics: Theory and Applications [3 credits]

  This course introduces basic statistical principles, methodology and computational tools needed in performing data analysis. The topics of the course include parametric models, sufficiency principles, estimation methods, liner models, quantile estimations, nonparametric curve estimation, resampling methods, statistical computing and hypothesis testing.

  高级统计:理论与应用(3学分)

  本课程介绍了基本的统计原则、方法和执行数据分析所需的计算工具。课程的主题包括参数模型、充分性原则,评估方法,衬管模型,分位数估计,非参数曲线估计,重采样方法、统计计算和假设检验。

  MSBD 5012 Machine Learning [3 credits]

  The course introduces fundamentals of machine learning, including concept learning, evaluating hypotheses. supervised learning, unsupervised learning and reinforcement learning, Bayesian learning, ensemble methods.

  机器学习(3学分)

  介绍了机器学习的基础,包括概念学习、评估假设。监督学习、无监督学习和强化学习贝叶斯学习,整体的方法。

  MSBD 5013 Statistical Prediction [3 credits]

  This course will introduce statistical predication models and algorithms, including regression models, classification, additive models, graphical models and network, model assessment and selection, model inference and model averaging.

  统计预测(3学分)

  本课程将介绍统计预测模型和算法,包括回归模型、分类、添加剂模型,图形模型和网络模型评估和选择、模型推理和平均模型。

  MSBD 5014 Independent Project [3 credits]

  An independent project carried out under the supervision of a faculty member. This course may be repeated for credit.

  独立项目(3学分)

  在学院成员监督下完成的一项独立课题项目。这个课程可以重复进行以获得学分。

  推荐阅读:

  2017年香港科技大学读研费用

  香港科技大学留学条件

  香港科技大学计算机专业世界排名

  想了解更多留学资讯,请访问出国留学网www.liuxue86.com

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