选校季第2期丨新加坡国立大学ISS学院专业推荐

【学校介绍】

新加坡国立大学（National University of Singapore），简称国大（NUS），是一所位于新加坡的公立研究型大学，国大共有17所学院，分布在新加坡肯特岗、武吉知马和欧南3大校区，提供跨学科跨院系的广泛课程，在全球设有12所海外学院。

2022年QS世界大学排名第11位

2022年美国U.S.New世界大学排名第29位

2022年英国THE世界大学第21位

2022年中国软科世界大学学术排名第75位

选校季第2期丨新加坡国立大学ISS学院专业推荐

【学院介绍】

系统科学学院(ISS)是新加坡大学下属17个学院之一，成立1981年，学院通过一系列研究生教育课程，职业发展课程，咨询，应用研究以及职业服务等项目，为行业输送数字人才。ISS学院被广泛誉为新加坡“未来技能培训”运动（national Skills Future Movement）的领军机构，引领着数字经济的不断学习和领先地位。ISS采用独具特色的多元化教育机制，设置有软件开发，数据科学，人工智能，网络安全，智能健康，数字政府及数字创新等与行业息息相关的多个关键学科。到目前为止，ISS已经向社会输送了12万名信息通讯与商业人才，服务于6800个企业客户，并有5500名研究生从这里毕业，ISS所有教授课程的专家教授平均从业经验超过20年。

【专业介绍】

【Master of Technology in Enterprise Business Analytics】

企业商业分析技术硕士

课程类型：授课型

所在学院：ISS Institute of Systems Science系统科学研究院

学制：1年

入学时间：7月

申请截止时间：3月

申请费：50新币

学费：52023.40新币-55811.20新币

入学要求：数学、统计学、计量经济学、管理科学、运筹学、科学或工程专业本科以上学历，平均绩点不低于B，雅思至少6.0+/托福85+，有入学考试可以用GRE代替，分数建议320+，2年以上工作经验优先；IT、工程和科学专业人士将是理想的候选人，数学、统计学、计量经济学、管理学、运筹学或是类似专业的相关学位，且学习成绩一直良好可以获得工作经验豁免；

职业前景：

业务分析经理，数据科学家和架构师，业务分析师，优化策略顾问，商业智能和绩效管理顾问，企业智能和绩效管理顾问，企业智能管理师，市场情报分析师，CRM数据分析师，风险分析师，市场分析员，大数据分析等

毕业校友就业企业：

埃森哲咨询公司，创新科技，星展银行，国防科学技术署，德意志银行，富士施乐亚太，惠普，IBM，新加坡资讯通讯发展管理局，新加坡税务局微软，华侨银行，新加坡电信等

毕业起薪（参考）：

分析专业人员的平均起薪取决于学位和学生以前的工作经验，对于没有工作经验的应届毕业生，起薪从4000新币到4500新币不等，有3年以上工作经验的毕业生起薪可达6000新币以上；

Modules 模块

Fundamental - Complete 2 Graduate Certificates

Analytics Project Management and Delivery

Students will be equipped with practice-oriented data analytics skills and knowledge in managing analytics project. Participants will be equipped with essential skillsets to understand analytics processes and best practices, to manage data and resources, to understand structure of analytics solution, to perform data visualisation, to present insights via compelling data storytelling, and to ensure successful implementation of analytics project.

Courses:

lStatistics for Business II

lData Storytelling

lData Management for Analytics

lManaging Business Analytics Projects

Core Analytics Techniques

Students will learn the foundation skills to understand, design and solve analytics problems in the industry involving structured and unstructured data. It is a course which prepares the participants to embark upon the journey to become a data scientist in due course.

Courses:

lData Analytics Process and Best Practices II

lStatistics Bootcamp II

lPredictive Analytics – Insights of Trends and Irregularities

lText Analytics

Specialist - Complete 2 of 4 Graduate Certificates

Customer Analytics

Students will be equipped with the skills to manage the customer data and build analytics solutions for customer relationship management. The course will enable them to apply techniques for targeted customer marketing, to reduce churn, increase customer satisfaction and loyalty and increase profitability.

Courses:

lCustomer Analytics

lAdvanced Customer Analytics

lCampaign Analytics

Big Data Processing

Students will learn various aspects of data engineering while building resilient distributed datasets. Participants will learn to apply key practices, identify multiple data sources appraised against their business value, design the right storage, and implement proper access model(s). Finally, participants will build a scalable data pipeline solution composed of pluggable component architecture, based on the combination of requirements in a vendor/technology agnostic manner. Participants will familiarize themselves on working with Spark platform along with additional focus on query and streaming libraries.

Courses:

lBig Data Engineering for Analytics

lRecommender Systems

lProcessing Big Data for Analytics

Practical Language Processing

Students will be taught advanced skills in practical language processing. This includes fundamental text processing, text analytics, deep learning techniques and their application in sentiment mining and chatbots development.

Courses:

lText Analytics

lNew Media and Sentiment Mining

lText Processing using Machine Learning

lConversational UIs

Advanced Predictive Modelling Techniques

Students who complete this certificate will have skills in advanced predictive, prescriptive & forecasting techniques applicable in the areas of health, government and many other domains. The topics include advanced predictive and forecasting techniques, survival analysis, health analytics, experimental design techniques, econometric forecasting, mathematical optimization methods etc.

Courses:

lComplex Predictive Modelling & Forecasting

lProduct & Pricing Analytics

lAnalytics for Commercial Excellence

【Master of Technology in Intelligent Systems (ISS)】

智能系统技术硕士

课程类型：授课型

所在学院：ISS Institute of Systems Science系统科学研究院

学制：1年

入学时间：7月

申请截止时间：3月

申请费：50新币

学费：55319新币-55208新币

入学要求：理工科学学士学位，平均绩点至少B；雅思至少6.0+/托福85+，有入学考试可以用GRE代替，分数建议320+；2年以上相关工作经验优先，作为IT专业人员，例如软件开发人员、业务分析人员、或是作为领域专家，在可以应用智能系统和知识工程领域工作；具有高度相关的IT学位，并通过课程学习、课程项目或是专业IT认证获得了良好的学习成绩和良好的实际软件开发知识，可以授予工作经验豁免。

职业前景：

人工智能，机器学习，智能系统，机器人系统开发人员，自动驾驶汽车系统开发，视觉和传感系统开发人员，人工智能业务系统开发者，智能过程自动化开发人员，智能医疗系统开发，智慧城市应用开发，语言系统工程师，文本挖掘/分析专家，大数据开发，游戏开发等

校友就业企业：

埃森哲咨询公司，创新科技，星展银行，德意志银行，惠普，IBM，微软等

Modules 模块

Fundamental - Complete 2 Graduate Certificates

Intelligent Reasoning Systems

Students will be taught how to build Intelligent Systems that solve problems by computational reasoning using captured domain knowledge and data. Example applications include, question answering systems such as IBM's Watson, personal assistants such as Amazon’s Alexa Skills and game-playing systems such as Google's AlphaGo

Courses:

lMachine Reasoning

lCognitive Systems

lReasoning Systems

Pattern Recognition Systems

Students will be taught how to design and build systems that make decisions by recognising complex patterns in data. Examples are robotic systems and smart city applications that take as input diverse sensor data streams. These systems will utilise the latest pattern recognition, machine learning and sensor signal processing techniques.

Courses:

lProblem Solving using Pattern Recognition

lIntelligent Sensing and Sense Making

lPattern Recognition and Machine Learning Systems

Specialist Modules - Complete 2 Graduate Certificates selected from 4

Intelligent Robotic Systems

Students will be taught the skills required to build Intelligent Systems that will help control the advanced robotic systems, autonomous vehicles and industrial automation that will be central to Industry 4.0.

Courses:

lRobotic Systems

lDeveloping Autonomous Robots & Vehicles

lHuman-Robot System Engineering

Intelligent Sensing Systems

Students will be taught the skills and techniques required to build Intelligent Sensing Systems that are able to make decisions based on visual and audio sensory signals, including human speech. Example systems include crowd monitoring, facial recognition, medical sensing, robot and vehicle control.

Courses:

lVision Systems

lSpatial Reasoning from Sensor Data

lReal Time Audio-Visual Sensing and Sense Making

Intelligent Software Agents

Students will be taught how to build intelligent software agents that can act on behalf of, and replicate the actions of, humans in commercial and business transactions as well as automate business processes. Example systems include intelligent personal assistants, intelligent shopping agents as well as intelligent agents performing robotic process automation.

Courses:

lIntelligent Process Automation

lRPA and IPA - Strategy and Management

lSoftware Robots - Best Practices

lSelf-Learning Systems

Practical Language Processing

Courses:

lText Analytics

lNew Media and Sentiment Mining

lText Processing using Machine Learning

lService Chatbots

【Master of Technology in Software Engineering】

软件工程硕士

课程类型：授课型

所在学院：ISS Institute of Systems Science系统科学研究院

学制：1年

入学时间：3月

申请截止时间：9月

申请费：50新币

学费：44512新币-48781.3新币

入学要求：理工科学士学位，平均绩点至少为B；雅思至少6.0+/托福85+，有入学考试可以用GRE代替，分数建议320+，；两年以上软件工程师相关工作经验（如程序员，设计师，技术团队领导）；精通以下领域（软件开发生命周期，包括敏捷软件开发方法，如Scrum。软件开发使用一种或是多种现代编程语言，软件设计包括使用设计模式，软件测试和测试驱动开发）

职业前景：软件架构师（通用，智能系统，数据），高级软件工程师，数据架构师，产品经理等

Modules 模块

Fundamental - Complete 1 Graduate Certificate

Architecting Scalable Systems

Students will learn how to architect scalable, robust and reliable ubiquitous systems using the latest Cloud-based technology. Techniques to automate and engineer DevOps pipelines and architecting platforms will also be covered. Students will also focus on how to architect the back-end support for large systems and platforms.

Courses:

lArchitecting Software Solutions

lPlatform Engineering

lDevOps Engineering and Automation

lCloud Native Solution Design

Specialist - Complete 2 of 4 Graduate Certificates

Architecting Smart Systems

Students will learn skills and techniques required to engineer end-to-end Intelligent Smart Systems. Topics in architecting smart IoT platforms and systems that are scalable will be covered. Students will learn to design, develop and integrate systems that make sense of data from a variety of sensors and edge devices. Students will also learn to create interfaces to smart systems that are apt for interacting with humans in intelligent manners.

Courses:

lArchitecting IoT Solutions

lDesigning Intelligent Edge Computing

lHumanizing Smart Systems

Designing and Managing Products and Platforms

Students will learn how to design and manage software products and platforms. The key components include using design thinking principles and market research to innovate and concretize product ideas; a framework to scaffold the multidisciplinary aspects of managing a product; develop a product strategy that aligns with business goals and to architect a platform business model from first principles. Students can expect a hands-on approach, engaging class dialogues, lectures and offline study. Valuable insights will be shared by industry practitioners.

Courses:

lService Design

lManaging Digital Products

lDigital Product Strategy

lArchitecting Platforms as a Business

Engineering Big Data

Students will learn various aspects of data engineering and processes required for building resilient distributed datasets. Students will also learn to apply key practices, identify multiple data sources appraised against their business value, design the right data storage model(s), and implement fitting data access patterns. Finally, Students will build a scalable data pipeline composed of pluggable functional compute components based on the business insight requirements in a vendor/technology agnostic manner. Students will work with Spark and Hadoop framework along with detailed focus on graph, ML, query and streaming libraries.

Courses:

lInformation Architecture for Data-driven Insights

lBig Data Engineering for Analytics

lArchitecting Systems for Real-Time data processing

Securing Ubiquitous Systems

Students will be equipped with skills to design and manage cyber security for ubiquitous systems that need to be highly secure . Students will learn about cyber security and its application in securing mobile systems and software platforms. Students will also learn how to incorporate security during the software development lifecycle.

Courses:

l(ISC)2 CISSP CBK Training Seminar

lSecure Software Development Lifecycle for Agile

lDesign Secure Mobile Architecture

lPlatform Security

【申请建议】