【學校介紹】
新加坡國立大學（National University of Singapore），簡稱國大（NUS），是一所位于新加坡的公立研究型大學，國大共有17所學院，分布在新加坡肯特崗、武吉知馬和歐南3大校區，提供跨學科跨院系的廣泛課程，在全球設有12所海外學院。
2022年QS世界大學排名第11位
2022年美國U.S.New世界大學排名第29位
2022年英國THE世界大學第21位
2022年中國軟科世界大學學術排名第75位
【學院介紹】
系統科學學院(ISS)是新加坡大學下屬17個學院之一，成立1981年，學院通過一系列研究生教育課程，職業發展課程，咨詢，應用研究以及職業服務等項目，爲行業輸送數字人才。ISS學院被廣泛譽爲新加坡“未來技能培訓”運動（national Skills Future Movement）的領軍機構，引領著數字經濟的不斷學習和領先地位。ISS采用獨具特色的多元化教育機制，設置有軟件開發，數據科學，人工智能，網絡安全，智能健康，數字政府及數字創新等與行業息息相關的多個關鍵學科。到目前爲止，ISS已經向社會輸送了12萬名信息通訊與商業人才，服務于6800個企業客戶，並有5500名研究生從這裏畢業，ISS所有教授課程的專家教授平均從業經驗超過20年。
【專業介紹】
【Master of Technology in Enterprise Business Analytics】
企業商業分析技術碩士
課程類型：授課型
所在學院：ISS Institute of Systems Science系統科學研究院
學制：1年
入學時間：7月
申請截止時間：3月
申請費：50新幣
學費：52023.40新幣-55811.20新幣
入學要求：數學、統計學、計量經濟學、管理科學、運籌學、科學或工程專業本科以上學曆，平均績點不低于B，雅思至少6.0+/托福85+，有入學考試可以用GRE代替，分數建議320+，2年以上工作經驗優先；IT、工程和科學專業人士將是理想的候選人，數學、統計學、計量經濟學、管理學、運籌學或是類似專業的相關學位，且學習成績一直良好可以獲得工作經驗豁免；
職業前景：
業務分析經理，數據科學家和架構師，業務分析師，優化策略顧問，商業智能和績效管理顧問，企業智能和績效管理顧問，企業智能管理師，市場情報分析師，CRM數據分析師，風險分析師，市場分析員，大數據分析等
畢業校友就業企業：
埃森哲咨詢公司，創新科技，星展銀行，國防科學技術署，德意志銀行，富士施樂亞太，惠普，IBM，新加坡資訊通訊發展管理局，新加坡稅務局微軟，華僑銀行，新加坡電信等
畢業起薪（參考）：
分析專業人員的平均起薪取決于學位和學生以前的工作經驗，對于沒有工作經驗的應屆畢業生，起薪從4000新幣到4500新幣不等，有3年以上工作經驗的畢業生起薪可達6000新幣以上；
Modules 模塊
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)】
智能系統技術碩士
課程類型：授課型
所在學院：ISS Institute of Systems Science系統科學研究院
學制：1年
入學時間：7月
申請截止時間：3月
申請費：50新幣
學費：55319新幣-55208新幣
入學要求：理工科學學士學位，平均績點至少B；雅思至少6.0+/托福85+，有入學考試可以用GRE代替，分數建議320+；2年以上相關工作經驗優先，作爲IT專業人員，例如軟件開發人員、業務分析人員、或是作爲領域專家，在可以應用智能系統和知識工程領域工作；具有高度相關的IT學位，並通過課程學習、課程項目或是專業IT認證獲得了良好的學習成績和良好的實際軟件開發知識，可以授予工作經驗豁免。
職業前景：
人工智能，機器學習，智能系統，機器人系統開發人員，自動駕駛汽車系統開發，視覺和傳感系統開發人員，人工智能業務系統開發者，智能過程自動化開發人員，智能醫療系統開發，智慧城市應用開發，語言系統工程師，文本挖掘/分析專家，大數據開發，遊戲開發等
校友就業企業：
埃森哲咨詢公司，創新科技，星展銀行，德意志銀行，惠普，IBM，微軟等
Modules 模塊
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
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
lService Chatbots
【Master of Technology in Software Engineering】
軟件工程碩士
課程類型：授課型
所在學院：ISS Institute of Systems Science系統科學研究院
學制：1年
入學時間：3月
申請截止時間：9月
申請費：50新幣
學費：44512新幣-48781.3新幣
入學要求：理工科學士學位，平均績點至少爲B；雅思至少6.0+/托福85+，有入學考試可以用GRE代替，分數建議320+，；兩年以上軟件工程師相關工作經驗（如程序員，設計師，技術團隊領導）；精通以下領域（軟件開發生命周期，包括敏捷軟件開發方法，如Scrum。軟件開發使用一種或是多種現代編程語言，軟件設計包括使用設計模式，軟件測試和測試驅動開發）
職業前景：軟件架構師（通用，智能系統，數據），高級軟件工程師，數據架構師，産品經理等
Modules 模塊
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
【申請建議】
- 院校背景建議是211/985，均分80+，若是雙非院校背景，均分建議83+
- 雅思成績至少6.0+，建議最好6.5+會比較有申請優勢
- 對于畢業生有2年以上相關工作經驗申請者比較友好