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Showing 25 course outlines from 2938 matches

651

COMPSCI 732

: Software Tools and Techniques
2020 Semester One (1203)
An advanced course examining research issues related to tools and techniques for software design and development. Topics include: techniques for data mapping and data integration, software architectures for developing software tools, issues in advanced database systems. Recommended preparation: COMPSCI 331
Subject: Computer Science
Prerequisite: Approval of the Academic Head or nominee
652

COMPSCI 734

: Web, Mobile and Enterprise Computing
2021 Semester One (1213)
Examines advanced and emerging software architectures at the confluence of XML, web services, distributed systems, and databases. Includes advanced topics in areas such as: mobile computing, remoting, web services for enterprise integration, workflow orchestrations for the enterprise, peer-to-peer computing, grid computing. Recommended preparation: COMPSCI 335.
Subject: Computer Science
Prerequisite: Approval of the Academic Head or nominee
653

COMPSCI 734

: Web, Mobile and Enterprise Computing
2020 Semester One (1203)
Examines advanced and emerging software architectures at the confluence of XML, web services, distributed systems, and databases. Includes advanced topics in areas such as: mobile computing, remoting, web services for enterprise integration, workflow orchestrations for the enterprise, peer-to-peer computing, grid computing. Recommended preparation: COMPSCI 335.
Subject: Computer Science
Prerequisite: Approval of the Academic Head or nominee
654

COMPSCI 742

: Advanced Internet: Global Data Communications
2023 Semester Two (1235)
The course covers wide area networks, global routing, network and protocol performance, buffering and queuing, advanced network measurement, network application performance, content networks, and advanced networking concepts.
Subject: Computer Science
Prerequisite: COMPSCI 314 or 315
655

COMPSCI 742

: Advanced Internet: Global Data Communications
2022 Semester Two (1225)
The protocols and performance of local area networks. The special requirements of very high speed networks (100 Mb/s and higher). Asynchronous transfer mode (ATM) and its relation to other protocols. The TCP/IP suite. Recommended preparation: COMPSCI 314, 315.
Subject: Computer Science
Prerequisite: Approval of the Academic Head or nominee
656

COMPSCI 742

: Advanced Internet: Global Data Communications
2021 Semester Two (1215)
The protocols and performance of local area networks. The special requirements of very high speed networks (100 Mb/s and higher). Asynchronous transfer mode (ATM) and its relation to other protocols. The TCP/IP suite. Recommended preparation: COMPSCI 314, 315.
Subject: Computer Science
Prerequisite: Approval of the Academic Head or nominee
657

COMPSCI 742

: Advanced Internet: Global Data Communications
2020 Semester Two (1205)
The protocols and performance of local area networks. The special requirements of very high speed networks (100 Mb/s and higher). Asynchronous transfer mode (ATM) and its relation to other protocols. The TCP/IP suite. Recommended preparation: COMPSCI 314, 315.
Subject: Computer Science
Prerequisite: Approval of the Academic Head or nominee
658

COMPSCI 747

: Computing Education
2023 Semester One (1233)
An overview of topics related to the use of technology in education and how people learn computer science concepts. Topics include research methodologies used in computer science education, how novices learn to program, and how technology can engage students in active learning, facilitate collaboration and enhance traditional educational practice. Recommended preparation: 30 points at Stage III in Computer Science
Subject: Computer Science
Prerequisite: Approval of the Academic Head or nominee
659

COMPSCI 747

: Computing Education
2022 Semester One (1223)
An overview of topics related to the use of technology in education and how people learn computer science concepts. Topics include research methodologies used in computer science education, how novices learn to program, and how technology can engage students in active learning, facilitate collaboration and enhance traditional educational practice. Recommended preparation: 30 points at Stage III in Computer Science
Subject: Computer Science
Prerequisite: Approval of the Academic Head or nominee
660

COMPSCI 747

: Computing Education
2021 Semester One (1213)
An overview of topics related to the use of technology in education and how people learn computer science concepts. Topics include research methodologies used in computer science education, how novices learn to program, and how technology can engage students in active learning, facilitate collaboration and enhance traditional educational practice. Recommended preparation: 30 points at Stage III in Computer Science
Subject: Computer Science
Prerequisite: Approval of the Academic Head or nominee
661

COMPSCI 750

: Computational Complexity
2023 Semester Two (1235)
Definitions of computational models and complexity classes: time complexity (e.g., P and NP), space complexity (e.g., L and PSPACE), circuit and parallel complexity (NC), polynomial-time hierarchy (PH), interactive complexity (IP), probabilistic complexity (BPP), and fixed-parameter complexity. Recommended preparation: COMPSCI 320 or 350.
Subject: Computer Science
Prerequisite: Approval of the Academic Head or nominee
662

COMPSCI 750

: Computational Complexity
2022 Semester Two (1225)
Definitions of computational models and complexity classes: time complexity (e.g., P and NP), space complexity (e.g., L and PSPACE), circuit and parallel complexity (NC), polynomial-time hierarchy (PH), interactive complexity (IP), probabilistic complexity (BPP), and fixed-parameter complexity. Recommended preparation: COMPSCI 320 or 350.
Subject: Computer Science
Prerequisite: Approval of the Academic Head or nominee
663

COMPSCI 750

: Computational Complexity
2021 Semester Two (1215)
Definitions of computational models and complexity classes: time complexity (e.g., P and NP), space complexity (e.g., L and PSPACE), circuit and parallel complexity (NC), polynomial-time hierarchy (PH), interactive complexity (IP), probabilistic complexity (BPP), and fixed-parameter complexity. Recommended preparation: COMPSCI 320 or 350.
Subject: Computer Science
Prerequisite: Approval of the Academic Head or nominee
664

COMPSCI 750

: Computational Complexity
2020 Semester Two (1205)
Definitions of computational models and complexity classes: time complexity (e.g., P and NP), space complexity (e.g., L and PSPACE), circuit and parallel complexity (NC), polynomial-time hierarchy (PH), interactive complexity (IP), probabilistic complexity (BPP), and fixed-parameter complexity. Recommended preparation: COMPSCI 320 or 350.
Subject: Computer Science
Prerequisite: Approval of the Academic Head or nominee
665

COMPSCI 751

: Advanced Topics in Database Systems
2023 Semester One (1233)
Database principles. Relational model, relational algebra, relational calculus, SQL, SQL and programming languages, entity-relationship model, normalisation, query processing and query optimisation, ACID transactions, transaction isolation levels, database recovery, database security, databases and XML. Research frontiers in database systems. Recommended preparation: COMPSCI 220, 225.
Subject: Computer Science
Prerequisite: Approval of the Academic Head or nominee
Restriction: COMPSCI 351, SOFTENG 351
666

COMPSCI 751

: Advanced Topics in Database Systems
2022 Semester One (1223)
Database principles. Relational model, relational algebra, relational calculus, SQL, SQL and programming languages, entity-relationship model, normalisation, query processing and query optimisation, ACID transactions, transaction isolation levels, database recovery, database security, databases and XML. Research frontiers in database systems. Recommended preparation: COMPSCI 220, 225.
Subject: Computer Science
Prerequisite: Approval of the Academic Head or nominee
Restriction: COMPSCI 351, SOFTENG 351
667

COMPSCI 751

: Advanced Topics in Database Systems
2021 Semester One (1213)
Database principles. Relational model, relational algebra, relational calculus, SQL, SQL and programming languages, entity-relationship model, normalisation, query processing and query optimisation, ACID transactions, transaction isolation levels, database recovery, database security, databases and XML. Research frontiers in database systems. Recommended preparation: COMPSCI 220, 225.
Subject: Computer Science
Prerequisite: Approval of the Academic Head or nominee
Restriction: COMPSCI 351, SOFTENG 351
668

COMPSCI 752

: Big Data Management
2023 Semester One (1233)
The deep diversity of modern-day data from many companies requires data scientists to master many technologies that rely on new principles to represent, describe, access, and analyse data. The course will provide insight into the rich landscape of big data modelling, management, and analysis in distributed and heterogeneous environments. Recommended preparation: COMPSCI 351.
Subject: Computer Science
No pre-requisites or restrictions
669

COMPSCI 752

: Big Data Management
2022 Semester One (1223)
The deep diversity of modern-day data from many companies requires data scientists to master many technologies that rely on new principles to represent, describe, access, and analyse data. The course will provide insight into the rich landscape of big data modelling, management, and analysis in distributed and heterogeneous environments. Recommended preparation: COMPSCI 351.
Subject: Computer Science
Prerequisite: Approval of the Academic Head or nominee
670

COMPSCI 752

: Big Data Management
2021 Semester One (1213)
Big data modelling and management in distributed and heterogeneous environments. Sample topics include: representation languages for data exchange and integration (XML and RDF), languages for describing the semantics of big data (DTDs, XML Schema, RDF Schema, OWL, description logics), query languages for big data (XPath, XQuery, SPARQL), data integration (Mediation via global-as-view and local-as-view), large-scale search (keyword queries, inverted index, PageRank) and distributed computing (Hadoop, MapReduce, Pig), big data and blockchain technology (SPARK, cryptocurrency). Recommended preparation: COMPSCI 351 or equivalent.
Subject: Computer Science
Prerequisite: Approval of the Academic Head or nominee
671

COMPSCI 752

: Big Data Management
2020 Semester One (1203)
Big data modelling and management in distributed and heterogeneous environments. Sample topics include: representation languages for data exchange and integration (XML and RDF), languages for describing the semantics of big data (DTDs, XML Schema, RDF Schema, OWL, description logics), query languages for big data (XPath, XQuery, SPARQL), data integration (Mediation via global-as-view and local-as-vie), large-scale search (keyword queries, inverted index, PageRank) and distributed computing (Hadoop, MapReduce, Pig), big data and blockchain technology (SPARK, cryptocurrency). Recommended preparation: COMPSCI 351 or equivalent.
Subject: Computer Science
Prerequisite: Approval of the Academic Head or nominee
672

COMPSCI 753

: Algorithms for Massive Data
2023 Semester Two (1235)
Modern enterprises and applications such as electronic commerce, social networks, location services, and scientific databases are generating data on a massive scale. Analysis of such data must be carried out by scalable algorithms. This course exposes data science practitioners and researchers to various advanced algorithms for processing and mining massive data, and explores best-practices and state-of-the-art developments in big data. Recommended preparation: COMPSCI 320
Subject: Computer Science
Prerequisite: Approval of the Academic Head or nominee
673

COMPSCI 753

: Algorithms for Massive Data
2022 Semester Two (1225)
Modern enterprises and applications such as electronic commerce, social networks, location services, and scientific databases are generating data on a massive scale. Analysis of such data must be carried out by scalable algorithms. This course exposes data science practitioners and researchers to various advanced algorithms for processing and mining massive data, and explores best-practices and state-of-the-art developments in big data. Recommended preparation: COMPSCI 320
Subject: Computer Science
Prerequisite: Approval of the Academic Head or nominee
674

COMPSCI 753

: Algorithms for Massive Data
2021 Semester Two (1215)
Modern enterprises and applications such as electronic commerce, social networks, location services, and scientific databases are generating data on a massive scale. Analysis of such data must be carried out by scalable algorithms. This course exposes data science practitioners and researchers to various advanced algorithms for processing and mining massive data, and explores best-practices and state-of-the-art developments in big data. Recommended preparation: COMPSCI 320
Subject: Computer Science
Prerequisite: Approval of the Academic Head or nominee
675

COMPSCI 753

: Uncertainty in Data
2020 Semester Two (1205)
Modern applications such as electronic commerce, social networks, and location services are expecting efficient big data solutions. This course exposes practitioners to challenging problems in managing and mining big data. It introduces a wide spectrum of advanced techniques that underpin big data processing. Best-practices and current developments in big data research are also explored. Recommended preparation: COMPSCI 351
Subject: Computer Science
Prerequisite: Approval of the Academic Head or nominee