Search Course Outline
Showing 25 course outlines from 2938 matches
651
COMPSCI 732
: Software Tools and Techniques2020 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
Prerequisite: Approval of the Academic Head or nominee
652
COMPSCI 734
: Web, Mobile and Enterprise Computing2021 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.
Prerequisite: Approval of the Academic Head or nominee
653
COMPSCI 734
: Web, Mobile and Enterprise Computing2020 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.
Prerequisite: Approval of the Academic Head or nominee
654
COMPSCI 742
: Advanced Internet: Global Data Communications2023 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.
Prerequisite: COMPSCI 314 or 315
655
COMPSCI 742
: Advanced Internet: Global Data Communications2022 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.
Prerequisite: Approval of the Academic Head or nominee
656
COMPSCI 742
: Advanced Internet: Global Data Communications2021 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.
Prerequisite: Approval of the Academic Head or nominee
657
COMPSCI 742
: Advanced Internet: Global Data Communications2020 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.
Prerequisite: Approval of the Academic Head or nominee
658
COMPSCI 747
: Computing Education2023 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
Prerequisite: Approval of the Academic Head or nominee
659
COMPSCI 747
: Computing Education2022 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
Prerequisite: Approval of the Academic Head or nominee
660
COMPSCI 747
: Computing Education2021 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
Prerequisite: Approval of the Academic Head or nominee
661
COMPSCI 750
: Computational Complexity2023 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.
Prerequisite: Approval of the Academic Head or nominee
662
COMPSCI 750
: Computational Complexity2022 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.
Prerequisite: Approval of the Academic Head or nominee
663
COMPSCI 750
: Computational Complexity2021 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.
Prerequisite: Approval of the Academic Head or nominee
664
COMPSCI 750
: Computational Complexity2020 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.
Prerequisite: Approval of the Academic Head or nominee
665
COMPSCI 751
: Advanced Topics in Database Systems2023 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.
Prerequisite: Approval of the Academic Head or nominee
Restriction: COMPSCI 351, SOFTENG 351
Restriction: COMPSCI 351, SOFTENG 351
666
COMPSCI 751
: Advanced Topics in Database Systems2022 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.
Prerequisite: Approval of the Academic Head or nominee
Restriction: COMPSCI 351, SOFTENG 351
Restriction: COMPSCI 351, SOFTENG 351
667
COMPSCI 751
: Advanced Topics in Database Systems2021 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.
Prerequisite: Approval of the Academic Head or nominee
Restriction: COMPSCI 351, SOFTENG 351
Restriction: COMPSCI 351, SOFTENG 351
668
COMPSCI 752
: Big Data Management2023 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.
No pre-requisites or restrictions
669
COMPSCI 752
: Big Data Management2022 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.
Prerequisite: Approval of the Academic Head or nominee
670
COMPSCI 752
: Big Data Management2021 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.
Prerequisite: Approval of the Academic Head or nominee
671
COMPSCI 752
: Big Data Management2020 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.
Prerequisite: Approval of the Academic Head or nominee
672
COMPSCI 753
: Algorithms for Massive Data2023 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
Prerequisite: Approval of the Academic Head or nominee
673
COMPSCI 753
: Algorithms for Massive Data2022 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
Prerequisite: Approval of the Academic Head or nominee
674
COMPSCI 753
: Algorithms for Massive Data2021 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
Prerequisite: Approval of the Academic Head or nominee
675
COMPSCI 753
: Uncertainty in Data2020 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
Prerequisite: Approval of the Academic Head or nominee
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