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Current and previous units that I've tutored and/or lectured at Monash University

Semester 1 2022: FIT2094 Databases
Overview: This unit will provide an introduction to the concepts of database design and usage and the related issues of data management. Students will develop skills in planning, designing, and implementing a data model using an enterprise-scale relational database system (Oracle). Methods and techniques will also be presented to populate, retrieve, update and implement integrity features on data in the implemented database system.

Learning outcomes

On successful completion of this unit, you should be able to:
  1. Apply the theories of the relational database model;
  2. Develop a sound relational database design;
  3. Implement a relational database based on a sound database design;
  4. Manage data that meets user requirements, including queries and transactions;
  5. Contrast the differences between non-relational database models and the relational database model.
Semester 1 2022: FIT3171 Databases
Overview: This unit will provide an introduction to the concepts of database design and usage and the related issues of data management. Students will develop skills in planning, designing, and implementing a data model using an enterprise-scale relational database system (Oracle). Methods and techniques will also be presented to populate, retrieve, update and implement integrity features on data in the implemented database system.

Learning outcomes

On successful completion of this unit, you should be able to:
  1. Apply the theories of the relational database model;
  2. Develop a sound relational database design;
  3. Implement a relational database based on a sound database design;
  4. Manage data that meets user requirements, including queries and transactions;
  5. Contrast the differences between non-relational database models and the relational database model;
  6. Develop programming structures within a database backend.
Semester 1 2022: FIT5149 Applied data analysis
Overview: This unit aims to provide students with the necessary analytical and data modeling skills for the roles of a data scientist or business analyst. Students will be introduced to established and contemporary Machine Learning techniques for data analysis and presentation using widely available analysis software. They will look at a number of characteristic problems/data sets and analyse them with appropriate machine learning and statistical algorithms. Those algorithms include regression, classification, clustering and so on. The unit focuses on understanding the analytical problems, machine learning models, and the basic modeling theory. Students will need to interpret the results and the suitability of the algorithms

Learning outcomes

On successful completion of this unit, you should be able to:
  1. Analyse data sets with a range of statistical, graphical and machine-learning tools;
  2. Evaluate the limitations, appropriateness and benefits of data analytics methods for given tasks;
  3. Design solutions to real world problems with data analytics techniques;
  4. Assess the results of an analysis;
  5. Communicate the results of an analysis for both specific and broad audiences.
Semester SSB 2021-2022: FIT3171 Databases
Synopsis: This unit will provide an introduction to the concepts of database design and usage and the related issues of data management. Students will develop skills in planning, designing, and implementing a data model using an enterprise-scale relational database system (Oracle). Methods and techniques will also be presented to populate, retrieve, update and implement integrity features on data in the implemented database system.

Learning outcomes

On successful completion of this unit, you should be able to:
  1. Explain the motivations behind the development of database management systems;
  2. Describe the underlying theoretical basis of the relational database model and apply the theories into practice;
  3. Critically compared the design constructs of object oriented model and relational model design;
  4. Develop a sound database design;
  5. Implement a database based on a sound database design;
  6. Construct queries that meet user requirements;
  7. Develop an application with a database backend;
  8. Use data modelling and database development tools effectively.
Semester 2 2021: FIT2094 Databases
Synopsis: This unit will provide an introduction to the concepts of database design and usage and the related issues of data management. Students will develop skills in planning, designing, and implementing a data model using an enterprise-scale relational database system (Oracle). Methods and techniques will also be presented to populate, retrieve, update and implement integrity features on data in the implemented database system.

Learning outcomes

On successful completion of this unit, you should be able to:
  1. Explain the motivations behind the development of database management systems;
  2. Describe the underlying theoretical basis of the relational database model and apply the theories into practice;
  3. Develop a sound database design;
  4. Develop a database based on a sound database design;
  5. Construct queries that meet user requirements;
  6. Develop a simple web-based interface for a database;
  7. Use data modelling and database development tools effectively.
Semester 2 2021: FIT3171 Databases
Synopsis: This unit will provide an introduction to the concepts of database design and usage and the related issues of data management. Students will develop skills in planning, designing, and implementing a data model using an enterprise-scale relational database system (Oracle). Methods and techniques will also be presented to populate, retrieve, update and implement integrity features on data in the implemented database system.

Learning outcomes

On successful completion of this unit, you should be able to:
  1. Explain the motivations behind the development of database management systems;
  2. Describe the underlying theoretical basis of the relational database model and apply the theories into practice;
  3. Critically compared the design constructs of object oriented model and relational model design;
  4. Develop a sound database design;
  5. Implement a database based on a sound database design;
  6. Construct queries that meet user requirements;
  7. Develop an application with a database backend;
  8. Use data modelling and database development tools effectively.
Semester 2 2021: FIT9132 Introduction to databases
Synopsis: This unit will introduce the concept of data management in an organisation through relational database technology. Theoretical foundation of relational model, analysis and design, implementation of relational database using SQL will be covered.

Learning outcomes

On successful completion of this unit, you should be able to:
  1. Explain the motivations behind the development of database management systems;
  2. Describe the underlying theoretical basis of the relational database model and apply the theories into practice;
  3. Evaluate several design options and construct a database design;
  4. Develop a database based on a sound database design;
  5. Construct queries that meet user requirements;
  6. Contrast the differences between non-relational database models and the relational database model.
MO-TP6 2021: FIT9132 Introduction to databases
Synopsis: This unit will introduce the concept of data management in an organisation through relational database technology. Theoretical foundation of relational model, analysis and design, implementation of relational database using SQL will be covered.

Learning outcomes

On successful completion of this unit, you should be able to:
  1. Explain the motivations behind the development of database management systems;
  2. Describe the underlying theoretical basis of the relational database model and apply the theories into practice;
  3. Evaluate several design options and construct a database design;
  4. Develop a database based on a sound database design;
  5. Construct queries that meet user requirements;
  6. Contrast the differences between non-relational database models and the relational database model.
Semester 1 2021: FIT2094 Databases
Synopsis: This unit will provide an introduction to the concepts of database design and usage and the related issues of data management. Students will develop skills in planning, designing, and implementing a data model using an enterprise-scale relational database system (Oracle). Methods and techniques will also be presented to populate, retrieve, update and implement integrity features on data in the implemented database system.

Learning outcomes

On successful completion of this unit, you should be able to:
  1. Explain the motivations behind the development of database management systems;
  2. Describe the underlying theoretical basis of the relational database model and apply the theories into practice;
  3. Develop a sound database design;
  4. Develop a database based on a sound database design;
  5. Construct queries that meet user requirements;
  6. Develop a simple web-based interface for a database;
  7. Use data modelling and database development tools effectively.
Semester 1 2021: FIT3171 Databases
Synopsis: This unit will provide an introduction to the concepts of database design and usage and the related issues of data management. Students will develop skills in planning, designing, and implementing a data model using an enterprise-scale relational database system (Oracle). Methods and techniques will also be presented to populate, retrieve, update and implement integrity features on data in the implemented database system.

Learning outcomes

On successful completion of this unit, you should be able to:
  1. Explain the motivations behind the development of database management systems;
  2. Describe the underlying theoretical basis of the relational database model and apply the theories into practice;
  3. Critically compared the design constructs of object oriented model and relational model design;
  4. Develop a sound database design;
  5. Implement a database based on a sound database design;
  6. Construct queries that meet user requirements;
  7. Develop an application with a database backend;
  8. Use data modelling and database development tools effectively.
Semester 1 2021: FIT5149 Applied data analysis
Synopsis: This unit aims to provide students with the necessary analytical and data modeling skills for the roles of a data scientist or business analyst. Students will be introduced to established and contemporary Machine Learning techniques for data analysis and presentation using widely available analysis software. They will look at a number of characteristic problems/data sets and analyse them with appropriate machine learning and statistical algorithms. Those algorithms include regression, classification, clustering and so on. The unit focuses on understanding the analytical problems, machine learning models, and the basic modeling theory. Students will need to interpret the results and the suitability of the algorithms.

Learning outcomes

On successful completion of this unit, you should be able to:
  1. Analyse data sets with a range of statistical, graphical and machine-learning tools;
  2. Evaluate the limitations, appropriateness and benefits of data analytics methods for given tasks;
  3. Design solutions to real world problems with data analytics techniques;
  4. Assess the results of an analysis;
  5. Communicate the results of an analysis for both specific and broad audiences.
Semester 1 2021: FIT9132 Introduction to databases
Synopsis: This unit will introduce the concept of data management in an organisation through relational database technology. Theoretical foundation of relational model, analysis and design, implementation of relational database using SQL will be covered.

Learning outcomes

On successful completion of this unit, you should be able to:
  1. Explain the motivations behind the development of database management systems;
  2. Describe the underlying theoretical basis of the relational database model and apply the theories into practice;
  3. Evaluate several design options and construct a database design;
  4. Develop a database based on a sound database design;
  5. Construct queries that meet user requirements;
  6. Contrast the differences between non-relational database models and the relational database model.
MO-TP3 2021: FIT9132 Introduction to databases
Synopsis: This unit will introduce the concept of data management in an organisation through relational database technology. Theoretical foundation of relational model, analysis and design, implementation of relational database using SQL will be covered.

Learning outcomes

On successful completion of this unit, you should be able to:
  1. Explain the motivations behind the development of database management systems;
  2. Describe the underlying theoretical basis of the relational database model and apply the theories into practice;
  3. Evaluate several design options and construct a database design;
  4. Develop a database based on a sound database design;
  5. Construct queries that meet user requirements;
  6. Contrast the differences between non-relational database models and the relational database model.
Semester 2 2020: FIT2094 Databases
Synopsis: This unit will provide an introduction to the concepts of database design and usage and the related issues of data management. Students will develop skills in planning, designing, and implementing a data model using an enterprise-scale relational database system (Oracle). Methods and techniques will also be presented to populate, retrieve, update and implement integrity features on data in the implemented database system.

Learning outcomes

On successful completion of this unit, you should be able to:
  1. Explain the motivations behind the development of database management systems;
  2. Describe the underlying theoretical basis of the relational database model and apply the theories into practice;
  3. Develop a sound database design;
  4. Develop a database based on a sound database design;
  5. Construct queries that meet user requirements;
  6. Develop a simple web-based interface for a database;
  7. Use data modelling and database development tools effectively.
Semester 2 2020: FIT3171 Databases
Synopsis: This unit will provide an introduction to the concepts of database design and usage and the related issues of data management. Students will develop skills in planning, designing, and implementing a data model using an enterprise-scale relational database system (Oracle). Methods and techniques will also be presented to populate, retrieve, update and implement integrity features on data in the implemented database system.

Learning outcomes

On successful completion of this unit, you should be able to:
  1. Explain the motivations behind the development of database management systems;
  2. Describe the underlying theoretical basis of the relational database model and apply the theories into practice;
  3. Critically compared the design constructs of object oriented model and relational model design;
  4. Develop a sound database design;
  5. Implement a database based on a sound database design;
  6. Construct queries that meet user requirements;
  7. Develop an application with a database backend;
  8. Use data modelling and database development tools effectively.
Semester 2 2020: FIT5149 Applied data analysis
Synopsis: This unit aims to provide students with the necessary analytical and data modeling skills for the roles of a data scientist or business analyst. Students will be introduced to established and contemporary Machine Learning techniques for data analysis and presentation using widely available analysis software. They will look at a number of characteristic problems/data sets and analyse them with appropriate machine learning and statistical algorithms. Those algorithms include regression, classification, clustering and so on. The unit focuses on understanding the analytical problems, machine learning models, and the basic modeling theory. Students will need to interpret the results and the suitability of the algorithms.

Learning outcomes

On successful completion of this unit, you should be able to:
  1. Analyse data sets with a range of statistical, graphical and machine-learning tools;
  2. Evaluate the limitations, appropriateness and benefits of data analytics methods for given tasks;
  3. Design solutions to real world problems with data analytics techniques;
  4. Assess the results of an analysis;
  5. Communicate the results of an analysis for both specific and broad audiences.
Semester 2 2020: FIT9132 Introduction to databases
Synopsis: This unit will introduce the concept of data management in an organisation through relational database technology. Theoretical foundation of relational model, analysis and design, implementation of relational database using SQL will be covered.

Learning outcomes

On successful completion of this unit, you should be able to:
  1. Explain the motivations behind the development of database management systems;
  2. Describe the underlying theoretical basis of the relational database model and apply the theories into practice;
  3. Evaluate several design options and construct a database design;
  4. Develop a database based on a sound database design;
  5. Construct queries that meet user requirements;
  6. Contrast the differences between non-relational database models and the relational database model.
MO-TP6 2020: FIT9132 Introduction to databases
Synopsis: This unit will introduce the concept of data management in an organisation through relational database technology. Theoretical foundation of relational model, analysis and design, implementation of relational database using SQL will be covered.

Learning outcomes

On successful completion of this unit, you should be able to:
  1. Explain the motivations behind the development of database management systems;
  2. Describe the underlying theoretical basis of the relational database model and apply the theories into practice;
  3. Evaluate several design options and construct a database design;
  4. Develop a database based on a sound database design;
  5. Construct queries that meet user requirements;
  6. Contrast the differences between non-relational database models and the relational database model.
Semester 1 2020: FIT2094 Databases
Synopsis: This unit will provide an introduction to the concepts of database design and usage and the related issues of data management. Students will develop skills in planning, designing, and implementing a data model using an enterprise-scale relational database system (Oracle). Methods and techniques will also be presented to populate, retrieve, update and implement integrity features on data in the implemented database system.

Learning outcomes

On successful completion of this unit, you should be able to:
  1. Explain the motivations behind the development of database management systems;
  2. Describe the underlying theoretical basis of the relational database model and apply the theories into practice;
  3. Develop a sound database design;
  4. Construct queries that meet user requirements;
  5. Develop a simple web-based interface for a database;
  6. Use data modelling and database development tools effectively.
Semester 1 2020: FIT3171 Databases
Synopsis: This unit will provide an introduction to the concepts of database design and usage and the related issues of data management. Students will develop skills in planning, designing, and implementing a data model using an enterprise-scale relational database system (Oracle). Methods and techniques will also be presented to populate, retrieve, update and implement integrity features on data in the implemented database system.

Learning outcomes

On successful completion of this unit, you should be able to:
  1. Explain the motivations behind the development of database management systems;
  2. Describe the underlying theoretical basis of the relational database model and apply the theories into practice;
  3. Critically compared the design constructs of object oriented model and relational model design;
  4. Develop a sound database design;
  5. Implement a database based on a sound database design;
  6. Construct queries that meet user requirements;
  7. Develop an application with a database backend;
  8. Use data modelling and database development tools effectively.
Semester 1 2020: FIT9132 Introduction to databases
Synopsis: This unit will introduce the concept of data management in an organisation through relational database technology. Theoretical foundation of relational model, analysis and design, implementation of relational database using SQL will be covered.

Learning outcomes

At the completion of this unit, students should be able to:
  1. explain the motivations behind the development of database management systems;
  2. describe the underlying theoretical basis of the relational database model and apply the theories into practice;
  3. evaluate several design options and construct a database design;
  4. develop a database based on a sound database design; construct queries that meet user requirements;
  5. contrast the differences between non-relational database models and the relational database model.
MO-TP3 2020: FIT9132 Introduction to databases
Synopsis: This unit will introduce the concept of data management in an organisation through relational database technology. Theoretical foundation of relational model, analysis and design, implementation of relational database using SQL will be covered.

Semester 1 2020: FIT9137 - Introduction to computer architecture and networks
Synopsis: This unit will introduce students to fundamentals of computer systems, with a focus on modern operating systems and networking technology. It covers: CPU, memory, storage and peripherals; Operating System basics; TCP/IP layered protocols; WAN and LAN networking fundamentals; internetworking and transport protocols; and basic concepts of computer and network security.

Learning outcomes

UpOn successful completion of this unit, you should be able to:
  1. examine and describe computer hardware and software architectures;
  2. explain the three major functions of an operating system (OS), namely, process management, memory management, and file management;
  3. describe the underlying fundamental theories, models and protocols for transmitting data across a network;
  4. describe the functions and architectures of local area networks, wide area networks and the Internet;
  5. identify and describe fundamental concepts of network security including common threats and countermeasures;
  6. evaluate several different design options and formulate a simple network design.
Semester 1 2020: FIT5149 Applied data analysis
Synopsis: This unit aims to provide students with the necessary analytical and data modelling skills for the roles of a data scientist or business analyst. Students will be introduced to established and contemporary Machine Learning techniques for data analysis and presentation using widely available analysis software. They will look at a number of characteristic problems/data sets and analyse them with appropriate machine learning and statistical algorithms implemented in software including R, Python and RapidMiner. Those algorithms include regression, classification, clustering and so on, and the focus is on understanding the problems, models, and use of software, but not in the underlying theory. They will need to interpret the results and the suitability of the algorithms.

Learning outcomes

On successful completion of this unit, you should be able to:
  1. analyse data sets with a range of statistical, graphical and machine-learning tools;
  2. evaluate the limitations, appropriateness and benefits of data analytics methods for given tasks;
  3. design solutions to real world problems with data analytics techniques;
  4. assess the results of an analysis;
  5. communicate the results of an analysis for both specific and broad audiences.
Semester 2 2019: FIT5149 Applied data analysis
Synopsis: This unit aims to provide students with the necessary analytical and data modelling skills for the roles of a data scientist or business analyst. Students will be introduced to established and contemporary Machine Learning techniques for data analysis and presentation using widely available analysis software. They will look at a number of characteristic problems/data sets and analyse them with appropriate machine learning and statistical algorithms implemented in software including R, Python and RapidMiner. Those algorithms include regression, classification, clustering and so on, and the focus is on understanding the problems, models, and use of software, but not in the underlying theory. They will need to interpret the results and the suitability of the algorithms.

Learning outcomes

On successful completion of this unit, you should be able to:
  1. analyse data sets with a range of statistical, graphical and machine-learning tools;
  2. evaluate the limitations, appropriateness and benefits of data analytics methods for given tasks;
  3. design solutions to real world problems with data analytics techniques;
  4. assess the results of an analysis;
  5. communicate the results of an analysis for both specific and broad audiences.
Semester 2 2019: FIT9132 Introduction to databases
Synopsis: This unit will introduce the concept of data management in an organisation through relational database technology. Theoretical foundation of relational model, analysis and design, implementation of relational database using SQL will be covered.

Learning outcomes

At the completion of this unit, students should be able to:
  1. explain the motivations behind the development of database management systems;
  2. describe the underlying theoretical basis of the relational database model and apply the theories into practice;
  3. evaluate several design options and construct a database design;
  4. develop a database based on a sound database design; construct queries that meet user requirements;
  5. contrast the differences between non-relational database models and the relational database model.
MO-TP6 2019: FIT9132 Introduction to databases
Synopsis: This unit will introduce the concept of data management in an organisation through relational database technology. Theoretical foundation of relational model, analysis and design, implementation of relational database using SQL will be covered.

Semester 1 2019: FIT3176 Advanced database design
Synopsis: This unit will introduce advanced concepts in the areas of database design, including extended E/R and its implementation using procedures and packages; data design of unstructured and semi-structured data, like XML and JSON, and its implementations in database systems.

Learning outcomes
 
At the completion of this unit students should be able to:
  1. design a database model from a given scenario, using the Extended Entity Relationship model;
  2. create triggers, procedures and functions to enhance the logic stored in a database;
  3. create XML documents and schemas to represent a given scenario;
  4. implement, and manipulate, XML structure in a database; investigate the use of other semi-structure data, like JSON, in a database.
Semester 1 2019: FIT5149 Applied data analysis
Synopsis: This unit aims to provide students with the necessary analytical and data modelling skills for the roles of a data scientist or business analyst. Students will be introduced to established and contemporary Machine Learning techniques for data analysis and presentation using widely available analysis software. They will look at a number of characteristic problems/data sets and analyse them with appropriate machine learning and statistical algorithms implemented in software including R, Python and RapidMiner. Those algorithms include regression, classification, clustering and so on, and the focus is on understanding the problems, models, and use of software, but not in the underlying theory. They will need to interpret the results and the suitability of the algorithms.

Semester 1 2019: FIT9132 Introduction to databases
Synopsis: This unit will introduce the concept of data management in an organisation through relational database technology. Theoretical foundation of relational model, analysis and design, implementation of relational database using SQL will be covered.

MO-TP3 2019: FIT9132 Introduction to databases
Synopsis: This unit will introduce the concept of data management in an organisation through relational database technology. Theoretical foundation of relational model, analysis and design, implementation of relational database using SQL will be covered.

Semester 2 2018: FIT5149 Applied data analysis
Synopsis: This unit aims to provide students with the necessary analytical and data modelling skills for the roles of a data scientist or business analyst. Students will be introduced to established and contemporary Machine Learning techniques for data analysis and presentation using widely available analysis software. They will look at a number of characteristic problems/data sets and analyse them with appropriate machine learning and statistical algorithms implemented in software including R, Python and RapidMiner. Those algorithms include regression, classification, clustering and so on, and the focus is on understanding the problems, models, and use of software, but not in the underlying theory. They will need to interpret the results and the suitability of the algorithms.

Semester 2 2018: FIT9132 Introduction to databases
Synopsis: This unit will introduce the concept of data management in an organisation through relational database technology. Theoretical foundation of relational model, analysis and design, implementation of relational database using SQL will be covered.

MO-TP6 2018: FIT9132 Introduction to databases
Synopsis: This unit will introduce the concept of data management in an organisation through relational database technology. Theoretical foundation of relational model, analysis and design, implementation of relational database using SQL will be covered.

Semester 2 2018: FIT9135 Data communications
Synopsis: The unit will introduce students to fundamentals of data and computer communications method and techniques. It covers: ISO and TCP/IP layered protocols; physical layer concepts: data transmission methods, signal encoding and digital data communication techniques; data link control protocol, multiplexing methods; WAN and LAN networking fundamentals; internetworking and transport protocols.

Semester 1 2018: FIT3176 Advanced database design
Synopsis: This unit will introduce advanced concepts in the areas of database design, including extended E/R and its implementation using procedures and packages; data design of unstructured and semi-structured data, like XML and JSON, and its implementations in database systems.

Semester 1 2018: FIT9132 Introduction to databases
Synopsis: This unit will introduce the concept of data management in an organisation through relational database technology. Theoretical foundation of relational model, analysis and design, implementation of relational database using SQL will be covered.

MO-TP3 2018: FIT9132 Introduction to databases
Synopsis: This unit will introduce the concept of data management in an organisation through relational database technology. Theoretical foundation of relational model, analysis and design, implementation of relational database using SQL will be covered.

Semester 2 2017: FIT5097 Business intelligence modelling
Synopsis: This unit introduces students to the principles, techniques and applications of computer-based decision support models for business and industry. Topics include: decision trees; linear programming and optimisation; other mathematical programming methods; waiting lines and queues; time series analysis and forecasting; inventory modelling and discrete-event simulation. Models will be built and solved using spreadsheets or other computer applications as appropriate.

Semester 2 2017: FIT5149 Applied data analysis
Synopsis: This unit aims to provide students with the necessary analytical and data modelling skills for the roles of a data scientist or business analyst. Students will be introduced to established and contemporary Machine Learning techniques for data analysis and presentation using widely available analysis software. They will look at a number of characteristic problems/data sets and analyse them with appropriate machine learning and statistical algorithms implemented in software including R, Python and RapidMiner. Those algorithms include regression, classification, clustering and so on, and the focus is on understanding the problems, models, and use of software, but not in the underlying theory. They will need to interpret the results and the suitability of the algorithms.

Semester 2 2017: FIT5197 Modelling for data analysis
Synopsis: This unit explores the statistical modelling foundations that underlie the analytic aspects of Data Science. Motivated by case studies and working through examples, this unit covers the mathematical and statistical basis with an emphasis on using the techniques in practice. It introduces data collection, sampling and quality. It considers analytic tasks such as statistical hypothesis testing and exploratory versus confirmatory analysis. It presents basic probability distributions, random number generation and simulation as well as estimation methods and effects such as maximum likelihood estimators, Monte Carlo estimators, Bayes theorem, bias versus variance and cross validation. Basic information theory and dependence models such as regression and log-linear models are also presented, as well as the role of general modelling such as inference and decision making, and predictive models.

Semester 2 2017: FIT9132 Introduction to databases
Synopsis: This unit will introduce the concept of data management in an organisation through relational database technology. Theoretical foundation of relational model, analysis and design, implementation of relational database using SQL will be covered.

MO-TP6 2017: FIT9132 Introduction to databases
Synopsis: This unit will introduce the concept of data management in an organisation through relational database technology. Theoretical foundation of relational model, analysis and design, implementation of relational database using SQL will be covered.

Semester 2 2017: FIT9135 Data communications
Synopsis: The unit will introduce students to fundamentals of data and computer communications method and techniques. It covers: ISO and TCP/IP layered protocols; physical layer concepts: data transmission methods, signal encoding and digital data communication techniques; data link control protocol, multiplexing methods; WAN and LAN networking fundamentals; internetworking and transport protocols.

Semester 1 2017: FIT2077 Advanced data management
Synopsis: This unit extends the study from FIT1004 Data management. FIT2077 will introduce more advanced concepts in the areas of database design, SQL, query optimisation and the handling of unstructured data (XML) both externally and within a database. The issue of "Big Data" and the role played by BI technologies and data warehouses will be explored.

Semester 1 2017: FIT9132 Introduction to databases
Synopsis: This unit will introduce the concept of data management in an organisation through relational database technology. Theoretical foundation of relational model, analysis and design, implementation of relational database using SQL will be covered.

MO-TP3 2017: FIT9132 Introduction to databases
Synopsis: This unit will introduce the concept of data management in an organisation through relational database technology. Theoretical foundation of relational model, analysis and design, implementation of relational database using SQL will be covered.

Semester 1 2017: FIT9135 Data communications
Synopsis: The unit will introduce students to fundamentals of data and computer communications method and techniques. It covers: ISO and TCP/IP layered protocols; physical layer concepts: data transmission methods, signal encoding and digital data communication techniques; data link control protocol, multiplexing methods; WAN and LAN networking fundamentals; internetworking and transport protocols.

Semester 2 2016: FIT1028 Business information technology and systems
Synopsis: This unit introduces students to the value of information within today's society and the critical role played by information technology to gather, generate, store, process and distribute information. The unit will familiarise students with hardware, operating systems, business-oriented software such as spreadsheets and databases, systems development, decision making, networks, communication, the Internet, e-commerce and recent developments in the World Wide Web. Students will be given the opportunity to develop their own information systems using common tools such as Microsoft Excel, Microsoft Access and Mashup editor tools.

Semester 2 2016: FIT9135 Data communications
Synopsis: The unit will introduce students to fundamentals of data and computer communications method and techniques. It covers: ISO and TCP/IP layered protocols; physical layer concepts: data transmission methods, signal encoding and digital data communication techniques; data link control protocol, multiplexing methods; WAN and LAN networking fundamentals; internetworking and transport protocols.

Semester 1 2016: FIT2077 Advanced data management
Synopsis: This unit extends the study from FIT1004 Data management. FIT2077 will introduce more advanced concepts in the areas of database design, SQL, query optimisation and the handling of unstructured data (XML) both externally and within a database. The issue of "Big Data" and the role played by BI technologies and data warehouses will be explored.

Semester 1 2016: FIT9132 Introduction to databases
Synopsis: This unit will introduce the concept of data management in an organisation through relational database technology. Theoretical foundation of relational model, analysis and design, implementation of relational database using SQL will be covered.

Semester 1 2016: FIT9135 Data communications
Synopsis: The unit will introduce students to fundamentals of data and computer communications method and techniques. It covers: ISO and TCP/IP layered protocols; physical layer concepts: data transmission methods, signal encoding and digital data communication techniques; data link control protocol, multiplexing methods; WAN and LAN networking fundamentals; internetworking and transport protocols.

Semester 2 2015: FIT1028 Business information technology and systems
Synopsis: This unit introduces students to the value of information within today's society and the critical role played by information technology to gather, generate, store, process and distribute information. The unit will familiarise students with hardware, operating systems, business-oriented software such as spreadsheets and databases, systems development, decision making, networks, communication, the Internet, e-commerce and recent developments in the World Wide Web. Students will be given the opportunity to develop their own information systems using common tools such as Microsoft Excel, Microsoft Access and Mashup editor tools.

Semester 2 2015: FIT9132 Introduction to databases
Synopsis: This unit will introduce the concept of data management in an organisation through relational database technology. Theoretical foundation of relational model, analysis and design, implementation of relational database using SQL will be covered.

Semester 2 2015: FIT9135 Data communications
Synopsis: The unit will introduce students to fundamentals of data and computer communications method and techniques. It covers: ISO and TCP/IP layered protocols; physical layer concepts: data transmission methods, signal encoding and digital data communication techniques; data link control protocol, multiplexing methods; WAN and LAN networking fundamentals; internetworking and transport protocols.

Semester 1 2015: FIT1030 Introduction to business information systems
This unit is aimed at providing the students with an overall knowledge of business organisations and their structure. The unit will cover all the steps from business strategy to operational planning and financial systems. The internal processes of a business organisation will be described with an emphasis on how they work together to achieve the financial and physical goals of the business.
Accounting information systems and the systems for the processing and recording of business transactions, inventory, sales, purchasing and financial reporting will be described in detail. Tutorial exercises using commercial software will take students through the operational steps of sales, purchasing and deliveries and then produce the main financial statements for the organisation.
A range of new management concepts and tools such as process oriented organisations, control matrices, and systems theory will be described. The unit will also look at how e-commerce and e-business is used in a modern organisation, and give a brief description of contracts and contract law.

Semester 1 2015: FIT2077 Advanced data management
Synopsis: This unit extends the study from FIT1004 Data management. FIT2077 will introduce more advanced concepts in the areas of database design, SQL, query optimisation and the handling of unstructured data (XML) both externally and within a database. The issue of "Big Data" and the role played by BI technologies and data warehouses will be explored.

Semester 1 2015: FIT9135 Data communications
Synopsis: The unit will introduce students to fundamentals of data and computer communications method and techniques. It covers: ISO and TCP/IP layered protocols; physical layer concepts: data transmission methods, signal encoding and digital data communication techniques; data link control protocol, multiplexing methods; WAN and LAN networking fundamentals; internetworking and transport protocols.

Semester 2 2014: FIT5132 Introduction to databases
Synopsis: This unit will introduce the concept of data management in an organisation through relational database technology. Theoretical foundation of relational model, analysis and design, implementation of relational database using SQL will be covered.

Semester 2 2014: FIT5135 Data communications
Synopsis: The unit will introduce students to fundamentals of data and computer communications method and techniques. It covers: ISO and TCP/IP layered protocols; physical layer concepts: data transmission methods, signal encoding and digital data communication techniques; data link control protocol, multiplexing methods; WAN and LAN networking fundamentals; internetworking and transport protocols.

Semester 1 2014: FIT5047 Intelligent systems
Synopsis: This unit introduces the main problems and approaches to designing intelligent software systems including automated search methods, knowledge representation and reasoning, planning, reasoning under uncertainty, machine learning paradigms, and evolutionary algorithms.

Semester 1 2014: FIT5167 Natural computation for intelligent systems
Synopsis: This unit looks at the development and application of biologically inspired models of computation. We study: basic components of a natural neural systems: synapses, dendrites and neurons and their computational models; fundamental concepts of data and signal encoding and processing; neural network architectures: pattern association networks, auto associative networks, feedforward networks, competitive networks, self organizing networks and recurrent networks; plasticity and learning. Hebb rule, supervised learning, reinforced learning, error-correcting learning, unsupervised learning, competitive learning, self-organization.

Semester 1 2014: FIT5135 Data communications
Synopsis: The unit will introduce students to fundamentals of data and computer communications method and techniques. It covers: ISO and TCP/IP layered protocols; physical layer concepts: data transmission methods, signal encoding and digital data communication techniques; data link control protocol, multiplexing methods; WAN and LAN networking fundamentals; internetworking and transport protocols.

Semester 2 2013: FIT5045 Knowledge discovery and data mining
Synopsis: Modern methods of discovering patterns in large-scale databases are introduced, including classification, clustering and association rules analysis. These are contrasted with more traditional methods of finding information from data, such as data queries. Data pre-processing methods for dealing with noisy and missing data and with dimensionality reduction are reviewed. Hands-on case studies in building data mining models are performed using a popular software package.

Semester 1 2013: FIT5167 Natural computation for intelligent systems
Synopsis: This unit looks at the development and application of biologically inspired models of computation. We study: basic components of a natural neural systems: synapses, dendrites and neurons and their computational models; fundamental concepts of data and signal encoding and processing; neural network architectures: pattern association networks, auto associative networks, feedforward networks, competitive networks, self organizing networks and recurrent networks; plasticity and learning. Hebb rule, supervised learning, reinforced learning, error-correcting learning, unsupervised learning, competitive learning, self-organization.

Semester 1 2012: FIT5167 Natural computation for intelligent systems
Synopsis: This unit looks at the development and application of biologically inspired models of computation. We study: basic components of a natural neural systems: synapses, dendrites and neurons and their computational models; fundamental concepts of data and signal encoding and processing; neural network architectures: pattern association networks, auto associative networks, feedforward networks, competitive networks, self organizing networks and recurrent networks; plasticity and learning. Hebb rule, supervised learning, reinforced learning, error-correcting learning, unsupervised learning, competitive learning, self-organization.

Semester 1 2012: FIT5158 Customer relationship management and data mining
Synopsis: This unit provides an understanding of the business value of customer relationship management and how data mining technology can be used to improve organizational interaction with customers. Building a business around the customer relationship is the aspiration of many modern organizations. Customer relationship management and data mining has been combined together to provide the required concepts, techniques, technology and tools to achieve this goal. The unit discuss how IT and IT based techniques can be used for customer segmentation, clustering and classification, market basket analysis and association rule mining in addition to traditional CRM.

Semester 2 2011: FIT5045 Knowledge discovery and data mining
Synopsis: Modern methods of discovering patterns in large-scale databases are introduced, including classification, clustering and association rules analysis. These are contrasted with more traditional methods of finding information from data, such as data queries. Data pre-processing methods for dealing with noisy and missing data and with dimensionality reduction are reviewed. Hands-on case studies in building data mining models are performed using a popular software package.

Semester 2 2011: FIT5097 Business intelligence modelling
Synopsis: This unit introduces students to the principles, techniques and applications of computer-based decision support models for business and industry. Topics include: decision trees; linear programming and optimisation; other mathematical programming methods; waiting lines and queues; time series analysis and forecasting; inventory modelling and discrete-event simulation. Models will be built and solved using spreadsheets or other computer applications as appropriate.

Semester 1 2011: FIT5167 Natural computation for intelligent systems
Synopsis: This unit looks at the development and application of biologically inspired models of computation. We study: basic components of a natural neural systems: synapses, dendrites and neurons and their computational models; fundamental concepts of data and signal encoding and processing; neural network architectures: pattern association networks, auto associative networks, feedforward networks, competitive networks, self organizing networks and recurrent networks; plasticity and learning. Hebb rule, supervised learning, reinforced learning, error-correcting learning, unsupervised learning, competitive learning, self-organization.

Semester 2 2010: FIT5045 Knowledge discovery and data mining
Synopsis: Modern methods of discovering patterns in large-scale databases are introduced, including classification, clustering and association rules analysis. These are contrasted with more traditional methods of finding information from data, such as data queries. Data pre-processing methods for dealing with noisy and missing data and with dimensionality reduction are reviewed. Hands-on case studies in building data mining models are performed using a popular software package.

Semester 2 2010: FIT3014 Analysis and design of algorithms
Synopsis: This unit provides students with advanced techniques for designing and analysing complex algorithms. In particular, it teaches advanced search strategies, how to select an appropriate search strategy for a given problem, advanced techniques for analysis of algorithmic complexity, dynamic programming, basic statistics to estimate program behaviour, Monte Carlo simulation techniques, and basic notions in computability such as NP completeness.

Semester 1 2010: FIT5167 Natural computation for intelligent systems
Synopsis: This unit looks at the development and application of biologically inspired models of computation. We study: basic components of a natural neural systems: synapses, dendrites and neurons and their computational models; fundamental concepts of data and signal encoding and processing; neural network architectures: pattern association networks, auto associative networks, feedforward networks, competitive networks, self organizing networks and recurrent networks; plasticity and learning. Hebb rule, supervised learning, reinforced learning, error-correcting learning, unsupervised learning, competitive learning, self-organization.

Semester 1 2010: FIT3014 Analysis and design of algorithms
Synopsis: This unit provides students with advanced techniques for designing and analysing complex algorithms. In particular, it teaches advanced search strategies, how to select an appropriate search strategy for a given problem, advanced techniques for analysis of algorithmic complexity, dynamic programming, basic statistics to estimate program behaviour, Monte Carlo simulation techniques, and basic notions in computability such as NP completeness.

Semester 2 2009: FIT5045 Knowledge discovery and data mining
Synopsis: Modern methods of discovering patterns in large-scale databases are introduced, including classification, clustering and association rules analysis. These are contrasted with more traditional methods of finding information from data, such as data queries. Data pre-processing methods for dealing with noisy and missing data and with dimensionality reduction are reviewed. Hands-on case studies in building data mining models are performed using a popular software package.

Semester 2 2009: FIT3014 Analysis and design of algorithms
Synopsis: This unit provides students with advanced techniques for designing and analysing complex algorithms. In particular, it teaches advanced search strategies, how to select an appropriate search strategy for a given problem, advanced techniques for analysis of algorithmic complexity, dynamic programming, basic statistics to estimate program behaviour, Monte Carlo simulation techniques, and basic notions in computability such as NP completeness.

Semester 1 2009: FIT3073 Data Mining
Synopsis: This unit provides an overview of the techniques used to search for knowledge within a data set using both supervised and unsupervised learning. The techniques include Classification, Prediction, Clustering, Association discovery, Time sequence discovery, Sequential pattern discovery, Visualization, Statistical Methods, Decision Trees, Rule based methods, Neural networks, Machine learning, Genetic Algorithms and Fuzzy Systems. Students are able to choose an appropriate technique to suit a particular situation.

Semester 1 2009: FIT3014 Analysis and design of algorithms
Synopsis: This unit provides students with advanced techniques for designing and analysing complex algorithms. In particular, it teaches advanced search strategies, how to select an appropriate search strategy for a given problem, advanced techniques for analysis of algorithmic complexity, dynamic programming, basic statistics to estimate program behaviour, Monte Carlo simulation techniques, and basic notions in computability such as NP completeness.

Semester 2 2008: CSE5230 Data mining
Synopsis: The unit explores various fundamental "data mining" techniques and their application areas. Supporting techniques like data pre-processing and statistics are also covered. Mention is made of the important relationships with each of machine learning, econometrics and inductive inference and with general over-arching techniques such as Minimum Message Length (MML).

Semester 2 2008: FIT3014 Analysis and design of algorithms
Synopsis: This unit provides students with advanced techniques for designing and analysing complex algorithms. In particular, it teaches advanced search strategies, how to select an appropriate search strategy for a given problem, advanced techniques for analysis of algorithmic complexity, dynamic programming, basic statistics to estimate program behaviour, Monte Carlo simulation techniques, and basic notions in computability such as NP completeness.

Semester 1 2008: FIT3014 Analysis and design of algorithms
Synopsis: This unit provides students with advanced techniques for designing and analysing complex algorithms. In particular, it teaches advanced search strategies, how to select an appropriate search strategy for a given problem, advanced techniques for analysis of algorithmic complexity, dynamic programming, basic statistics to estimate program behaviour, Monte Carlo simulation techniques, and basic notions in computability such as NP completeness.

Semester 2 2007: CSE5230 Data mining
Synopsis: The unit explores various fundamental "data mining" techniques and their application areas. Supporting techniques like data pre-processing and statistics are also covered. Mention is made of the important relationships with each of machine learning, econometrics and inductive inference and with general over-arching techniques such as Minimum Message Length (MML).

Semester 2 2006: CSE5230 Data mining
Synopsis: The unit explores various fundamental "data mining" techniques and their application areas. Supporting techniques like data pre-processing and statistics are also covered. Mention is made of the important relationships with each of machine learning, econometrics and inductive inference and with general over-arching techniques such as Minimum Message Length (MML).

Semester 2 2006: CSE2500 Systems security and privacy
Semester 1 2006: CSE3208 Unix programming
Semester 2 2005: CSE3212 Data mining
Semester 2 2005: CSE3001/4151 Unix system programming
Semester 2 2005: CSE2500 Systems security and privacy
Semester 1 2005: CSE3151 Communications network performance
Semester 1 2005: CSE3208 Unix programming
Semester 2 2004: CSE3001/4151 Unix system programming
Semester 1 2003: CPE1003 Web page development

Previous unit that I tutored at The University of Melbourne

Semester 1 2020: COMP90050 Advanced Database Systems
Synopsis: Many applications require access to very large amounts of data. These applications often require reliability (data must not be lost even in the presence of hardware failures), and the ability to retrieve and process the data very efficiently.

The subject will cover the technologies used in advanced database systems. Topics covered will include: transactions, including concurrency, reliability (the ACID properties) and performance; and indexing of both structured and unstructured data. The subject will also cover additional topics such as: uncertain data; Xquery; the Semantic Web and the Resource Description Framework; dataspaces and data provenance; datacentres; and data archiving.

On completion of this subject the student is expected to:
  1. Understand issues related performance and reliability in building applications involving large-scale database systems
  2. Understand Database Technologies used in large-scale applications such as Google search Engines
  3. Understand the concepts and technologies underpinning new forms of Web data
  4. Deep knowledge of transaction processing and recovery from failures and concepts employed in modern database systems
Previous unit that I lectured and tutored at Holmes Institute

Trimester 2 2018: HI5019 Strategic Information Systems for Business and Enterprise
Synopsis: We examine accounting-based systems and business processes, their role in today's business environment, the impacts, methodologies and issues and how they support and fit into business structures. This unit provides students with the concepts relating to the management and organisational use of computer-based information systems. In particular, the unit focuses on the design of accounting-based information systems and their role in providing support for business strategy formulation.

Previous units that I lectured and tutored at Melbourne Institute of Technology

Trimester 2 2014: BE103/BN201/MN501/ME501 Professional Practice
Trimester 1 2014: BN102/BN102D Web Systems
Trimester 1 2014: BN104/BN104D Operating Systems
Trimester 1 2014: BN105/BN105D Information Technology for Users in Organisations
Trimester 1 2014: BE103/BN201/MN501/ME501 Professional Practice
Trimester 2 2013: BN203 Network Security 1
Trimester 2 2013: MN502 Overview of Network Security
Trimester 2 2013: BN204D/BN204 Database Technologies
Trimester 2 2013: BN206 System Administration
Trimester 2 2013: MN506 System Management
Trimester 2 2013: BN208 Networked Applications
Trimester 2 2013: MN504 Networked Application Management
Trimester 1 2013: BN104/BN104D Operating Systems
Trimester 1 2013: MN402 Overview of Operating Systems
Trimester 1 2013: BN105/BN105D Information Technology for Users in Organisations
Trimester 1 2013: BN206 System Administration
Trimester 1 2013: MN506 System Management
Trimester 1 2013: MN501 Network Management in Organisations
Trimester 3 2012: MN402 Overview of Operating Systems
Trimester 3 2012: BN104/BN104D Operating Systems
Trimester 3 2012: MN504 Networked Application Management
Trimester 3 2012: BN208 Networked Applications
Trimester 2 2012: BN102/BN102D Web Systems
Trimester 2 2012: BN105/BN105D Information Technology for Users in Organisations
Trimester 2 2012: MN402 Overview of Operating Systems
Trimester 2 2012: BN104/BN104D Operating Systems
Trimester 2 2012: BN107/BN107D Multimedia Systems
Trimester 2 2012: MN506 System Management
Trimester 2 2012: BN206 System Administration
Trimester 1 2012: BN107/BN107D Multimedia Systems
Trimester 1 2012: MN402 Overview of Operating Systems
Trimester 1 2012: BN104/BN104D Operating Systems

Previous unit that I lectured and tutored at AUSBATAR college

Term 1 2004: ICPMM65DA Create web pages with multimedia
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