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  2. Data Analysis and Visualisation [MAT6206]

School: Science

This unit information may be updated and amended immediately prior to semester. To ensure you have the correct outline, please check it again at the beginning of semester.

  • Unit Title

    Data Analysis and Visualisation

  • Unit Code

    MAT6206

  • Year

    2019

  • Enrolment Period

    1

  • Version

    1

  • Credit Points

    20

  • Full Year Unit

    N

  • Mode of Delivery

    On Campus
    Online

  • Unit Coordinator

    Dr Johnny Su Hau LO

Description

This unit introduces students to the principles and practices of machine learning to uncover patterns and trends in complex data sets, and to visualise these patterns in meaningful ways. Machine learning is a process by which computer models are not explicitly programmed but "learn from data". Students will use existing data to develop models used to predict various outcomes for new data.

Learning Outcomes

On completion of this unit students should be able to:

  1. Critically assess the strengths and weaknesses of a range of machine learning methodologies as used in a range of applications.
  2. Select, implement and train appropriate machine learning algorithms for given real-world applications.
  3. Use a range of modern visualisation methods appropriate for different types of data.

Unit Content

  1. Principles of unsupervised and supervised machine learning.
  2. Model selection and feature selection.
  3. Model optimisation: cost functions, search space and other methods.
  4. Model evaluation and visualisation.
  5. Current machine learning methods to analyse and visualise large and complex data sets.

Assessment

GS1 GRADING SCHEMA 1 Used for standard coursework units

Students please note: The marks and grades received by students on assessments may be subject to further moderation. All marks and grades are to be considered provisional until endorsed by the relevant Board of Examiners.

ON CAMPUS
TypeDescriptionValue
Laboratory WorkLaboratory exercises30%
ReportReport on analysis of a real data set30%
ExaminationEnd of semester examination40%
ONLINE
TypeDescriptionValue
Laboratory WorkLaboratory exercises30%
ReportReport on analysis of a real data set30%
ExaminationEnd of semester examination40%

Disability Standards for Education (Commonwealth 2005)

For the purposes of considering a request for Reasonable Adjustments under the Disability Standards for Education (Commonwealth 2005), inherent requirements for this subject are articulated in the Unit Description, Learning Outcomes and Assessment Requirements of this entry. The University is dedicated to provide support to those with special requirements. Further details on the support for students with disabilities or medical conditions can be found at the Access and Inclusion website.

Academic Misconduct

Edith Cowan University has firm rules governing academic misconduct and there are substantial penalties that can be applied to students who are found in breach of these rules. Academic misconduct includes, but is not limited to:

  • plagiarism;
  • unauthorised collaboration;
  • cheating in examinations;
  • theft of other students' work;

Additionally, any material submitted for assessment purposes must be work that has not been submitted previously, by any person, for any other unit at ECU or elsewhere.

The ECU rules and policies governing all academic activities, including misconduct, can be accessed through the ECU website.

MAT6206|1|1

School: Science

This unit information may be updated and amended immediately prior to semester. To ensure you have the correct outline, please check it again at the beginning of semester.

  • Unit Title

    Data Analysis and Visualisation

  • Unit Code

    MAT6206

  • Year

    2019

  • Enrolment Period

    2

  • Version

    1

  • Credit Points

    20

  • Full Year Unit

    N

  • Mode of Delivery

    On Campus
    Online

  • Unit Coordinator

    Dr Johnny Su Hau LO

Description

This unit introduces students to the principles and practices of machine learning to uncover patterns and trends in complex data sets, and to visualise these patterns in meaningful ways. Machine learning is a process by which computer models are not explicitly programmed but "learn from data". Students will use existing data to develop models used to predict various outcomes for new data.

Learning Outcomes

On completion of this unit students should be able to:

  1. Critically assess the strengths and weaknesses of a range of machine learning methodologies as used in a range of applications.
  2. Select, implement and train appropriate machine learning algorithms for given real-world applications.
  3. Use a range of modern visualisation methods appropriate for different types of data.

Unit Content

  1. Principles of unsupervised and supervised machine learning.
  2. Model selection and feature selection.
  3. Model optimisation: cost functions, search space and other methods.
  4. Model evaluation and visualisation.
  5. Current machine learning methods to analyse and visualise large and complex data sets.

Assessment

GS1 GRADING SCHEMA 1 Used for standard coursework units

Students please note: The marks and grades received by students on assessments may be subject to further moderation. All marks and grades are to be considered provisional until endorsed by the relevant Board of Examiners.

ON CAMPUS
TypeDescriptionValue
Laboratory WorkLaboratory exercises30%
ReportReport on analysis of a real data set30%
ExaminationEnd of semester examination40%
ONLINE
TypeDescriptionValue
Laboratory WorkLaboratory exercises30%
ReportReport on analysis of a real data set30%
ExaminationEnd of semester examination40%

Disability Standards for Education (Commonwealth 2005)

For the purposes of considering a request for Reasonable Adjustments under the Disability Standards for Education (Commonwealth 2005), inherent requirements for this subject are articulated in the Unit Description, Learning Outcomes and Assessment Requirements of this entry. The University is dedicated to provide support to those with special requirements. Further details on the support for students with disabilities or medical conditions can be found at the Access and Inclusion website.

Academic Misconduct

Edith Cowan University has firm rules governing academic misconduct and there are substantial penalties that can be applied to students who are found in breach of these rules. Academic misconduct includes, but is not limited to:

  • plagiarism;
  • unauthorised collaboration;
  • cheating in examinations;
  • theft of other students' work;

Additionally, any material submitted for assessment purposes must be work that has not been submitted previously, by any person, for any other unit at ECU or elsewhere.

The ECU rules and policies governing all academic activities, including misconduct, can be accessed through the ECU website.

MAT6206|1|2