Overview

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Academic contacts

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Offerings

MURDOCH-S1-EXT-2023-2024
MURDOCH-S1-FACE2FACE-2025-ONGOING
MURDOCH-S1-INT-2023-2024
MURDOCH-S1-ONLINEFLEX-2025-ONGOING
MURDOCH-S2-EXT-2022-2024
MURDOCH-S2-FACE2FACE-2025-ONGOING
MURDOCH-S2-INT-2020-2024
MURDOCH-S2-ONLINEFLEX-2025-ONGOING

Enrolment rules

Students are to be enrolled in Information Technology, or Media and Communications, or Mathematics and Statistics, or MJ-IA, or M1267 Master of Health Administration, Policy and Leadership, or the permission of the Academic Chair to take this unit.

Other learning activities

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Learning activities

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Learning outcomes

1.

Describe and implement fundamental statistics for data analysis.

2.

Describe and implement a range of data analysis methods and predictive modelling techniques that are appropriate to individual datasets.

3.

Describe the basic ideas and techniques underlying modern data science applications.

4.

Implement basic statistics, data pre-processing, visualization, and analysis techniques using the R programming language.

Assessments

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Additional information

Unit content:

· Introduction to data science  · Data pre-processing · Data exploration and visualization  · Introduction to basic statistics and machine learning techniques: regression, classification and clustering · Popular data science applications  · R practices

Other notes:

A minimum of 10 hours per week of personal study for completing workshop activities, reading materials, assignments, private study and revision. Each student is required to complete assignments and an examination.