Overview

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Academic contacts

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Offerings

DUBAI-ISC-TMD-INT-2023-2023
DUBAI-ISC-TSD-INT-2022-2022
MURDOCH-S1-INT-2020-ONGOING
MURDOCH-S2-INT-2020-ONGOING
OUA-OUA2-EXT-2020-ONGOING

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Enrolment rules

Enrolment in an IT graduate course or permission of the Academic Chair.

Other learning activities

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Learning activities

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Learning outcomes

1.

Establish basic statistical techniques relevant with data science;

2.

Apply basic data analysis methods and predictive modelling that are appropriate to individual datasets and interpret the results

3.

Propose the basic ideas and techniques behind modern data science applications

4.

Apply knowledge in data pre-processing, visualization and analysis using R.

Assessments

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Additional information

Unit content:

· Introduction to data science and big data  · Data pre-processing · Data exploration and visualization  · Introduction to basic statistical and machine learning techniques: regression, classification and clustering · Popular data science applications from fundamentals, such as medical decision making, recommender systems, web search · Introduction to R

Other notes:

A minimum of 3 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 online examination.