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Please note that this Course has Requisites listed.

Offering Information

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Course Description

Government, enterprise, and science are collecting more data than ever before. It is not only the huge volume of data, but the type of data has also changed to cater for wider use and more insight. Reliable, scalable, distributed, and efficient handling of data of any size, requires pragmatics of… For more content click the Read More button below.

Topics

Introduction to Big Data Management (5%) Ethics, privacy, and security – deeper impact (5%) Understanding the data (10%) Programming skills for data interrogation (R, Hadoop, MapReduce) (10%) Categories of applications, technologies, and architecture (10%) Using R to interrogate large data set and database (10%) Differentiate programming versus use of… For more content click the Read More button below.

Availability

Springfield

Toowoomba

Course Contacts

Rajib Rana

Enrolment Rules

Course Pre-requisites

When planning your enrolment, you might come across terms like prerequisite, corequisite, and anti-requisite. These are important to understand because they affect which courses you can take and when.

Prerequisite

A prerequisite is a course you must complete before enrolling in another course. It ensures you have the necessary background knowledge and skills to succeed in the next course.

Corequisite

A corequisite is a course that you need to take at the same time as another course, or before it. These courses complement each other and are often required together in professionally accredited programs.

Anti-requisite

An anti-requisite is a course that has very similar content to another course. You can’t enrol in both because it wouldn’t benefit your learning to repeat the same material.

Assessments

Report 1

Report 2

Time limited online examination

Learning Outcomes

Upon completion of this course, graduates will be able to:
1.
Apply advanced and integrated knowledge and technical skills of data modelling, storage, and retrieval methods to retrieve information from data storage using technical skills within a variety of areas within the Applied Data Science domain
2.
Analyse critically and interpret large data sets to determine trends and discover hidden pattern to provide considered decisions in the recommendations
3.
Apply technical skills to extract, interpret, and transmit information and knowledge and report on
4.
Analyse critically and recognize ethics, privacy, and security are applied to datasets.

Learning Resources

Other