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Area/Catalogue
INFS 4020

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Course Level
Undergraduate

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Offered Externally
Yes

Note: This offering may or may not be scheduled in every study period. Please refer to the timetable for further details.

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Course ID
172170

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Unit Value
4.5

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University-wide elective course
No

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Timetable/s
First Semester
(Study Period 2)

Second Semester
(Study Period 5)
Course owner

Course owner
UniSA STEM

Course aim

To explore Big Data concepts, including cloud and Big Data architectures, an overview of Big Data analytics, implementation of Big Data initiatives, and to apply these concepts using an industry standard non-relational database environment.

Course content

Part A: Big Data Fundamentals
The Data explosion and the evolution of Big Data: definition, characteristics, sources and benefits of Big Data; Challenges with the data explosion: information management and strategy, privacy & security, data analytics; Skills, competencies and roles associated with Big Data.

Part B: Big Data Technologies Roadmap
Cloud and Big Data architectures; Non-relational databases: techniques for storing and processing large volumes of structured and unstructured data; Hadoop, MapReduce and the Big Data ecosystem; Information quality & governance of Big Data.

Part C: Big Data Analytics Overview
Reasoning, logic, dealing with uncertainty, search, indexing and memory, streams, information, language and sentiment, visualisation of Big Data.

Part D: Execution and Implementation of Big Data Initiatives
The business case for Big Data Initiatives, analytics process management, innovation using Big Data.

Part E: Future Directions in Big Data

Textbook(s)

Nil

Prerequisite(s)

Subject Area & Catalogue Number Course Name
Group 1
Students must have completed one of the following:
INFS 1025 Data Driven Web Technologies
INFT 1032 UO Data Driven Web Technologies

Students studying in a postgraduate program in Engineering (LMEL) or Data Science (LCDA, LCDS, LGDS, LMDS) are exempt from the pre-requisite (INFS 1025) requirement. Student studying a sub-major in Digital Technologies for Education within the undergraduate LHSE program must complete 18 units of the sub-major before enrolling into Big Data Concepts.

Corequisite(s)

Nil

Teaching method

Component Duration
INTERNAL, MAWSON LAKES
Workshop (Face-to-face and/or virtual workshops, including small group work) 2 hours x 13 weeks
INTERNAL, CITY WEST
Workshop (Face-to-face and/or virtual workshops, including small group work) 2 hours x 13 weeks
EXTERNAL, MAWSON LAKES, ONLINE
External (On-line materials, online interactions with these materials, online interactions with the course coordinator and with other students, including through virtual workshops) N/A x 13 weeks

Note: These components may or may not be scheduled in every study period. Please refer to the timetable for further details.


Assessment

Task Length Weighting Duration
INTERNAL, MAWSON LAKES
Continuous assessment 500 words 20% N/A
Assignment 1500 words 30% N/A
Assignment 2500 words 50% N/A
INTERNAL, CITY WEST
Continuous assessment 500 words 20% N/A
Assignment 1500 words 30% N/A
Assignment 2500 words 50% N/A
EXTERNAL, MAWSON LAKES, ONLINE
Continuous assessment 500 words 20% N/A
Assignment 1500 words 30% N/A
Assignment 2500 words 50% N/A

Fees

EFTSL*: 0.125
Commonwealth Supported program (Band 2)
To determine the fee for this course as part of a Commonwealth Supported program, go to:
How to determine your Commonwealth Supported course fee. (Opens new window)

Fee-paying program for domestic and international students
International students and students undertaking this course as part of a postgraduate fee paying program must refer to the relevant program home page to determine the cost for undertaking this course.

Non-award enrolment
Non-award tuition fees are set by the university. To determine the cost of this course, go to:
How to determine the relevant non award tuition fee. (Opens new window)

Not all courses are available on all of the above bases, and students must check to ensure that they are permitted to enrol in a particular course.

* Equivalent Full Time Study Load. Please note: all EFTSL values are published and calculated at ten decimal places. Values are displayed to three decimal places for ease of interpretation.

Course Coordinators

Checking your eligibility