UO Big Data Analytics

Course aim

To provide an overview of the tools and techniques of data analytics for big data, and how these can be used by an organisation. To introduce and use some of the common big data tools.

Course content

Overview of Big Data analytics, Characteristics of Big Data, Analysis flow for Big Data.
NoSQL systems: HBase, MongoDB
Data acquisition and storage: Kafka, HDFS, S3
Big Data manipulation techniques: Batch Analytics (Hadoop Map-Reduce), Real-Time Analytics, Interactive Querying
Big Data languages/tools: Pig; Oozie, Spark, Strom, Hive
Big Data applications (e.g. recommendation systems, time series analysis; text analytics).

Textbooks

Nil

Prerequisites

Common to all relevant programs
Subject Area & Catalogue Number	Course Name
INFS 3079	UO Business Analytics
INFS 3081	UO Predictive Analytics

Corequisite(s)

Nil

Teaching Method

Component	Duration
EXTERNAL, ONLINE ACTIVITY
Online	10 weeks x N/A

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

Assessment

Continuous assessment, Examination

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:
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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:
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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