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The current and official versions of the course specifications are available on the web at https://www.usq.edu.au/course/specification/current.
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CSC3502 Principles of Big Data Management

Semester 2, 2022 Springfield On-campus
Units : 1
Faculty or Section : Faculty of Health, Engineering and Sciences
School or Department : School of Mathematics, Physics & Computing
Student contribution band : Band 2
Grading basis : Graded
Version produced : 27 June 2022

Staffing

Examiner: Rajib Rana

Requisites

Pre-requisite: CSC3400

Overview

Organisations and governments rely on meaningful data for their decision making processes. The growth of data collection has driven advances in managing and processing of large quantities of data. From businesses to government and scientists the amount of data generated has come to a point where it is difficult to find meaningful answers. There has been growth in technology to provide mechanisms to manage, analyse and distil the meaning of data for decision making. This course focuses on the management of big data sets and exposes students to tools to manage them, and applying existing statistical skills in discovering relevant information.

This course is intended for students with background skills in data analysis and systems design, and focuses on the coordination, management and utilization of data using modern computer data base management systems. Developing, analysing and managing data including privacy and ethical concerns is covered in this course. Reliable, scalable, distributed and efficient handling of data of varying sizes is emphasized.

Course learning outcomes

On successful completion of this course students should be able to:

  1. Articulate data modelling, storage, and retrieval methods and apply knowledge and skills to retrieve information from data storage;
  2. Apply knowledge and skills to complete a project to coordinate and manage large data sets;
  3. Analyse critically and interpret the knowledge from large data sets;
  4. Interpret and transmit information and knowledge in the application discipline to specialist and non-specialist audiences;
  5. Analyse critically and reflect on the issues of privacy and ethics of Big Data.

Topics

Description Weighting(%)
1. Introduction to Big Data Management 10.00
2. Programming for Big Data 20.00
3. Modern methods of distributed processing of large data sets (such as Hadoop and MapReduce) 25.00
4. Modern distributed database for large tables 25.00
5. Manage, store and retrieve processed data in a variety of common formats 10.00
6. Privacy, ethics and professionalism 10.00

Text and materials required to be purchased or accessed

Watson, R T 2013-2015, Data Management, Databases and Organizations, 6th edn, eGreens Press, Athens, Georgia.
(Please follow the links on the textbook author's web site http://www.richardtwatson.com/dm6e/. This book is also available in print.)
Watson, R T 2016, Data management, databases and organizations, 6th edn, Prospect Press.

Student workload expectations

To do well in this subject, students are expected to commit approximately 10 hours per week including class contact hours, independent study, and all assessment tasks. If you are undertaking additional activities, which may include placements and residential schools, the weekly workload hours may vary.

Assessment details

Approach Type Description Group
Assessment
Weighting (%) Course learning outcomes
Assignments Practical Tech and/or scntific artefact 1 No 10 1,2,5
Assignments Practical Tech and/or scntific artefact 2 No 40 3,4
Examinations Non-invigilated Time limited online examinatn No 50 1,3,4,5
Date printed 27 June 2022