| Semester 3, 2022 Online | |
| Units : | 1 |
| Faculty or Section : | Faculty of Health, Engineering and Sciences |
| School or Department : | School of Mathematics, Physics & Computing |
| Grading basis : | Graded |
| Course fee schedule : | /current-students/administration/fees/fee-schedules |
Staffing
Examiner:
Requisites
Pre-requisite or Co-requisite: (CSC1401 or CSC5020) and (STA2300 or STA1003 or STA8170) or equivalent program and statistical knowledge and skills or students are enrolled in MCYS.
Overview
Businesses and scientists are collecting more data than ever before. This course is designed to equip the students with computers skills and concepts required to ensure that the data collected is secure, stored and retrieved efficiently, and presented in a useful form. It is also designed to ensure that graduates have the qualitative and information technology skills needed to handle the challenge of data volume, velocity, and variety.
This course is intended for students experienced in statistical analysis, experimental design, and basic systems design, and focuses on the coordination, management and utilization of data using modern computer data base management systems. This course, in emphasizing the reliable, scalable, distributed and efficient handling of data of any size, develops the pragmatics of managing data, alongside with retrieval and analysis of information.
Course learning outcomes
On successful completion of this course students should be able to:
- demonstrate advanced and integrated understanding of data modelling, storage, and retrieval methods and apply knowledge and skills to retrieve information from data storage
- apply knowledge and skills to design and complete a project to coordinate and manage large data sets
- analyse critically and interpret the knowledge from large data sets
- interpret and transmit information and knowledge in the application discipline to specialist and non-specialist audiences
- 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
(Please follow the links on the textbook author's web site . This book is also available in print.)
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
| Description | Group Assessment |
Weighting (%) | Course learning outcomes |
|---|---|---|---|
| Tech and/or scntific artefact 1 | No | 25 | 1 |
| Tech and/or scntific artefact 2 | No | 25 | 1,2,3,5 |
| Tech and/or scntific artefact 3 | No | 50 | 1,2,3,4,5 |