|Semester 3, 2020 Online|
|Short Description:||Big Data Management|
|Faculty or Section :||Faculty of Health, Engineering and Sciences|
|School or Department :||School of Sciences|
|Student contribution band :||Band 2|
|ASCED code :||029999 - Information Technology not els|
|Grading basis :||Graded|
|Version produced :||4 December 2020|
Examiner: Rajib Rana
Pre-requisite or Co-requisite: CSC1401 and (STA2300 or STA8170) or equivalent program and statistical knowledge and skills.
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.
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.
|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
ALL textbooks and materials available to be purchased can be sourced from USQ's Online Bookshop (unless otherwise stated). (https://omnia.usq.edu.au/textbooks/?year=2020&sem=03&subject1=CSC8002)
Please contact us for alternative purchase options from USQ Bookshop. (https://omnia.usq.edu.au/info/contact/)
(Please follow the links on the textbook author's web site http://www.richardtwatson.com/dm6e/. This book is also available in print.)
Student workload expectations
|Description||Marks out of||Wtg (%)||Due Date||Notes|
|Assignment 1||100||25||18 Dec 2020|
|Assignment 2||100||25||15 Jan 2021|
|Final Project Report||100||50||12 Feb 2021|
Important assessment information
There are no attendance requirements for this course. However, it is the students’ responsibility to study all material provided to them or required to be accessed by them to maximise their chance of meeting the objectives of the course and to be informed of course-related activities and administration.
Requirements for students to complete each assessment item satisfactorily:
To satisfactorily complete an individual assessment item a student must achieve at least 50% of the marks for that item.
Penalties for late submission of required work:
Students should refer to the Assessment Procedure http://policy.usq.edu.au/documents.php?id=14749PL (point 4.2.4)
Requirements for student to be awarded a passing grade in the course:
To be assured of receiving a passing grade a student must achieve at least 50% of the total weighted marks available for the course.
Method used to combine assessment results to attain final grade:
The final grades for students will be assigned on the basis of the aggregate of the weighted marks obtained for each of the summative items for the course..
There is no examination in this course.
Examination period when Deferred/Supplementary examinations will be held:
There is no examination in this course, there will be no deferred or supplementary examinations.
University Student Policies:
Students should read the USQ policies: Definitions, Assessment and Student Academic Misconduct to avoid actions which might contravene University policies and practices. These policies can be found at http://policy.usq.edu.au.
Students must familiarise themselves with the USQ Assessment Procedures (http://policy.usq.edu.au/documents.php?id=14749PL).
If electronic submission is specified for a course assessment, students will be notified of this on the Course Study Desk. The due date for an electronically submitted assessment is the date by which a student must electronically submit the assignment irrespective of holidays. The assignment files must be submitted by 11.55pm on the due date using USQ time (as displayed on the clock on the course home page; that is, Australian Eastern Standard Time).
If hardcopy submission is specified for a course assessment students will be notified of this on the Course Study Desk. The due date for a hardcopy assignment is the date by which a student must submit at USQ or despatch the assignment to USQ irrespective of holidays.
USQ will NOT accept submission of assignments by facsimile or email unless expressly requested by the course examiner.
Referencing in Assignments must comply with the Harvard (AGPS) referencing system. This system should be used by students to format details of the information sources they have cited in their work. The Harvard (APGS) style to be used is defined by the USQ library’s referencing guide. These policies can be found at http://www.usq.edu.au/library/referencing
Evaluation and benchmarking
In meeting the University’s aims to establish quality learning and teaching for all programs, this course monitors and ensures quality assurance and improvements in at least two ways. This course:
- conforms to the USQ Policy on Evaluation of Teaching, Courses and Programs to ensure ongoing monitoring and systematic improvement.
- forms part of the Applied Data Science Program (inset name of Program e.g. Bachelor of Engineering or provide dropdown of program names) and is benchmarked against the internal USQ accreditation/reaccreditation processes which include (i) stringent standards in the independent accreditation of its academic programs, (ii) close integration between business and academic planning, and (iii) regular and rigorous review.
Computer, e-mail and Internet access:
Students are required to have access to a personal computer, e-mail capabilities and Internet access to UConnect. Current details of computer requirements can be found at http://www.usq.edu.au/current-students/support/computing/hardware.
Students can expect that questions in assessment items in this course may draw upon knowledge and skills that they can reasonably be expected to have acquired before enrolling in this course. This includes knowledge contained in pre-requisite courses and appropriate communication, information literacy, analytical, critical thinking, problem solving or numeracy skills. Students who do not possess such knowledge and skills should not expect the same grades as those students who do possess them.