CSC3502 Principles of Big Data Management
|Semester 2, 2019 Online|
|Short Description:||Principles of Big Data Managt|
|Faculty or Section :||Faculty of Health, Engineering and Sciences|
|School or Department :||School of Agric, Comp and Environ Sciences|
|Student contribution band :||Band 2|
|ASCED code :||029999 - Information Technology not els|
|Grading basis :||Graded|
Examiner: Angela Howard
Requisite: Knowledge of computing consistent with CSC1401 Foundation Programming
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 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 varying sizes, develops the pragmatics of managing data, alongside with retrieval and analysis of information.
On successful completion of this course students should be able to:
- Articulate data modelling, storage, and retrieval methods and apply knowledge and skills to retrieve information from data storage;
- Apply knowledge and skills to 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=2019&sem=02&subject1=CSC3502)
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||15||12 Aug 2019|
|Assignment 2||100||15||14 Oct 2019|
|Exam||100||70||End S2||(see note 1)|
- This will be a Closed exam. The total working time for the examination is 2 hours. The examination date will be available via UConnect when the official examination timetable has been released.
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 obtain at least 50% of the total weighted marks available for the course (i.e. the Primary Hurdle), and have satisfied the Secondary Hurdle (Supervised), i.e. the end of semester examination by achieving at least 40% of the weighted marks available for that assessment item.
Supplementary assessment may be offered where a student has undertaken all of the required summative assessment items and has passed the Primary Hurdle but failed to satisfy the Secondary Hurdle (Supervised), or has satisfied the Secondary Hurdle (Supervised) but failed to achieve a passing Final Grade by 5% or less of the total weighted Marks.
To be awarded a passing grade for a supplementary assessment item (if applicable), a student must achieve at least 50% of the available marks for the supplementary assessment item as per the Assessment Procedure http://policy.usq.edu.au/documents/14749PL (point 4.4.2).
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.
This is Closed examination: Candidates are allowed to bring only writing and drawing instruments into a closed examination.
Examination period when Deferred/Supplementary examinations will be held:
Any Deferred or Supplementary examinations for this course will be held during the next examination period.
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).
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
1. 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:
2. conforms to the USQ Policy on Evaluation of Teaching, Courses and Programs to ensure ongoing monitoring and systematic improvement.
3. forms part of the BITC 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.