Unit convenor and teaching staff |
Unit convenor and teaching staff
Amin Beheshti
Jia Wu
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Credit points |
Credit points
3
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Prerequisites |
Prerequisites
39cp at 100 level or above including COMP257
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Corequisites |
Corequisites
ISYS358
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Co-badged status |
Co-badged status
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Unit description |
Unit description
Even simple tasks like counting elements can seem impossible when the amount of data to process is huge. This unit explores some of the key aspects related to processing and mining information from large volumes of data. We present technology commonly used in industry such as map-reduce, and show how a range of data processing methods can be realised using map-reduce. Especial emphasis will be placed in the adaptation of data mining techniques for large volumes of data and for data streaming.
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Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates
On successful completion of this unit, you will be able to:
All assignments will be submitted using iLearn. The results of all assignments will be available via iLearn.
Late submission to the assignments will be penalised with the following deductions:
The final exam is a hurdle assessment. This means that:
The final mark of the unit will be obtained by summing the marks of all the assessment tasks for a total mark of 100. In order to pass the unit:
Name | Weighting | Hurdle | Due |
---|---|---|---|
Assignment 1 | 5% | No | Week 3 |
Assignment 2 | 20% | No | Week 8 |
Assignment 3 | 15% | No | Week 12 |
Final Exam | 60% | Yes | Examination period |
Due: Week 3
Weighting: 5%
Due: Week 3 Weighting: 5%
In this assignment you will acquire hands-on experience in designing, implementing and querying a NoSQL database, i.e. MongoDB. This Assessment Task relates to the following Learning Outcomes:
Due: Week 8
Weighting: 20%
Due: Week 8 Weighting: 20%
In this assignment you will implement MapReduce techniques for the processing of Big Data. You will build your assignment on top of Hadoop (i.e. an open-source version of MapReduce written in Java).
This Assessment Task relates to the following Learning Outcomes:
Due: Week 12
Weighting: 15%
Due: Week 12 Weighting: 15%
In this assignment you will implement a non-trivial problem that processes Big Data.
This Assessment Task relates to the following Learning Outcomes:
Due: Examination period
Weighting: 60%
This is a hurdle assessment task (see assessment policy for more information on hurdle assessment tasks)
Due: Examination period Weighting: 60% This is a hurdle assessment task (see assessment policy for more information on hurdle assessment tasks)
The final exam will focus on the theoretical aspects of the unit, including algorithms and implementation issues.
This is a hurdle assessment. This means that you need to pass the exam in order to pass the unit.
This Assessment Task relates to the following Learning Outcomes:
Much of the contents of the unit will be based on the following books:
Additional material including lecture notes will be made available during the semester. See the unit schedule for a listing of the most relevant reading for each week.
The following software is used in COMP336:
This software is installed in the labs; you should also ensure that you have working copies of all the above on your own machine. Note that some of this software requires internet access.
Many packages come in various versions; to avoid potential incompatibilities, you should install versions as close as possible to those used in the labs.
The unit web page will be hosted in iLearn, where you will need to login using your Student One ID and password. The unit will make extensive use of discussion boards also hosted in iLearn. Please post questions there, they will be monitored by the staff on the unit.
Week 1 - Data and Big Data
Week 2 - Organizing Big Data
Week 3 - Curating Big Data
Week 4 - Processing Big Data (Cloud Computing)
Week 5 - Processing Big Data (MapReduce-Part I)
Week 6 - Processing Big Data (MapReduce-Part II)
Week 7: Big Data Mining with High Dimensions
Week 8: Big Data Mining with Large Instances
Week 9: Deep Learning Model
Week 10: Fast Mining Models
Week 11: Handling Uncertainty
Week 12: Big Data Mining Applications
Week 13: Unit and Exam Review
Macquarie University policies and procedures are accessible from Policy Central (https://staff.mq.edu.au/work/strategy-planning-and-governance/university-policies-and-procedures/policy-central). Students should be aware of the following policies in particular with regard to Learning and Teaching:
Undergraduate students seeking more policy resources can visit the Student Policy Gateway (https://students.mq.edu.au/support/study/student-policy-gateway). It is your one-stop-shop for the key policies you need to know about throughout your undergraduate student journey.
If you would like to see all the policies relevant to Learning and Teaching visit Policy Central (https://staff.mq.edu.au/work/strategy-planning-and-governance/university-policies-and-procedures/policy-central).
Macquarie University students have a responsibility to be familiar with the Student Code of Conduct: https://students.mq.edu.au/study/getting-started/student-conduct
Results published on platform other than eStudent, (eg. iLearn, Coursera etc.) or released directly by your Unit Convenor, are not confirmed as they are subject to final approval by the University. Once approved, final results will be sent to your student email address and will be made available in eStudent. For more information visit ask.mq.edu.au or if you are a Global MBA student contact globalmba.support@mq.edu.au
Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/
Learning Skills (mq.edu.au/learningskills) provides academic writing resources and study strategies to improve your marks and take control of your study.
Students with a disability are encouraged to contact the Disability Service who can provide appropriate help with any issues that arise during their studies.
For all student enquiries, visit Student Connect at ask.mq.edu.au
If you are a Global MBA student contact globalmba.support@mq.edu.au
For help with University computer systems and technology, visit http://www.mq.edu.au/about_us/offices_and_units/information_technology/help/.
When using the University's IT, you must adhere to the Acceptable Use of IT Resources Policy. The policy applies to all who connect to the MQ network including students.
Our graduates will also be capable of creative thinking and of creating knowledge. They will be imaginative and open to experience and capable of innovation at work and in the community. We want them to be engaged in applying their critical, creative thinking.
This graduate capability is supported by:
Our graduates will take with them the intellectual development, depth and breadth of knowledge, scholarly understanding, and specific subject content in their chosen fields to make them competent and confident in their subject or profession. They will be able to demonstrate, where relevant, professional technical competence and meet professional standards. They will be able to articulate the structure of knowledge of their discipline, be able to adapt discipline-specific knowledge to novel situations, and be able to contribute from their discipline to inter-disciplinary solutions to problems.
This graduate capability is supported by:
We want our graduates to be capable of reasoning, questioning and analysing, and to integrate and synthesise learning and knowledge from a range of sources and environments; to be able to critique constraints, assumptions and limitations; to be able to think independently and systemically in relation to scholarly activity, in the workplace, and in the world. We want them to have a level of scientific and information technology literacy.
This graduate capability is supported by:
Our graduates should be capable of researching; of analysing, and interpreting and assessing data and information in various forms; of drawing connections across fields of knowledge; and they should be able to relate their knowledge to complex situations at work or in the world, in order to diagnose and solve problems. We want them to have the confidence to take the initiative in doing so, within an awareness of their own limitations.
This graduate capability is supported by:
The Big Data domain is advancing very fast. Accordingly, the content proposed in 2018 has been reviewed and updated for this offering. Particularly, we have offered new and trending topics in:
- Big Data Mining with High Dimensions
- Deep Learning Model
- Big Data Mining Applications