Session 2 Learning and Teaching Update
The decision has been made to conduct study online for the remainder of Session 2 for all units WITHOUT mandatory on-campus learning activities. Exams for Session 2 will also be online where possible to do so.
This is due to the extension of the lockdown orders and to provide certainty around arrangements for the remainder of Session 2. We hope to return to campus beyond Session 2 as soon as it is safe and appropriate to do so.
Some classes/teaching activities cannot be moved online and must be taught on campus. You should already know if you are in one of these classes/teaching activities and your unit convenor will provide you with more information via iLearn. If you want to confirm, see the list of units with mandatory on-campus classes/teaching activities.
Visit the MQ COVID-19 information page for more detail.
Unit convenor and teaching staff |
Unit convenor and teaching staff
Convener
Amin Beheshti
Lecturer
Diego Molla-Aliod
Tutor
Urvashi Khanna
Tutor
Sam Khadivizand
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Credit points |
Credit points
10
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Prerequisites |
Prerequisites
Admission to MRes
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Corequisites |
Corequisites
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Co-badged status |
Co-badged status
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Unit description |
Unit description
This unit introduces students to the specialised technologies required for big data applications in business, organisations and scientific research. It covers specialised methods for storing, manipulating, analysing and exploiting the ever-increasing amounts of data that are encountered in practical applications, and provides hands-on training in advanced topics such as distributed computing clusters and 'cloud computing'.
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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:
Important Academic Dates
Information about important academic dates including deadlines for withdrawing from units are available at https://students.mq.edu.au/important-dates
General Assessment Information
All assignments will be submitted using iLearn. The results of all assignments will be available via iLearn.
Late Submission
No extensions will be granted without an approved application for Special Consideration. There will be a deduction of 10% of the total available marks made from the total awarded mark for each 24 hour period or part thereof that the submission is late. For example, 25 hours late in submission for an assignment worth 10 marks – 20% penalty or 2 marks deducted from the total. No submission will be accepted after solutions have been posted.
The final exam is not a hurdle assessment.
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 |
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Assignment 1 - Data Lakes | 10% | No | Week 4 |
Assignment 2 - Processing Data | 25% | No | Week 7 |
Assignment 3 - Data Analysis | 25% | No | Week 12 |
Final examination | 40% | No | Week 13 |
Assessment Type 1: Essay
Indicative Time on Task 2: 10 hours
Due: Week 4
Weighting: 10%
In this assignment you will explore the management of big data using data lake technology.
Assessment Type 1: Essay
Indicative Time on Task 2: 20 hours
Due: Week 7
Weighting: 25%
In this assignment you will apply techniques to index, search and process high-dimensional data.
Assessment Type 1: Essay
Indicative Time on Task 2: 20 hours
Due: Week 12
Weighting: 25%
In this assignment you will perform analysis of Big Data.
Assessment Type 1: Case study/analysis
Indicative Time on Task 2: 25 hours
Due: Week 13
Weighting: 40%
We will replace the final exam with a Problem Analysis Report, to assess students' understanding of the learning outcomes in the Big Data Problems. The final exam will no longer be a Hurdle. It will be a Problem Analysis Report that will be made available to the students online on week 12, with the submission deadline in week 14. The final exam report should be submitted on iLearn (Turnitin), by the deadline.
1 If you need help with your assignment, please contact:
2 Indicative time-on-task is an estimate of the time required for completion of the assessment task and is subject to individual variation
For details of days, times and rooms consult the timetables webpage.
Required and Recommended Texts
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.
Technology Used and Required
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.
Unit Web Page
The unit web page will be hosted in iLearn, where you will need to log in 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.
| Lecture | Workshop/Practical -------------------------------------------------------------------------------------------------------------------- Week 01 | Intro to Big Data | Data Lake -------------------------------------------------------------------------------------------------------------------- Week 02 | Bringing data together for analysis (Data Lake) | Microsoft Data Lake -------------------------------------------------------------------------------------------------------------------- Week 03 | Azure ML | Azure Labs -------------------------------------------------------------------------------------------------------------------- Week 04 | Visualisation of data/PowerBI | PowerBI -------------------------------------------------------------------------------------------------------------------- Week 05 | Principles of Architecture/Azure Labs | Azure Labs -------------------------------------------------------------------------------------------------------------------- Week 06 | Applications of Big Data Analytics | Knowledge Lake -------------------------------------------------------------------------------------------------------------------- Week 07 | Analysing Big Data | Data Analytics -------------------------------------------------------------------------------------------------------------------- Week 08 | Text Analytics | Text Analytics -------------------------------------------------------------------------------------------------------------------- Week 09 | Text Analytics (II) | Text Analytics -------------------------------------------------------------------------------------------------------------------- Week 10 | Visualising Big Data | Visual Analytics -------------------------------------------------------------------------------------------------------------------- Week 11 | Analysing Streaming Data | Visual and Text Analytics -------------------------------------------------------------------------------------------------------------------- Week 12 | Big Data and Society | Assignment Demonstration -------------------------------------------------------------------------------------------------------------------- Week 13 | Unit Review + Final Assessment | NA --------------------------------------------------------------------------------------------------------------------
Macquarie University policies and procedures are accessible from Policy Central (https://policies.mq.edu.au). Students should be aware of the following policies in particular with regard to Learning and Teaching:
Students seeking more policy resources can visit Student Policies (https://students.mq.edu.au/support/study/policies). It is your one-stop-shop for the key policies you need to know about throughout your undergraduate student journey.
To find other policies relating to Teaching and Learning, visit Policy Central (https://policies.mq.edu.au) and use the search tool.
Macquarie University students have a responsibility to be familiar with the Student Code of Conduct: https://students.mq.edu.au/admin/other-resources/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 help you improve your marks and take control of your study.
The Library provides online and face to face support to help you find and use relevant information resources.
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.
In this offering, we work closely with Microsoft Team to learn about Big Data Technologies used in Microsoft to Organize, Analyze and Visualize Big Data.
Unit information based on version 2021.01R of the Handbook