Notice
As part of Phase 3 of our return to campus plan, most units will now run tutorials, seminars and other small group activities on campus, and most will keep an online version available to those students unable to return or those who choose to continue their studies online.
To check the availability of face-to-face and online activities for your unit, please go to timetable viewer. To check detailed information on unit assessments visit your unit's iLearn space or consult your unit convenor.
Unit convenor and teaching staff |
Unit convenor and teaching staff
Nejhdeh Ghevondian
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---|---|
Credit points |
Credit points
10
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Prerequisites |
Prerequisites
MGSM960 or MMBA8160
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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 is a bridge between business and information technology and will equip students with knowledge and skills required to lead and manage big data and data science projects for organisations. Specifically, the unit focuses on data science development practices and the underlying big data applications, on both strategic and operational levels. |
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:
Checking your grades
Assessment criteria for all assessment tasks will be posted on the unit's iLearn site. It is the responsibility of students to view their marks for each within session assessment on iLearn within 20 working days of posting. If there are any discrepancies, students must contact the unit convenor immediately. Failure to do so will mean that queries received after the release of final results regarding assessment marks (not including the final exam mark) will not be addressed.”
Special Consideration
Macquarie University recognises that students may experience events or conditions that adversely affect their academic performance. If you experience serious and unavoidable difficulties at exam time or when assessment tasks are due, you can consider applying for Special Consideration. The University Policy for Special Consideration is given at https://students.mq.edu.au/study/my-study-program/special-consideration. Where a Special Consideration application is approved, the student may be offered an alternative assessment or may receive a mark based on the percentage mark achieved by the student in one or more other assessment tasks, at the Unit Convenor’s discretion.
Extension of time for assignments
Tasks 10% or less – No extensions will be granted. Students who have not submitted the task prior to the deadline will be awarded a mark of 0 for the task, except for cases in which an application for special consideration is made and approved.
Tasks above 10% - No extensions will be granted. 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 – 20% penalty). This penalty does not apply for cases in which an application for special considereation is made and approved. No submission will be accepted after solutions have been posted.
Name | Weighting | Hurdle | Due |
---|---|---|---|
Final Examination | 40% | No | University Exam Period |
Class contribution | 10% | No | 21/02/21 |
Group Assignment and Presentation | 35% | No | 21/02/21 |
Individual Assignment | 15% | No | 12/02/21 |
Assessment Type 1: Examination
Indicative Time on Task 2: 15 hours
Due: University Exam Period
Weighting: 40%
A closed book three hour examination will be held during the University Examination Period.
Assessment Type 1: Participatory task
Indicative Time on Task 2: 5 hours
Due: 21/02/21
Weighting: 10%
Students will be required to participate in in-class discussions.
Assessment Type 1: Project
Indicative Time on Task 2: 20 hours
Due: 21/02/21
Weighting: 35%
The group will be required to produce a report of no more than 6000 words and present the findings to the class.
Assessment Type 1: Modelling task
Indicative Time on Task 2: 15 hours
Due: 12/02/21
Weighting: 15%
Individual assignments are based on a number of analytics case studies given in class with their relevant datasets. Students will be given a choice to select one of these case studies and perform suitable predictive modelling techniques, including exploratory analysis, modelling and visualisation. Students will be required to submit a report (approx. 5 – 6 pages in length) highlighting the application of insights, concepts, and relevant techniques used to perform the case study outcomes.
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
Session |
Day |
Topic |
Required Readings |
---|---|---|---|
1 |
Day 1 (29/01/21) |
Module 1: Introduction to Big Data & Data Science |
Book: Chapter 1 & 2 Article: An Introduction to Big Data – Journal of Financial Service Professionals (2018) |
2 |
Day 1 (29/01/21) |
Module 2: Big Data, Best Practices & Managerial Decisions |
Book: Chapter 1 & 2 Article: Implementing big data strategies: A managerial perspective – Business Horizons (2019) |
3 |
Day 2 (30/01/21) |
Module 3: Fundamentals of Statistics |
Article: Are Business Leaders Prepared to Handle the Upcoming Revolution in Business AI (2018) |
4 |
Day 2 (30/01/21) |
Module 4: Exploratory Data Analysis |
Article: How Netflix Used Big Data To Give Us The Programmes We Want1,2 |
5 |
Day 3 (31/01/21) |
Module 5: Introduction to Predictive Modelling – part 1 |
Article: WALMART: How Big Data is Used to Drive Supermarket Performance |
6 |
Day 3 (31/01/21) |
Module 6: Introduction to Predictive Modelling – part 2 |
Article: Facebook language predicts depression in medical records (2018)
|
7 |
Day 4 (20/02/21) |
Module 7: Visualisation & Story Telling |
Article: The rising tide of AI & Business Automation: Developing an Ethical Framework (2018) – Business Horizons |
8 |
Day 4 (20/02/21) |
Module 8: Big Data Architecture, Operationalisation & Model Management |
Article: Big Data Analytics in Medicine & Healthcare (2018) |
9 |
Day 5 (21/02/21) |
Module 9: Putting it Altogether – Big Data Business Strategy Roadmap
|
Article: An Artificial Intelligence Approach to Financial Fraud Detection under IoT Environment |
10 |
Day 5 (21/02/21) |
Module 10: Group Assignment Presentation |
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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.
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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.
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