Students
MMBA8113 – Big Data and Decision Making
2023 – Term 1, In person-scheduled-weekday, North Ryde
General Information
Download as PDF
| Unit convenor and teaching staff |
Unit convenor and teaching staff
Nejhdeh Ghevondian
Chow
Angela
|
|---|---|
| Credit points |
Credit points
10
|
| Prerequisites |
Prerequisites
(MGSM960 or MMBA8160) or Admission to GradCertBusAdmin or GradDipBusAdmin
|
| Corequisites |
Corequisites
|
| Co-badged status |
Co-badged status
|
| 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. |
Important Academic Dates
Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates
Learning Outcomes
On successful completion of this unit, you will be able to:
- ULO1: Develop a broad understanding and knowledge of the Big Data ecosystem and its applications within the context of managerial decision-making processes.
- ULO2: Explore Data Science theories, methodologies and tools and their practical applications to solve real life business problems.
- ULO3: Use tangible and intangible resources to gain insights from large and versatile sets of data and understand the additional requirements needed.
- ULO4: Apply and/or customise big data and data science solutions to various business contexts.
General Assessment Information
Late Assessment Submission Penalty (written assessments)
Unless a Special Consideration request has been submitted and approved, a 5% penalty (of the total possible mark) will be applied each day a written assessment is not submitted, up until the 7th day (including weekends). After the 7th day, a grade of ‘0’ will be awarded even if the assessment is submitted. Submission time for all written assessments is set at 11.55pm. A 1-hour grace period is provided to students who experience a technical concern.
For any late submissions of time-sensitive tasks, such as scheduled tests/exams, performance assessments/presentations, and/or scheduled practical assessments/labs, students need to submit an application for Special Consideration.
Assessment Tasks
| Name | Weighting | Hurdle | Due |
|---|---|---|---|
| Individual Assignment | 30% | No | Week 5 |
| Final Examination | 30% | No | 20-26 March 2023 |
| Class contribution | 10% | No | Week 10 |
| Group Assignment | 30% | No | Week 10 |
Individual Assignment
Assessment Type 1: Modelling task
Indicative Time on Task 2: 20 hours
Due: Week 5
Weighting: 30%
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.
On successful completion you will be able to:
- Develop a broad understanding and knowledge of the Big Data ecosystem and its applications within the context of managerial decision-making processes.
- Explore Data Science theories, methodologies and tools and their practical applications to solve real life business problems.
- Use tangible and intangible resources to gain insights from large and versatile sets of data and understand the additional requirements needed.
- Apply and/or customise big data and data science solutions to various business contexts.
Final Examination
Assessment Type 1: Examination
Indicative Time on Task 2: 10 hours
Due: 20-26 March 2023
Weighting: 30%
A closed book two hour examination will be held during the University Examination Period.
On successful completion you will be able to:
- Develop a broad understanding and knowledge of the Big Data ecosystem and its applications within the context of managerial decision-making processes.
- Explore Data Science theories, methodologies and tools and their practical applications to solve real life business problems.
- Use tangible and intangible resources to gain insights from large and versatile sets of data and understand the additional requirements needed.
- Apply and/or customise big data and data science solutions to various business contexts.
Class contribution
Assessment Type 1: Participatory task
Indicative Time on Task 2: 5 hours
Due: Week 10
Weighting: 10%
Students will be required to participate in in-class discussions.
On successful completion you will be able to:
- Develop a broad understanding and knowledge of the Big Data ecosystem and its applications within the context of managerial decision-making processes.
- Explore Data Science theories, methodologies and tools and their practical applications to solve real life business problems.
- Use tangible and intangible resources to gain insights from large and versatile sets of data and understand the additional requirements needed.
- Apply and/or customise big data and data science solutions to various business contexts.
Group Assignment
Assessment Type 1: Project
Indicative Time on Task 2: 20 hours
Due: Week 10
Weighting: 30%
The group will be required to produce a report of no more than 6000 words and present the findings to the class.
On successful completion you will be able to:
- Develop a broad understanding and knowledge of the Big Data ecosystem and its applications within the context of managerial decision-making processes.
- Explore Data Science theories, methodologies and tools and their practical applications to solve real life business problems.
- Use tangible and intangible resources to gain insights from large and versatile sets of data and understand the additional requirements needed.
- Apply and/or customise big data and data science solutions to various business contexts.
1 If you need help with your assignment, please contact:
- the academic teaching staff in your unit for guidance in understanding or completing this type of assessment
- the Writing Centre for academic skills support.
2 Indicative time-on-task is an estimate of the time required for completion of the assessment task and is subject to individual variation
Delivery and Resources
This course will use a combination of analytical tools, including Azure Machine Learning Studio and Power BI
There will be class reading whte papers and journals. Please refer to iLearn for more information
Policies and Procedures
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:
- Academic Appeals Policy
- Academic Integrity Policy
- Academic Progression Policy
- Assessment Policy
- Fitness to Practice Procedure
- Assessment Procedure
- Complaints Resolution Procedure for Students and Members of the Public
- Special Consideration Policy
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.
Student Code of Conduct
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
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
Academic Integrity
At Macquarie, we believe academic integrity – honesty, respect, trust, responsibility, fairness and courage – is at the core of learning, teaching and research. We recognise that meeting the expectations required to complete your assessments can be challenging. So, we offer you a range of resources and services to help you reach your potential, including free online writing and maths support, academic skills development and wellbeing consultations.
Student Support
Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/
The Writing Centre
The Writing Centre provides resources to develop your English language proficiency, academic writing, and communication skills.
- Workshops
- Chat with a WriteWISE peer writing leader
- Access StudyWISE
- Upload an assignment to Studiosity
- Complete the Academic Integrity Module
The Library provides online and face to face support to help you find and use relevant information resources.
Student Services and Support
Macquarie University offers a range of Student Support Services including:
- IT Support
- Accessibility and disability support with study
- Mental health support
- Safety support to respond to bullying, harassment, sexual harassment and sexual assault
- Social support including information about finances, tenancy and legal issues
- Student Advocacy provides independent advice on MQ policies, procedures, and processes
Student Enquiries
Got a question? Ask us via AskMQ, or contact Service Connect.
IT Help
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.
Unit information based on version 2023.05 of the Handbook