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MMBA8113 – Big Data and Decision Making

2022 – Term 3, In person-scheduled-weekday, North Ryde

General Information

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Unit convenor and teaching staff Unit convenor and teaching staff
Nejhdeh Ghevondian
nejhdeh.ghevondian@mq.edu.au
Prani Manandhar
prani.manandhar@mq.edu.au
Credit points Credit points
10
Prerequisites Prerequisites
MGSM960 or MMBA8160
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.
More importantly, this unit focuses on transforming business processes through big data and data science, the impact on companies’ IT infrastructure, the use of resources to conduct data science workstreams, and identifying the necessary technological underpinnings of big data ecosystem.
The unit is especially tailored for MBA students and business managers with a primary focus on managerial discussions surrounding big data employment and decision making, using big data and analytics insights within large companies. The technical aspect of the unit is on a level comprehensible and applicable to MBA students who do not necessarily possess technical training in big data software applications.

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.

Assessment Tasks

Name Weighting Hurdle Due
Group Assignment 30% No 02/09/22
Individual Assignment 30% No 29/07/22
Final Examination 30% No University Exam Period
Class contribution 10% No 30/09/22

Group Assignment

Assessment Type 1: Project
Indicative Time on Task 2: 20 hours
Due: 02/09/22
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.

Individual Assignment

Assessment Type 1: Modelling task
Indicative Time on Task 2: 20 hours
Due: 29/07/22
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: University Exam Period
Weighting: 30%

 

A closed book three 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: 30/09/22
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.

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

  • In MMBA8113 - Big Data and Managerial Decisions, for indicidual assignments , technology such as Azure Machine Leraning Studio and Microsoft Power BI will be used
  • Recommended text book is Big Data MBA (2016), Bill Schmarzo. Wiley Publishing, ISBN (Hardcover): 978-1119181118

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:

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.

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:

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 2022.04 of the Handbook