Students

MMBA8113 – Big Data and Managerial Decisions

2020 – MGSM term 1, Weekday attendance, City

Coronavirus (COVID-19) Update

Due to the Coronavirus (COVID-19) pandemic, any references to assessment tasks and on-campus delivery may no longer be up-to-date on this page.

Students should consult iLearn for revised unit information.

Find out more about the Coronavirus (COVID-19) and potential impacts on staff and students

General Information

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Unit convenor and teaching staff Unit convenor and teaching staff Unit Convenor and Lecturer - PhD
Nejhdeh Ghevondian
nejhdeh.ghevondian@mq.edu.au
Angela Chow
angela.chow@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 the 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 underlaying 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 by 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:

  • ULO2: Explore Data Science theories, methodologies and tools and their practical applications to solve real life business problems.
  • ULO1: Develop a broad understanding and knowledge of the Big Data ecosystem and its applications within the context of managerial decision-making processes.
  • 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

Coronavirus (COVID-19) Update

Assessment details are no longer provided here as a result of changes due to the Coronavirus (COVID-19) pandemic.

Students should consult iLearn for revised unit information.

Find out more about the Coronavirus (COVID-19) and potential impacts on staff and students

General Assessment Information

Extensions and Penalties:

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 consideration is made and approved. No submission will be accepted after solutions/results/feedback have been posted.

Delivery and Resources

Coronavirus (COVID-19) Update

Any references to on-campus delivery below may no longer be relevant due to COVID-19.

Please check here for updated delivery information: https://ask.mq.edu.au/account/pub/display/unit_status

Prescribed Textbook:

  • Big Data MBA (2016), Bill Schmarzo. Wiley Publishing, ISBN (Hardcover): 978-1119181118

Recommended Textbooks:

  • HBR Guide to Data Analytics Basics for Managers (2018), Harvard Business Review Press, ISBN (Hardcover): 978-1633694286
  • Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking (2013), Foster Provost, O'Reilly Media, Inc, ISBN (Hardcover): 978-1449361327

Further sources of information:

Top academic management and information systems outlets (some suggestions)

  • Harvard business review
  • MIT Sloan Review
  • MIS Quarterly
  • Information Sciences
  • Information Systems Research

Useful academic databases (DB), search engines (SE), and publishers (PB)

  • Emerald Insight (DB)
  • Elsevier (DB)
  • Scopus (SE)
  • Web of Science (SE)
  • Wiley (PB)
  • Springer (PB)

Useful Industry databases

  • IBISWorld
  • Factiva
  • EBSCO Business Searching Interface

Access to Technology

Access to a personal computer and internet connection is required to access learning material/resources online on Macquarie University's online learning management system called iLearn.

iLearn - Your class online learning resources page

The class iLearn page for this unit is located at: https://ilearn.mq.edu.au/​. You must be enrolled in this class to see the class iLearn page.

Lecture Slides

Lecture Slides will be provided to students only in soft-copy format via the class iLearn page. You must be enrolled in this class to see these items in the class iLearn page.

Readings

Readings are provided to students only in soft-copy format via the class iLearn page. You must be enrolled in this class to see these items in the class iLearn page.

Email Use

It is University policy that the University issued email account will be used for official University communication. All students are required to access their University account frequently. Only contact Macquarie University staff (including tutors), using your official MQ student’s account because this is one method used to verify your identity.

Unit Schedule

Coronavirus (COVID-19) Update

The unit schedule/topics and any references to on-campus delivery below may no longer be relevant due to COVID-19. Please consult iLearn for latest details, and check here for updated delivery information: https://ask.mq.edu.au/account/pub/display/unit_status

Please only attend the class you are enrolled in as reflected in your e-Student account. This unit will be presented over 10 sessions as follows.

Class sessions are scheduled from: 6pm to 10pm of every Monday starting from 6 January 2020 (session 1) until 9 March 2020 (session 10). 
CBD campus location: Macquarie University City Campus (MUCC). Level 24, 123 Pitt Street, Sydney (please call the MUCC reception desk on (02) 9234 1700 for any problems entering the premises).

(The proposed program might be subject to some minor changes as the term progresses (TBA)).

Session Topics

1

Introduction to Big Data & Data Science

2

Big Data, Best Practices & Managerial Decisions

3

Fundamentals of Statistics

4

Exploratory Data Analysis

5

Introduction to Predictive Modelling – part 1

6

Introduction to Predictive Modelling – part 2

7

Visualisation & Story Telling

8

Big Data Architecture, Operationalisation & Model Management

9

Putting it Altogether – Big Data Business Strategy Roadmap

10

Group Assignment Presentation

Policies and Procedures

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:

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).

Student Code of Conduct

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

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

Student Support

Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/

Learning Skills

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. 

Student Services and Support

Students with a disability are encouraged to contact the Disability Service who can provide appropriate help with any issues that arise during their studies.

Student Enquiries

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

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.

Changes since First Published

Date Description
03/01/2020 Created sub-label for prescribed textbook to highlight which book is prescribed and which textbook/s are only recommended.
31/12/2019 Just changed the recommended textbooks required for students