Late submissions of assessments  

Unless a Special Consideration request has been submitted and approved, no extensions will be granted. There will be a deduction of 10% of the total available assessment-task marks made from the total awarded mark for each 24-hour period or part thereof that the submission is late. Late submissions will only be accepted up to 96 hours after the due date and time.

 

No late submissions will be accepted for timed assessments – e.g., quizzes, online tests.

 

Table 1: Penalty calculation based on submission time

Submission time after the due date (including weekends)	Penalty (% of available assessment task mark)	Example: for a non-timed assessment task marked out of 30
< 24 hours	10%	10% x 30 marks = 3-mark deduction
24-48 hours	20%	20% x 30 marks = 6-mark deduction
48-72 hours	30%	30% x 30 marks = 9-mark deduction
72-96 hours	40%	40% x 30 marks = 12-mark deduction
> 96 hours	100%	Assignment won’t be accepted

 

 

Special Consideration

To request an extension on the due date/time for a timed or non-timed assessment task, you must submit a Special Consideration application. An application for Special Consideration does not guarantee approval.

The approved extension date for a student becomes the new due date for that student. The late submission penalties above then apply as of the new due date

Recommended Textbook: 

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

Optional: 

2.       HBR Guide to Data Analytics Basics for Managers (2018), Harvard Business Review Press, ISBN (Hardcover): 978-1633694286

3.         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

Where to purchase textbook?

Springer Global Website – Online store: This textbook is also available for order via the publisher’s online store. For information on textbook prices and online ordering, please refer to the Springer Global Website online store at https://www.springer.com/gp/book/9783319135021.

eBook disclaimer for open book exams: As notebook computers, iPads, tablets, PDAs and similar are not allowed in the exam room, the eBook version available for this textbook which would require a student to bring in a notebook computer, iPad, tablet, PDA or a similar device in order to view it, will not be allowed in the exam room. Students are advised to only get the hard copy version of the required text.

Disclaimer: MGSM does not take responsibility for the stock levels of required textbooks from preferred retail outlets and other book retailers. While we advise our preferred book retail outlet, The Co-op Bookshop, of our maximum expected number of students purchasing specific  required text each term, The Co-op Bookshop and other book retailers will make their own judgement in regard to their physical holding stock levels. To prevent disappointment if a  textbook is out-of-stock, we highly advise students to order their textbooks as early as possible, or if the required textbook is currently out-of-stock, place an order with the book retailer as soon as possible so that these book retailers can monitor demand and supply, and adjust their stock orders accordingly.

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.

 

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