Students

BUSA7090 – Data and Visualisation for Business

2024 – Session 1, In person-scheduled-weekday, North Ryde

General Information

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Unit convenor and teaching staff Unit convenor and teaching staff
Credit points Credit points
10
Prerequisites Prerequisites
Admission to MRes
Corequisites Corequisites
Co-badged status Co-badged status
BUSA8090
Unit description Unit description

This unit prepares students for the world where "data is the new oil". Numerous business case studies are treated in depth so students emerge with a clear understanding of the "unreasonable effectiveness of data". It examines big data technologies, moving from relational databases through to Hadoop and parallel processing. Along the way students will develop the skills necessary to tease data out of relational databases using SQL. Data visualisation is also a focus of the unit, which treats its analysis and design as well as its implementation using a variety of open source and commercial tools. Students will develop research methodology skills throughout the unit.

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: Devise programming language code for data analytics and visualisation using a variety of computer tools.
  • ULO2: Formulate SQL language approaches to relational database problems.
  • ULO3: Assemble statistical learning techniques to tackle data science problems.
  • ULO4: Examine and employ a variety of data visualisation techniques.
  • ULO5: Evaluate various popular data visualisation solutions.
  • ULO6: Evaluate and critique a debate in data science and visualisation.

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
Literature review 40% No Friday of week 7
Assignment 2 30% No Friday of Week 13
Assignment 1 30% No Exam week

Literature review

Assessment Type 1: Literature review
Indicative Time on Task 2: 20 hours
Due: Friday of week 7
Weighting: 40%

 

A paper of up to 5,000 words covering a topic in data science and visualisation that has been agreed with the unit co-ordinator.

 
On successful completion you will be able to:
  • Evaluate and critique a debate in data science and visualisation.

Assignment 2

Assessment Type 1: Modelling task
Indicative Time on Task 2: 20 hours
Due: Friday of Week 13
Weighting: 30%

 

Practical coding assignment using data visualisation packages.

 
On successful completion you will be able to:
  • Devise programming language code for data analytics and visualisation using a variety of computer tools.
  • Assemble statistical learning techniques to tackle data science problems.
  • Examine and employ a variety of data visualisation techniques.
  • Evaluate various popular data visualisation solutions.

Assignment 1

Assessment Type 1: Programming Task
Indicative Time on Task 2: 20 hours
Due: Exam week
Weighting: 30%

 

Practical coding assignment using SQL.

 
On successful completion you will be able to:
  • Devise programming language code for data analytics and visualisation using a variety of computer tools.
  • Formulate SQL language approaches to relational database problems.

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 unit will cover knowledge in four areas:

1. Data analysis/governance in business (Week1&Week12)

2. Relational database design and management (Week2-Week6)

3. Data visualisation using R (Week7-9)

4. Data visualisation using Tableau (Week 10-12)

A systematic review and revision of all knowledge covered in the semester will be carried out in Week 13.

Four streams are operated for this subject.

Friday 1PM-4PM (Dr Elias Maroun)

Wednesday 9AM-12PM (Dr Geng Sun)

Wednesday 1PM-PM (Dr Geng Sun)

Tuesday 9AM-12PM (Dr Geng Sun)

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 connect.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 the Service Connect Portal, 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 2024.01R of the Handbook