Students

BUSA8000 – Techniques in Business Analytics

2024 – Session 2, In person-scheduled-weekday, North Ryde

General Information

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Unit convenor and teaching staff Unit convenor and teaching staff Senior Lecturer
Narges Safari
narges.safari@mq.edu.au
510
Credit points Credit points
10
Prerequisites Prerequisites
BUSA6004 or ECON6034 or (Admission to MBusAnalytics or MActPrac)
Corequisites Corequisites
Co-badged status Co-badged status
Unit description Unit description

This unit develops some of the core skills needed for the practice of modern business analytics. Statistical inference and associated statistical computing will be covered along with an introduction to analytical techniques needed for working with both structured and unstructured data. The reporting of the results from quantitative style research will also be studied.

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: Articulate the importance and application of data in a variety of contexts.
  • ULO2: Apply methods for handling data in R or suitable language.
  • ULO3: Implement statistical learning algorithms in R or suitable language.
  • ULO4: Apply appropriate statistical methods/models, and perform analysis on various types of data and interpret the result.
  • ULO5: Understand and apply the principles of statistical inference.
  • ULO6: Successfully work in teams to achieve group and organizational objectives

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
Assessment 1 20% No Week 05
Online Quiz 1 20% No Week 08
Assessment 2 20% No Week 11
Group Project 40% No Week 13

Assessment 1

Assessment Type 1: Case study/analysis
Indicative Time on Task 2: 10 hours
Due: Week 05
Weighting: 20%

 

Students will be given a dataset/business case and required to perform various analyses based on the techniques taught in classes. Details will be provided on iLearn.

 
On successful completion you will be able to:
  • Articulate the importance and application of data in a variety of contexts.
  • Apply methods for handling data in R or suitable language.
  • Implement statistical learning algorithms in R or suitable language.

Online Quiz 1

Assessment Type 1: Quiz/Test
Indicative Time on Task 2: 5 hours
Due: Week 08
Weighting: 20%

 

Students will complete some multiple-choice and/or short-answer questions

 
On successful completion you will be able to:
  • Apply methods for handling data in R or suitable language.
  • Implement statistical learning algorithms in R or suitable language.

Assessment 2

Assessment Type 1: Case study/analysis
Indicative Time on Task 2: 10 hours
Due: Week 11
Weighting: 20%

 

Students will be given a dataset/business case and required to perform various analyses based on the techniques taught in classes. Details will be provided on iLearn.

 
On successful completion you will be able to:
  • Apply appropriate statistical methods/models, and perform analysis on various types of data and interpret the result.
  • Understand and apply the principles of statistical inference.

Group Project

Assessment Type 1: Project
Indicative Time on Task 2: 20 hours
Due: Week 13
Weighting: 40%

 

Students will work on a group project.

 
On successful completion you will be able to:
  • Apply appropriate statistical methods/models, and perform analysis on various types of data and interpret the result.
  • Understand and apply the principles of statistical inference.
  • Successfully work in teams to achieve group and organizational objectives

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

The lectures and tutorials are delivered face-to-face. For more information, please refer to iLearn.

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 2024.06 of the Handbook