Students
BUSA8000 – Techniques in Business Analytics
2021 – Session 2, Special circumstances
Session 2 Learning and Teaching Update
The decision has been made to conduct study online for the remainder of Session 2 for all units WITHOUT mandatory on-campus learning activities. Exams for Session 2 will also be online where possible to do so.
This is due to the extension of the lockdown orders and to provide certainty around arrangements for the remainder of Session 2. We hope to return to campus beyond Session 2 as soon as it is safe and appropriate to do so.
Some classes/teaching activities cannot be moved online and must be taught on campus. You should already know if you are in one of these classes/teaching activities and your unit convenor will provide you with more information via iLearn. If you want to confirm, see the list of units with mandatory on-campus classes/teaching activities.
Visit the MQ COVID-19 information page for more detail.
General Information
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| Unit convenor and teaching staff |
Unit convenor and teaching staff
Unit Convenor
Ken Siu
|
|---|---|
| Credit points |
Credit points
10
|
| Prerequisites |
Prerequisites
ECON6034 or ECON634 or admission to MBusAnalytics
|
| 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.
- ULO3: Implement statistical learning algorithms in R.
- 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.
General Assessment Information
Assessment criteria for all assessment tasks will be provided on the unit iLearn site.
It is the responsibility of students to view their marks for each within-session-assessment on iLearn within 20 days of posting. If there are any discrepancies, students must contact the unit convenor immediately. Failure to do so will mean that queries received after the release of final results regarding assessment tasks (not including the final exam mark) will not be addressed.
Late submissions of assessments
Sometimes unavoidable circumstances occur that might prevent you from submitting an assessment on time and, in that case, you may be eligible to lodge a Special Consideration request.
Unless a Special Consideration request has been submitted and approved, please note that no extensions to assessment deadlines will be granted. Assessments that are submitted late will attract a late penalty:
- There will be a deduction of 10% of the total available marks for each 24 hour period or part thereof that the submission is late.
- No assessment will be accepted more than 72 hours after the original due date and time (incl. weekends).
- No late submissions will be accepted for timed assessments (e.g., quizzes, online tests) or for tasks with a weighting of 10% or less.
Assessment Tasks
| Name | Weighting | Hurdle | Due |
|---|---|---|---|
| Online Quiz 1 | 10% | No | Week 6 |
| Report | 20% | No | Week 8 |
| Online Quiz 2 | 10% | No | Week 10 |
| Final Examination | 60% | No | University Examination Period |
Online Quiz 1
Assessment Type 1: Quiz/Test
Indicative Time on Task 2: 5 hours
Due: Week 6
Weighting: 10%
Students will be given a dataset and required to perform various calculations based on the techniques taught in classes.
On successful completion you will be able to:
- Apply methods for handling data in R.
- Implement statistical learning algorithms in R.
Report
Assessment Type 1: Case study/analysis
Indicative Time on Task 2: 15 hours
Due: Week 8
Weighting: 20%
Students will be presented with a selection of case studies and given a report scope. 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.
- Implement statistical learning algorithms in R.
Online Quiz 2
Assessment Type 1: Quiz/Test
Indicative Time on Task 2: 5 hours
Due: Week 10
Weighting: 10%
Students will complete some multiple choice and/or short answer questions.
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.
Final Examination
Assessment Type 1: Examination
Indicative Time on Task 2: 20 hours
Due: University Examination Period
Weighting: 60%
An open book two hour online exam will be held in the University Examination Period.
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.
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
Learning and Teaching Activities
The content for each week will consist of a lecture and a tutorial. Please visit https://timetables.mq.edu.au/2021/ for more information about classes. Details will also be available on iLearn. It is the responsibility of individual students to stay up to date with the unit material.
Lectures
A lecture recording will be available for each week. Lecture notes and other related material will be available in the week of the lecture, at the iLearn weekly tabs.
Tutorials
Tutorials are considered compulsory but no marks are allocated. The tutorial schedule will be made available on iLearn. If a student is unable to attend the regular class, it is expected they will watch the recording and keep up to date with the content.
Students are expected to complete the ‘Tutorial Questions’ as a self-directed study activity before attending a tutorial. Where applicable, solutions will be posted at the end of the tutorial.
Recommended Texts
1. R for Data Science, Wickham and Grolemund
2. An Introduction to Statistical Learning, James et al.
3. Collaborative Statistics, Illowsky and Dean
These are all open-source textbooks and are available freely and legally online.
Technology Used and Required
Calculator
A calculator will be required during the Final Examination. Note: students are expected to clearly show all steps (working) in their solutions to 'calculation' questions.
Non-programmable calculators with no text-retrieval functionality are permitted. Calculators that have a full alphabet on the keyboard are not permitted. Graphics calculators are not permitted. Calculators need the following minimum functionality: xy or ^, 1/x and log or ln functions, and a memory. Non-programmable financial calculators are permitted but it is not a requirement to use a financial calculator.
Students are assumed to already be familiar with the basic operation of their calculator prior to the start of this unit.
Computing
Prior to the start of this unit, students are expected to be familiar at least with the basic operation of their computing device.
Software
This unit does use R in its first portion. Whilst it is not strictly necessary that students have any background using R, it will certainly be beneficial.
Knowledge of Mathematics and Statistics
A background of basic mathematics and statistics is assumed. Students entering the unit should be familiar with basic calculus, as well as concepts such as expected value, variance, and standard deviation.
Unit Schedule
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:
- Academic Appeals Policy
- Academic Integrity Policy
- Academic Progression Policy
- Assessment Policy
- Fitness to Practice Procedure
- Grade Appeal Policy
- Complaint Management Procedure for Students and Members of the Public
- Special Consideration Policy
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
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.
Unit information based on version 2021.03 of the Handbook