Skip to main content
Unit of study_

QBUS1040: Foundations of Business Analytics

Semester 1, 2024 [Normal day] - Camperdown/Darlington, Sydney

This unit provides students with the necessary foundations and skills to undertake second year units in business analytics and successfully complete the Business Analytics major. Theoretical models discussed are motivated by real-life business applications and decision problems. The unit provides a grounding in linear algebra (matrix properties) and calculus and applies these methods to regression models with multiple variables. Topics covered include logistic regression, interaction and nonlinear effects. The unit also introduces the key ideas of optimization (particularly for quadratic problems) and shows how optimisation models can be used to make statistical estimates. At the same time as building the understanding of the mathematical foundations needed in business analytics, the unit helps students to build programming skills to solve practical problems from the business area. The unit makes use of modern programming languages such as Python.

Unit details and rules

Unit code QBUS1040
Academic unit Business Analytics
Credit points 6
Prohibitions
? 
None
Prerequisites
? 
BUSS1020 or DATA1001 or ECMT1010 or ENVX1001 or ENVX1002 or STAT1021 or ((MATH1005 or MATH1015) and MATH1115) or 6 credit points of MATH 1000-level units which must include MATH1905
Corequisites
? 
None
Assumed knowledge
? 

None

Available to study abroad and exchange students

Yes

Teaching staff

Coordinator Alison Wong, a.wong@sydney.edu.au
Type Description Weight Due Length
Supervised exam
? 
Final exam
Paper-based exam.
45% Formal exam period 2 hours
Outcomes assessed: LO1 LO2 LO3
Assignment Assignment 1
Covers weeks 1-3 and assumed knowledge. #earlyfeedbacktask
10% Week 03
Due date: 08 Mar 2024 at 23:59
Details to be provided on Canvas
Outcomes assessed: LO1
Supervised test
? 
Mid-semester exam
Paper-based exam. Exact date will be announced on Canvas.
25% Week 07
Due date: 14 Apr 2024 at 12:10
80 minutes
Outcomes assessed: LO1 LO2
Assignment Assignment 2
Covers weeks 4-8.
10% Week 08
Due date: 19 Apr 2024 at 23:59
Details to be provided on Canvas
Outcomes assessed: LO1
Assignment Assignment 3
Covers weeks 9-13.
10% Week 13
Due date: 24 May 2024 at 23:59
Details to be provided on Canvas
Outcomes assessed: LO1 LO2 LO3

Early feedback task

This unit includes an early feedback task, designed to give you feedback prior to the census date for this unit. Details are provided in the Canvas site and your result will be recorded in your Marks page. It is important that you actively engage with this task so that the University can support you to be successful in this unit.

Assessment summary

  • Assignments: These will be regular homework tasks with some exam-style questions and Python programming questions.
    • Assignment 1 serves as an early feedback task to provide students with feedback prior to the census date.
  • Mid-semester exam: This exam will cover content from weeks 1-6 inclusive. It will include short answer, calculation, proof, Python comprehension, and Python code questions.
  • Final exam: This exam will cover all unit content. The exam will include short answer, calculation, proof, written response, Python comprehension and Python code questions.

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name

Mark range

Description

High distinction

85 - 100

Awarded when you demonstrate the learning outcomes for the unit at an exceptional standard, as defined by grade descriptors or exemplars outlined by your faculty or school. 

Distinction

75 - 84

Awarded when you demonstrate the learning outcomes for the unit at a very high standard, as defined by grade descriptors or exemplars outlined by your faculty or school.

Credit

65 - 74

Awarded when you demonstrate the learning outcomes for the unit at a good standard, as defined by grade descriptors or exemplars outlined by your faculty or school.

Pass

50 - 64

Awarded when you demonstrate the learning outcomes for the unit at an acceptable standard, as defined by grade descriptors or exemplars outlined by your faculty or school. 

Fail

0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see sydney.edu.au/students/guide-to-grades.

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

  • Deduction of 5% of the maximum mark for each calendar day after the due date.
  • After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

Late homework will not be accepted unless special consideration is obtained.

Academic integrity

The Current Student website  provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.  

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy 2023 reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy 2023. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

WK Topic Learning activity Learning outcomes
Week 01 Introduction and vectors Lecture and tutorial (4 hr) LO1
Week 02 Linear functions Lecture and tutorial (4 hr) LO1
Week 03 Norm and distance Lecture and tutorial (4 hr) LO1
Week 04 Clustering Lecture and tutorial (4 hr) LO1
Week 05 Linear independence Lecture and tutorial (4 hr) LO1
Week 06 Matrices and linear equations Lecture and tutorial (4 hr) LO1
Week 07 Matrix multiplication Lecture and tutorial (4 hr) LO1
Week 08 Matrix inverses Lecture and tutorial (4 hr) LO1
Week 09 Least squares Lecture and tutorial (4 hr) LO1 LO2
Week 10 Least squares data fitting Lecture and tutorial (4 hr) LO1 LO2
Week 11 Multi-objective least squares and regularisation Lecture and tutorial (4 hr) LO1 LO2
Week 12 Optimisation and Lagrange multipliers Lecture and tutorial (4 hr) LO1 LO2 LO3
Week 13 Constrained least squares Lecture and tutorial (4 hr) LO1 LO2 LO3

Attendance and class requirements

Lecture recordings: All lectures are recorded and will be available on Canvas for student use. Please note the Business School does not own the system and cannot guarantee that the system will operate or that every class will be recorded. Students should ensure they attend and participate in all classes.

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

All readings for this unit can be accessed through the Library eReserve, available on Canvas.

  • Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares, Stephen Boyd and Lieven Vandenberghe, Cambridge University Press.
  • Python Language Companion to Introduction to Applied Linear Algebra: Vectors, Matrices and Least Squares, Jessica Leung and Dmytro Matsypura, Sydney University.

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

At the completion of this unit, you should be able to:

  • LO1. use linear algebra in various applications
  • LO2. solve least squares regression and data-fitting problems
  • LO3. understand how optimisation models can be used to make statistical estimates.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1 Depth of disciplinary expertise

Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.

GQ2 Critical thinking and problem solving

Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.

GQ3 Oral and written communication

Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.

GQ4 Information and digital literacy

Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.

GQ5 Inventiveness

Generating novel ideas and solutions.

GQ6 Cultural competence

Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.

GQ7 Interdisciplinary effectiveness

Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.

GQ8 Integrated professional, ethical, and personal identity

An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.

GQ9 Influence

Engaging others in a process, idea or vision.

Outcome map

Learning outcomes Graduate qualities
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

This section outlines changes made to this unit following staff and student reviews.

No changes have been made since this unit was last offered.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.