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Unit outline_

QBUS3600: Business Analytics in Practice

Semester 1, 2022 [Normal day] - Remote

This capstone unit bridges the gap between theory and practice by integrating knowledge and consolidating key skills developed across the Business Analytics major. The problem-based approach to learning in this unit offers vital tools and techniques for business decision makers in the big data era through the use of very large and rich data sources. The unit casts the knowledge of statistical learning in a modern machine learning context and exposes business students to a range of state-of-the-art machine learning topics with the emphasis on applications involving the analysis of business data. Machine Learning is a fundamental aspect of business analytics that automates analytical modelling and decision making. Students ensure their career-readiness by demonstrating their ability to apply concepts, theories, methodologies, and programming skills to authentic problems and challenges faced in the field of business analytics.

Unit details and rules

Academic unit Business Analytics
Credit points 6
Student commencing from 2018: completion of at least 120 credit points including QBUS2310, QBUS2810 and QBUS2820. 2018 continuing students: completion of at least 120 credit points including QBUS2310 and QBUS2810
Assumed knowledge

All other requirements for the major or program associated with this capstone must be completed prior to or concurrently with (if enrolment rules permit) this unit of study. Capstones must be completed at the University of Sydney Business School only.

Available to study abroad and exchange students


Teaching staff

Coordinator Ali Yaseen,
Lecturer(s) Ali Yaseen,
Tutor(s) Michael Arnold,
Suman Saha,
Type Description Weight Due Length
Assignment Individual Assignment (Individual Report)
Final Individual Report
30% Formal exam period
Due date: 06 Jun 2022 at 17:00

Closing date: 13 Jun 2022
2000+ words
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO7
Assignment Individual Assignment
Exploratory Data Analysis
30% Week 06
Due date: 01 Apr 2022 at 17:00

Closing date: 08 Apr 2022
15 pages
Outcomes assessed: LO1 LO7 LO6 LO5 LO4 LO3 LO2
Assignment group assignment Group project
Written task and oral presentation
40% Week 12
Due date: 20 May 2022 at 17:00

Closing date: 27 May 2022
25 pages
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7
group assignment = group assignment ?

Assessment summary

  • Individual Assignment 1: Students will individually complete a written report based on real-world data sets, including but not limited to a technical report or feasibility study, and an executive summary or briefing. The assignment questions will be based on the knowledge covered in weeks 1-6.
  • Group project: The group assignment will be an applied machine learning or data science project on a real-world or simulated business problem. Students will incorporate machine learning, statistical learning, visualisation and presentation techniques from prior units of study. The report is due in week 12 and the presentation is tentatively scheduled for week 13.
  • Individual Assignment 2 (Report): This written report will record and reflect what students will have done in the entire semester and the process of completing the group project. The report will also propose what in the group project can be further explored with their foreseeable benefits for the industry. 

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


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. 


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.


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.


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. 


0 - 49

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

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:

The late penalty for the assignment is 5% of the assigned mark per day, starting after 5 pm on the due date.

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.

WK Topic Learning activity Learning outcomes
Week 01 Introduction Lecture (3 hr)  
Week 02 Machine learning foundation Lecture (3 hr)  
Week 03 Features and Model Selection Lecture (3 hr)  
Week 04 Model Selection in Practice and Clustering Lecture (3 hr)  
Week 05 Guest Lecture Seminar (3 hr)  
Week 06 Decision Trees Lecture (3 hr)  
Week 07 Bagging and Boosting Lecture (3 hr)  
Week 08 Project Q&A Seminar (3 hr)  
Week 09 Gradient Boosting Lecture (3 hr)  
Week 10 Neural Networks Lecture (3 hr)  
Week 11 Guess Lecture or Deep Neural Networks Lecture (3 hr)  
Week 12 Presentation Rehearsal or Guest Lecture Workshop (3 hr)  
Week 13 Final Presentation Workshop (3 hr)  

Attendance and class requirements

Lecture recordings: All lectures and tutorials are recorded and will be available on Canvas for student use. Guest lectures may not be recorded if a speaker does not give consent. 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.

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. demonstrate a deep understanding of different types of learning algorithms and identify the advantages and limitations of each method
  • LO2. build a strong machine learning skill set for business decision making
  • LO3. create machine learning models for studying relationships amongst business variables
  • LO4. work with various data sets and identify problems within real-world constraints
  • LO5. demonstrate proficiency in the use of statistical software, e.g. Python, for machine learning models implementation
  • LO6. work productively and collaboratively in a team
  • LO7. present and write insights and suggestions effectively, professionally and ethically.

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

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

No content changes have been made since this unit was last offered, but the final exam is not an assessment item from this offer.


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

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