Outline

Overview

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
Prerequisites ?	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
Corequisites ?	None
Prohibitions ?	None
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	No

Teaching staff

Coordinator	Ali Yaseen, ali.yaseen@sydney.edu.au
Lecturer(s)	Ali Yaseen, ali.yaseen@sydney.edu.au
Tutor(s)	Suman Saha, s.saha@sydney.edu.au
Tutor(s)	Michael Arnold, michael.arnold@sydney.edu.au

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:
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:
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:
= 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	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 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.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

Learning support

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.

Weekly schedule

WK	Topic	Learning activity
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

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.

Outcomes
Graduate qualities

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
Learning outcomes

Responding to student feedback

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.

Additional information

More information can be found on Canvas.

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.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect

Current students

QBUS3600: Business Analytics in Practice

Semester 1, 2022 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect