Outline

Overview

This unit serves as the capstone unit for the Data Analytics for Business specialisation. The unit's teaching and learning framework consists of problem-based teaching with practical application, challenging students to apply their data analytics skills to real-world business problems. The unit allows students to link theory to practice by integrating knowledge and key consolidating skills, that students have developed throughout the specialisation. Work-integrated learning and career-readiness outcomes are a focus of this unit where students utilize data analytics techniques and skills, together with business knowledge, assisting in business decision making via professional practice.

Unit details and rules

Academic unit	Business Analytics
Credit points	6
Prerequisites ?	Completion of 18 credit points of units towards the Data Analytics for Business specialisation (including QBUS5001 and BUSS6002)
Corequisites ?	Completion of 6 credit points towards the Data Analytics for Business specialisation
Prohibitions ?	None
Assumed knowledge ?	Students should complete this unit in their final semester of study
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Peter Radchenko, peter.radchenko@sydney.edu.au

Type	Description	Weight	Due	Length
Assignment	Individual assignment 1 Exploratory data analysis project and report.	30%	Week 06 Due date: 27 Mar 2023 at 23:59	Page limit: 12.
Outcomes assessed:
Assignment	Group assignment Data analysis project, report, and presentation	40%	Week 11 Due date: 08 May 2023 at 23:59	Page limit: 15.
Outcomes assessed:
Assignment	Individual assignment 2 Individual report reflecting on the work done throughout the semester.	30%	Week 13 Due date: 26 May 2023 at 23:59	Page limit: 6.
Outcomes assessed:
= group assignment ?

Assessment summary

Individual assignment 1: You will conduct exploratory data analysis of the dataset corresponding to one of our industry projects. Your aim is to reveal all relevant properties, characteristics, and patterns hidden in the dataset, and to describe your findings in the written report. You will use the results of your analysis in the subsequent group assignment towards the final goal of addressing the questions posed by the industry partners and collaborators.
Group assignment: Your task as a group is to synthesise the insights discovered in the first assignment and to use statistical/machine learning modeling tools to address the questions relevant to your industry project. You will also use the results from your analysis to outline a strategy and provide recommendations to the management team corresponding to your industry project. The written report will contain an executive summary for decision makers or business audience. Your group will also record and submit a presentation of your findings.
Individual assignment 2: In this assignment, you will reflect on what you have learnt from the semester-long data analysis project involving a real-world dataset. You will document your experiences, reflect on the difficulties you faced, and discuss what you could have done differently. You will summarise your experience with the group project, think critically about the analysis and the findings, and offer constructive recommendations/suggestions.

Further information for each assessment will be provided 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.

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 to the unit and the industry partners.	Lecture (1.5 hr)
Week 01	Data understanding and data cleaning.	Tutorial (1.5 hr)
Week 02	The data analytic workflow and statistical/machine learning fundamentals.	Lecture (1.5 hr)
Week 02	Exploratory data analysis.	Tutorial (1.5 hr)
Week 03	Exploratory data analysis and feature engineering.	Lecture (1.5 hr)
Week 03	Linear regression and feature engineering.	Tutorial (1.5 hr)
Week 04	Clustering. Introduction to Classification.	Lecture (1.5 hr)
Week 04	Clustering.	Tutorial (1.5 hr)
Week 05	Logistic regression. Decision theory and model evaluation for binary classification.	Lecture (1.5 hr)
Week 05	Logistic regression.	Tutorial (1.5 hr)
Week 06	Model Selection.	Lecture (1.5 hr)
Week 06	Model selection.	Tutorial (1.5 hr)
Week 07	Regularization in linear regression.	Lecture (1.5 hr)
Week 07	Regularization in linear regression.	Tutorial (1.5 hr)
Week 08	Classification and regression trees.	Lecture (1.5 hr)
Week 08	Classification and regression trees.	Tutorial (1.5 hr)
Week 09	Tree-based methods (regression).	Lecture (1.5 hr)
Week 09	Tree-based methods for regression (random forests, boosting).	Tutorial (1.5 hr)
Week 10	Tree-based methods (classification).	Lecture (1.5 hr)
Week 10	Tree-based methods for classification (random forests, boosting).	Tutorial (1.5 hr)
Week 11	Guest lectures.	Lecture (1.5 hr)
Week 11	Introduction to LaTeX.	Tutorial (1.5 hr)
Week 12	Guest lectures.	Lecture (3 hr)
Week 13	Guest lectures.	Lecture (3 hr)

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. Analyse and explain the role of data analytics and insights in business decision making
LO2. Operationally and efficiently cast a business problem into data analytics problems using a Business Data Analytics process
LO3. Apply data analytics skills to business decision making across different organisational areas
LO4. Demonstrate proficiency in the use of Data Analytics software such as Python and R for implementing a Data Analytic project
LO5. Work productively, collaboratively and collegially in a team
LO6. Communicate the Data Analytics findings efficiently
LO7. Discuss cultural and ethical dimensions in the data analytics context

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.

The ULOs are updated to better incorporate three GQs (GQ6, GQ7, GQ8). These are currently covered in the AOL mapping but not sufficiently explicit in the ULOs. We have also reviewed and streamlined the existing ULOs, and reduced some overlap (i.e. in the previous LO3 & LO4).

Additional information

Business School Policies

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

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

QBUS6600: Data Analytics for Business Capstone

Semester 1, 2023 [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

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