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Unit outline_

QBUS6600: Data Analytics for Business Capstone

Semester 2, 2022 [Normal day] - Camperdown/Darlington, Sydney

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 24 credit points of units towards the Data Analytics for Business specialisation (including QBUS5001 and BUSS6002)
Corequisites
? 
None
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 Andrey Vasnev, andrey.vasnev@sydney.edu.au
Type Description Weight Due Length
Assignment Individual assignment 1
Exploratory data analysis project and report.
30% Week 06
Due date: 05 Sep 2022 at 23:59
Page limit: 12.
Outcomes assessed: LO1 LO3 LO4 LO5 LO7
Assignment group assignment Group assignment
Data analysis project, report, and presentation
40% Week 11
Due date: 17 Oct 2022 at 23:59
Page limit: 15.
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7
Assignment Individual assignment 2
Individual report reflecting on the work done throughout the semester.
30% Week 13
Due date: 04 Nov 2022 at 23:59
Page limit: 6.
Outcomes assessed: LO1 LO3 LO4 LO5 LO7
group assignment = group assignment ?

Assessment summary

  • Individual assignment 1: You will conduct exploratory data analysis of the dataset provided by one of the industry partners. 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 partner.
  • 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 posed by the industry partner. You will describe the methods you used to prioritize your earlier insights and defend the models and results with supporting evidence. The group report will contain an executive summary for decision makers or business audience.
  • 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.

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 to the unit and the industry partners. Lecture (1.5 hr) LO1
Data understanding and data cleaning. Tutorial (1.5 hr) LO4 LO5
Week 02 The data analytic workflow and statistical/machine learning fundamentals. Lecture (1.5 hr) LO1 LO2
Exploratory data analysis. Tutorial (1.5 hr) LO4 LO5
Week 03 Exploratory data analysis and feature engineering. Lecture (1.5 hr) LO1 LO2 LO3 LO4
Linear regression and feature engineering. Tutorial (1.5 hr) LO4 LO5
Week 04 Clustering. Introduction to Classification. Lecture (1.5 hr) LO1 LO2 LO3 LO4
Clustering. Tutorial (1.5 hr) LO4 LO5
Week 05 Logistic regression. Decision theory and model evaluation for binary classification. Lecture (1.5 hr) LO1 LO2 LO3 LO4
Logistic regression. Tutorial (1.5 hr) LO4 LO5
Week 06 Model Selection. Lecture (1.5 hr) LO1 LO2 LO3 LO4 LO5
Model selection. Tutorial (1.5 hr) LO4 LO5
Week 07 Regularization in linear regression. Lecture (1.5 hr) LO1 LO2 LO3 LO4
Regularization in linear regression. Tutorial (1.5 hr) LO4 LO5
Week 08 Classification and regression trees. Lecture (1.5 hr) LO1 LO2 LO3 LO4
Classification and regression trees. Tutorial (1.5 hr) LO4 LO5
Week 09 Tree-based methods (regression). Lecture (1.5 hr) LO1 LO2 LO3 LO4
Tree-based methods for regression (random forests, boosting). Tutorial (1.5 hr) LO4 LO5
Week 10 Tree-based methods (classification). Lecture (1.5 hr) LO1 LO2 LO3 LO4
Tree-based methods for classification (random forests, boosting). Tutorial (1.5 hr) LO4 LO5
Week 11 Guest lectures. Lecture (1.5 hr) LO1
Python essentials. Introduction to LaTeX. Tutorial (1.5 hr) LO4 LO5
Week 12 Guest lectures. Lecture (3 hr) LO1
Week 13 Guest lectures. Lecture (3 hr) LO1

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. 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. Demonstrate a deep understanding of various statistical learning methods and identify the advantages and limitations of each method
  • LO4. Build a strong Data Analytics skill set for business decision making
  • LO5. Demonstrate proficiency in the use of Data Analytics software such as Python and R for implementing a Data Analytic project
  • LO6. Work productively, collaboratively and collegially in a team
  • LO7. Communicate the Data Analytics findings efficiently

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.

Adding ULOs and Assessments as endorsed by Education Committee 8 July 2021

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.