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.

Details

Academic unit	Business Analytics
Unit code	QBUS6600
Unit name	Data Analytics for Business Capstone
Session, year ?	Semester 2, 2022
Attendance mode	Normal day
Location	Camperdown/Darlington, Sydney
Credit points	6

Enrolment rules

Prohibitions ?	None
Prerequisites ?	Completion of 24 credit points of units towards the Data Analytics for Business specialisation (including QBUS5001 and BUSS6002)
Corequisites ?	None
Assumed knowledge ?	Students should complete this unit in their final semester of study
Available to study abroad and exchange students	No

Teaching staff and contact details

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:
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:
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:
= group assignment ?

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 sydney.edu.au/students/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.

Special consideration

If you experience short-term circumstances beyond your control, such as illness, injury or misadventure or if you have essential commitments which impact your preparation or performance in an assessment, you may be eligible for special consideration or special arrangements.

Academic integrity

The Current Student website provides information on academic honesty, academic dishonesty, 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 dishonesty or plagiarism seriously.

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

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	Python essentials. 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. 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
Learning outcomes

Closing the loop

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

Additional information

Business School Policies

Key dates
Key dates through the academic year, including teaching periods, census, payment deadlines and exams.
Student administration
Enrolment, course planning, fees, graduation, support services, student IT
Expectations of student conduct
Code of Conduct for Students, Conditions of Enrollment, University Privacy Statement, Academic Integrity
Academic appeals
Academic appeals process, special consideration, rules and guidelines, advice and support
Learning and teaching policy
Policy register, policy search
Financial support
Scholarships, interest free loans, bursaries, money management
Study resources
Learning Centre, faculty and school programs, Library, online resources
Health and support
Student Centre, counselling & psychological services, University Health Service, general health and wellbeing

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

Details

Enrolment rules

Teaching staff and contact details

Assessment

Assessment criteria

Late submission

Special consideration

Academic integrity

Weekly schedule

Study commitment

Learning outcomes

Graduate qualities

Outcome map

Closing the loop

Additional information

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect

Current students

QBUS6600: Data Analytics for Business Capstone

Overview

Details

Enrolment rules

Teaching staff and contact details

Assessment

Assessment criteria

Late submission

Special consideration

Academic integrity

Weekly schedule

Study commitment

Learning outcomes

Graduate qualities

Outcome map

Closing the loop

Additional information

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect