QBUS6840: Semester 2, 2022

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Units QBUS6840 Semester 2 2022 [Normal day]

Unit of study_

QBUS6840: Predictive Analytics

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

Overview

To be effective in a competitive business environment, a business analyst needs to be able to use predictive analytics to translate information into decisions and to convert information about past performance into reliable forecasts. An effective analyst also should be able to identify the analytical tools and data structures to anticipate market trends. In this unit, students gain skills required to succeed in today's highly analytical and data-driven economy. The unit introduces the basics of data management, business forecasting, decision trees, logistic regression, and predictive modelling. The unit features corporate case studies and hands-on exercises to demonstrate the concepts presented.

Unit details and rules

Unit code	QBUS6840
Academic unit	Business Analytics
Credit points	6
Prohibitions ?	None
Prerequisites ?	(QBUS5001 or ECMT5001 or STAT5003) and (BUSS6002 or COMP5310 or COMP5318)
Corequisites ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Chao Wang, chao.wang@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam (Record+)	Final exam Exam	50%	Formal exam period	2 hours
Outcomes assessed:
In-semester test (Record+)	Mid-Semester Exam In-semester Exam	25%	Week 08 Due date: 20 Sep 2022 at 16:00	1.5 hours
Outcomes assessed:
Assignment	Group assignment Assignment	25%	Week 13	TBD
Outcomes assessed:
= group assignment ? = Type B final exam ? = Type B in-semester exam ?

Assessment summary

Group assignment: This task will assess material covered throughout the whole unit. This assignment will help to develop valuable communication and collaboration skills and allow students to contextualise their predictive analytics skills on real and applied problems.

Mid-semester exam: The mid-term exam will assess material covered from week 1 to week 7 (or week 8) of this unit.

Final exam: The final exam will assess all aspects of this unit from week 1 to week 13.

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 sydney.edu.au/students/guide-to-grades.

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.

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.

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 Predictive Analytics	Lecture and tutorial (3 hr)
Week 02	Time Series Analysis I	Lecture and tutorial (3 hr)
Week 03	Time Series Analysis II	Lecture and tutorial (3 hr)
Week 04	Time Series Analysis III	Lecture and tutorial (3 hr)
Week 05	Forecasting with Exponential Smoothing I	Lecture and tutorial (3 hr)
Week 06	Forecasting with Exponential Smoothing II	Lecture and tutorial (3 hr)
Week 07	AutoRegressive Integrated Moving Average (ARIMA) Model I	Lecture and tutorial (3 hr)
Week 08	AutoRegressive Integrated Moving Average (ARIMA) Model II	Lecture and tutorial (3 hr)
Week 09	AutoRegressive Integrated Moving Average (ARIMA) Model III	Lecture and tutorial (3 hr)
Week 10	Predictive Analytics with Neural Networks and Deep Learning I	Lecture and tutorial (3 hr)
Week 11	Predictive Analytics with Neural Networks and Deep Learning II	Lecture and tutorial (3 hr)
Week 12	Predictive Analytics with Neural Networks and Deep Learning III	Lecture and tutorial (3 hr)
Week 13	Summary and Revision	Lecture and tutorial (3 hr)

Attendance and class requirements

Lecture recordings: Lecture and tutorial are recorded and will be available on Canvas for student use. 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.

Required readings

Rob J Hyndman and George Athanasopoulos "Forecasting: principles and practice".

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. Select and use the appropriate technique to analyse the structure of time series data
LO2. Analyse real business data considering the characteristics of time-series data.
LO3. Demonstrate proficiency in the use of statistical software, e.g. Python, for the implementation of predictive models
LO4. Select and use an appropriate technique to predict the future behaviour of business variables of interest, including the prediction of discrete outcomes.
LO5. Work productively and collaboratively in a team

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 changes have been made since this unit was last offered.

Additional information

Software: Python is the recommended programing language for the unit. Python is available in all the computer labs in the Business School Codrington Building (H69). Students are encouraged to use their own computer/laptop.

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

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

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