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Unit of study_

QBUS6840: Predictive Analytics

Semester 1, 2021 [Normal day] - Camperdown/Darlington, Sydney

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
(QBUS5001 or ECMT5001 or STAT5003) and (BUSS6002 or COMP5310 or COMP5318)
Assumed knowledge


Available to study abroad and exchange students


Teaching staff

Coordinator Thang Bui,
Lecturer(s) Thang Bui,
Type Description Weight Due Length
Final exam (Record+) Type B final exam Final exam
MCQ and written answer questions
50% Formal exam period 2 hours
Outcomes assessed: LO1 LO2 LO3 LO4
Assignment Homework
Written report and submitted online
20% Week 07
Due date: 23 Apr 2021 at 16:00

Closing date: 30 Apr 2021
15 pages
Outcomes assessed: LO1 LO2 LO3 LO4
Assignment group assignment Group assignment
Quantitative data analysis and report
30% Week 13
Due date: 04 Jun 2021 at 16:00

Closing date: 11 Jun 2021
25 pages
Outcomes assessed: LO1 LO2 LO3 LO4
group assignment = group assignment ?
Type B final exam = Type B final exam ?

Assessment summary

  • Group assignment: This task will assess material covered throughout the whole unit. It should be done in groups of 5. This assignment will help to develop valuable communication and collaboration skills and allow students to contextualise their predictive analytics skills on a real, applied problem of their choice. The group project will have a peer and academic assessment component.
  • Homework: This assignment will assess material covered in weeks 1 to 6 of this unit. This assignment will help to develop students' basic predictive analytics skills on synthetic and possible real applied problems, including data visualisation, model building, programming, and analysis in terms of understanding in theory and practices with raw data.
  • Final exam: The exam will assess all aspects of this unit from weeks 1-12. It will primarily test how familiar the students are with all the materials, including some programming basics covered in this unit.

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


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. 


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.


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.


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. 


0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see

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.

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 forecasting Lecture and tutorial (3 hr)  
Week 02 Time series components, prediction errors, and measures Lecture and tutorial (3 hr)  
Week 03 Time series decomposition Lecture and tutorial (3 hr)  
Week 04 Time series regression Lecture and tutorial (3 hr)  
Week 05 Forecasting with exponential smoothing Lecture and tutorial (3 hr)  
Week 06 Forecasting with exponential smoothing (seasonal data) Lecture and tutorial (3 hr)  
Week 07 Autoregressive integrated moving average (ARIMA) 1 Lecture and tutorial (3 hr)  
Week 08 Autoregressive integrated moving average (ARIMA) 2 Lecture and tutorial (3 hr)  
Week 09 Forecasting with deep neural networks for cross-sectional data Lecture and tutorial (3 hr)  
Week 10 Forecasting with deep neural networks for time series data Lecture and tutorial (3 hr)  
Week 11 Forecasting with recurrent neural networks Lecture and tutorial (3 hr)  
Week 12 Case study and Revision Lecture and tutorial (3 hr)  

Attendance and class requirements

Lecture recordings: All lectures and seminars 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".

All readings for this unit can be accessed through the Library eReserve, available on Canvas.

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. select and use the appropriate technique to analyse the structure of multivariate data, especially when individual data points are identified as belonging to different classes
  • LO2. apply multivariate data techniques using a training data set to predict classifications for real data
  • LO3. understand the characteristics of time-series data in order to analyse real business data of this form
  • LO4. select and use an appropriate technique to predict the future behaviour of business variables of interest, including the prediction of discrete outcomes.

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

This section outlines changes made to this unit following staff and student reviews.

No changes have been made since this unit was last offered.

Software: Python is available in all the computer labs in the Business School Codrington Building (H69). Students are encouraged to use their own computer/laptop. Students can use software other than the recommended Python (for example R), but no intensive technical support will be guaranteed due to available resources. Excel is incapable of running many of the analyses required in this unit.

More information can be found on Canvas.


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