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Unit of study_

QBUS6810: Statistical Learning and Data Mining

Semester 1, 2020 [Normal day] - Camperdown/Darlington, Sydney

It is now common for businesses to have access to very rich information data sets, often generated automatically as a by-product of the main institutional activity of a firm or business unit. Data Mining deals with inferring and validating patterns, structures and relationships in data, as a tool to support decisions in the business environment. This unit offers an insight into the main statistical methodologies for the visualization and the analysis of business and market data. It provides the tools necessary to extract information required for specific tasks such as credit scoring, prediction and classification, market segmentation and product positioning. Emphasis is given to business applications of data mining using modern software tools.

Unit details and rules

Unit code QBUS6810
Academic unit Business Analytics
Credit points 6
ECMT5001 or QBUS5001 or BUSS6002
Assumed knowledge


Available to study abroad and exchange students


Teaching staff

Coordinator Peter Radchenko,
Type Description Weight Due Length
Final exam Final exam
50% Formal exam period 1.5 hours
Outcomes assessed: LO1 LO3 LO2
In-semester test Mid-semester exam
20% Mid-semester exam period 1 hour
Outcomes assessed: LO1 LO3 LO2
Assignment group assignment Group project
Statistical analysis and report
30% Week 13 15 pages
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7
group assignment = group assignment ?

Assessment summary

  • Group project: The group project will examine your understanding of the concepts presented in the unit. The assignment task will measure your knowledge of supervised learning methods, your skill in applying and evaluating these methods using Python, and your ability to communicate the results and conclusions in a professional way. The assignment must be done in groups of up to five students. This assignment will help you develop valuable communication and collaboration skills and allow you to contextualise your statistical learning skills on real applied problems.
  • Mid-semester exam and Final exam: The exam questions will test your understanding of the essential characteristics of the methods covered and your ability to make informed comparisons of the methods, to correctly interpret and analyse statistical results and to formulate substantive conclusions based on these results. The questions will also cover the theoretical aspects of the material.

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


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 statistical learning (notation; key concepts and problems in statistical learning; statistical decision theory; model assessment). Tutorial: Introduction to Python. Lecture and tutorial (3 hr) LO1 LO5
Week 02 Linear regression (least-squares estimation; interpreting a linear regression model; residual diagnostics; data transformation; categorical predictors; polynomial regression). Tutorial: Introduction to Python. Lecture and tutorial (3 hr) LO1 LO3 LO5
Week 03 Regression modelling (the Gaussian MLR model; statistical inference; K-nearest neighbors regression) Lecture and tutorial (3 hr) LO1 LO3 LO5
Week 04 Model selection (validation set; cross-validation, analytical criteria). Lecture and tutorial (3 hr) LO1 LO3 LO5
Week 05 Maximum likelihood estimation. Subset selection methods in linear regression. Lecture and tutorial (3 hr) LO1 LO3
Week 07 Variable selection and regularization in linear regression (Ridge regression; Lasso; Elastic Net). Lecture and tutorial (3 hr) LO1 LO3 LO5
Week 08 Classification methods I ( introduction to decision theory for classification; k-nearest neighbors classifier; review of the Bayes’ rule; Naive Bayes classifier; decision theory for binary classification) Lecture and tutorial (3 hr) LO1 LO3 LO5
Week 09 Classification methods II (evaluating classification models; logistic regression; Gaussian discriminant analysis). Lecture and tutorial (3 hr) LO1 LO3 LO5
Week 10 Nonlinear modelling (polynomial regression; regression splines; splines; local regression; generalized additive models). Lecture and tutorial (3 hr) LO1 LO3 LO5
Week 11 Tree-based methods I (regression trees; classification trees). Lecture and tutorial (3 hr) LO1 LO3 LO5
Week 12 Tree-based methods II (bagging; random forests; boosting). Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5
Week 13 Ensemble learning and discussion. Lecture and tutorial (3 hr) LO1 LO3

Attendance and class requirements

Lecture recordings: All lectures 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

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

Primary textbook

James, G., Witten, D., Hastie, T. and Tibshirani, R., 2013. An Introduction to Statistical Learning. New York: Springer.

Advanced textbook

Students with sufficient background in statistics can consider progressing to more advanced reading, after studying the primary textbook.

Friedman, J., Hastie, T. and Tibshirani, R., 2009. The Elements of Statistical Learning. Second Edition. Springer, Berlin: Springer Series in Statistics.

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. know the statistical theory required for business data mining and data analysis
  • LO2. identify which statistical tool is most relevant for specific business analytic tasks
  • LO3. identify the advantages and limitations of each method
  • LO4. extract information from large volumes of data readily available from the business environment
  • LO5. obtain and interpret a meaningful analytical result using a software package such as Python
  • LO6. work productively in a team
  • LO7. present and write about findings effectively.

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.

Since this unit was last offered, a mid-semester exam replaced online quizzes in the assessments.

Python and Jupyter Notebook

Python is the computational engine that will allow us to bring the content of the unit into practice in a sophisticated way. Python is a free and open source general purpose programming language that has become the language of data science (together with R). It has powerful data manipulation, statistics, machine learning, data visualisation, and scientific libraries. Python is simple to learn and use and is supported by a large community of users in industry and academia.

More information can be found on Canvas.


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.