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Unit of study_

QBUS6810: Statistical Learning and Data Mining

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.

Details

Academic unit Business Analytics
Unit code QBUS6810
Unit name Statistical Learning and Data Mining
Session, year
? 
Semester 1, 2022
Attendance mode Normal day
Location Remote
Credit points 6

Enrolment rules

Prohibitions
? 
Prerequisites
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(ECMT5001 or QBUS5001 or STAT5003) and (BUSS6002 or COMP5310 or COMP5318)
Corequisites
? 
Available to study abroad and exchange students

Yes

Teaching staff and contact details

Coordinator Marcel Scharth, marcel.scharth@sydney.edu.au
Type Description Weight Due Length
Final exam (Record+) Type B final exam Final exam
Online exam
50% Formal exam period 2 hours
Outcomes assessed: LO1 LO2 LO3
In-semester test (Record+) Type B in-semester exam In-semester exam
Online exam
20% Week 08
Due date: 11 Apr 2022 at 16:00
50 minutes
Outcomes assessed: LO1 LO2 LO3
Assignment group assignment Group project
Data analysis project and report
30% Week 13
Due date: 27 May 2022 at 23:59
15 pages
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7
group assignment = group assignment ?
Type B final exam = Type B final exam ?
Type B in-semester exam = Type B in-semester exam ?
  • 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.
     
  • In-semester and final exams: the exam questions will test your understanding of the essential characteristics and theoretical properties of the methods covered. They will also test your ability to make informed comparisons of the methods,  correctly interpret and analyse statistical results and to formulate substantive conclusions based on these results. 

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.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

Late submission is not possible for certain aspects of the group assignment. A mark of zero will apply to the corresponding part of the assessment in this case.

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.

WK Topic Learning activity Learning outcomes
Week 01 Introduction to statistical learning Lecture (2 hr) LO1 LO2 LO3 LO4
Machine learning exploration Tutorial (1 hr) LO1 LO2 LO3 LO4 LO5
Week 02 Basic methods for regression Lecture (2 hr) LO1 LO2 LO3 LO4
Linear regression and k-nearest neighbours Tutorial (1 hr) LO1 LO2 LO3 LO4 LO5
Week 03 Feature engineering Lecture (2 hr) LO1 LO2 LO3 LO4
Feature engineering Tutorial (1 hr) LO1 LO2 LO3 LO4 LO5
Week 04 Basic methods for classification Lecture (2 hr) LO1 LO2 LO3 LO4
Logistic regression Tutorial (1 hr) LO1 LO2 LO3 LO4 LO5
Week 05 Machine learning fundamentals (decision theory, building blocks of learning algorithms, key concepts in machine learning) Lecture (2 hr) LO1 LO2 LO3 LO4
Machine learning fundamentals Tutorial (1 hr) LO1 LO2 LO3 LO4 LO5
Week 06 Practical methodology (model selection, hyperparameter optimisation, model stacking, evaluation) Lecture (2 hr) LO1 LO2 LO3 LO4
Model selection Tutorial (1 hr) LO1 LO2 LO3 LO4 LO5
Week 07 Variable selection and regularisation Lecture (2 hr) LO1 LO2 LO3 LO4
Regularisation methods Tutorial (1 hr) LO1 LO2 LO3 LO4 LO5
Week 08 Nonlinear modelling Lecture (2 hr) LO1 LO2 LO3 LO4
Generalised additive models Tutorial (1 hr) LO1 LO2 LO3 LO4 LO5
Week 09 Decision trees and random forests Lecture (2 hr) LO1 LO2 LO3 LO4
Classification trees Tutorial (1 hr) LO1 LO2 LO3 LO4 LO5
Week 10 Gradient boosting Lecture (2 hr) LO1 LO2 LO3 LO4
LightGBM, XGBoost and CatBoost Tutorial (1 hr) LO1 LO2 LO3 LO4 LO5
Week 11 Training machine learning models (part 1) Lecture (2 hr) LO1 LO2 LO3 LO4
Maximum likelihood Tutorial (1 hr) LO1 LO2 LO3
Week 12 Training machine learning models (part 2) Lecture (2 hr) LO1 LO2 LO3 LO4
Algorithms for optimisation Tutorial (1 hr) LO1 LO2 LO3 LO4 LO5
Week 13 Neural networks Lecture (2 hr) LO1 LO2 LO3 LO4
Introduction to PyTorch Tutorial (1 hr) LO1 LO2 LO3 LO4 LO5

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 Canvas.

Primary textbook

James, G., Witten, D., Hastie, T. and Tibshirani, R. (2021). An Introduction to Statistical Learning (Second Edition). New York: Springer.

Additional reading

Domingos, P. (2012). A Few Useful Things to Know about Machine Learning. Communications of the ACM, 55(10), 78-87.

Advanced textbook

Students with sufficient background in statistics can consider progressing to more advanced reading. 

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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9
No changes have been made since this unit was last offered.

Python

Python is a free and open-source general-purpose programming language that will allow us to bring the content of the unit into practice. Currently, Python is the most widely used language for machine learning and data science. 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.

You can find more information on Canvas.

Jupyter

The tutorials will make extensive use of Jupyter notebooks. A Jupyter Notebook is a web-based interactive document that combines live code, text and figures.  

We recommend JupyterLab as an integrated development environment for working with Jupyter notebooks. 

Cloud computing

Some tools used in this unit of study can have high computational requirements that exceed the capabilities of your current device. You should be ready to use a cloud environment such as Google Colab (free service) to run computationally intensive code as necessary. See Canvas for more options. 

Self-study

Students will be asked to study additional material beyond the lectures and tutorials.

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.