Outline

Overview

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, 2021
Attendance mode	Normal day
Location	Remote
Credit points	6

Enrolment rules

Prohibitions ?	None
Prerequisites ?	(ECMT5001 or QBUS5001 or STAT5003)
Corequisites ?	(BUSS6002 or COMP5310 or COMP5318)
Available to study abroad and exchange students	Yes

Teaching staff and contact details

Coordinator	Peter Radchenko, peter.radchenko@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam (Record+)	Final exam N/A	50%	Formal exam period	1.5 hours
Outcomes assessed:
In-semester test (Record+)	Mid-semester exam The exact date of the exam will be announced on Canvas during the semester.	20%	Week 07 Due date: 19 Apr 2021 at 17:00	50 minutes
Outcomes assessed:
Assignment	Group project Statistical analysis and report	30%	Week 13 Due date: 04 Jun 2021 at 17:00	15 pages
Outcomes assessed:
= group assignment ? = Type B final 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.
Mid-semester exam and Final exam: 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, to 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.

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 statistical learning (notation; key concepts and problems in statistical learning; statistical decision theory; model assessment)	Lecture (2 hr)
Week 01	Introduction to Python	Tutorial (1 hr)
Week 02	Linear regression (least-squares estimation; interpreting a linear regression model; residual diagnostics; data transformation; categorical predictors; polynomial regression)	Lecture (2 hr)
Week 02	Introduction to Python	Tutorial (1 hr)
Week 03	Regression modelling (the Gaussian MLR model; statistical inference; K-nearest neighbors regression)	Lecture (2 hr)
Week 03	Regression modelling	Tutorial (1 hr)
Week 04	Model selection (validation set; cross-validation, analytical criteria)	Lecture (2 hr)
Week 04	Linear regression and k-nearest neighbors	Tutorial (1 hr)
Week 05	Maximum likelihood estimation. Subset selection methods in linear regression.	Lecture (2 hr)
Week 05	Statistical learning fundamentals	Tutorial (1 hr)
Week 06	Variable selection and regularization in linear regression (Ridge regression; Lasso; Elastic Net)	Lecture (2 hr)
Week 06	Model selection	Tutorial (1 hr)
Week 07	Classification methods I ( introduction to decision theory for classification; k-nearest neighbors classifier; review of the Bayes’ rule; Naive Bayes classifier)	Lecture (2 hr)
Week 07	Variable selection and regularization in linear regression	Tutorial (1 hr)
Week 08	Classification methods II (decision theory for binary classification; evaluating classification models; logistic regression; Gaussian discriminant analysis)	Lecture (2 hr)
Week 08	Naïve Bayes and sentiment analysis	Tutorial (1 hr)
Week 09	Classification methods II (decision theory for binary classification; evaluating classification models; logistic regression; Gaussian discriminant analysis)	Lecture (2 hr)
Week 09	Classification methods	Tutorial (1 hr)
Week 10	Nonlinear modelling (polynomial regression; regression splines; splines; local regression; generalized additive models)	Lecture (2 hr)
Week 10	Regression splines	Tutorial (1 hr)
Week 11	Tree-based methods I (regression trees; classification trees)	Lecture (2 hr)
Week 11	Tree-based methods for classification	Tutorial (1 hr)
Week 12	Tree-based methods II (bagging; random forests; boosting)	Lecture (2 hr)
Week 12	Model stacking	Tutorial (1 hr)
Week 13	Ensemble learning and discussion	Lecture (2 hr)
Week 13	Statistical learning methods	Tutorial (1 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.

Required readings

All readings for this unit can be accessed through 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

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. 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
Learning outcomes

Closing the loop

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

Additional information

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.

Self-study

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

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

Required readings

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

QBUS6810: Statistical Learning and Data Mining

Overview

Details

Enrolment rules

Teaching staff and contact details

Assessment

Assessment criteria

Late submission

Special consideration

Academic integrity

Weekly schedule

Study commitment

Required readings

Learning outcomes

Graduate qualities

Outcome map

Closing the loop

Additional information

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect