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Unit outline_

QBUS3830: Advanced Analytics

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

This unit is designed to equip students with advanced tools for estimation and testing in relevant business statistical models. In particular, the unit covers maximum likelihood, Bayesian estimation and inference, and hypothesis testing. The unit acknowledges the importance of learning computing skills as helpful for job applications and special emphasis is made throughout the unit to learn numerical methods such as Monte Carlo simulations and Bootstrapping. Special topics in advanced statistical modelling, such as nonlinear estimators and time series regression, are also covered. The materials taught are essential as preparation for honours in Quantitative Business Analysis.

Unit details and rules

Academic unit Business Analytics
Credit points 6
Prerequisites
? 
QBUS2810 or DATA2002 or ECMT2110
Corequisites
? 
None
Prohibitions
? 
None
Assumed knowledge
? 

None

Available to study abroad and exchange students

Yes

Teaching staff

Coordinator Chao Wang, chao.wang@sydney.edu.au
Lecturer(s) Minh-Ngoc Tran, minh-ngoc.tran@sydney.edu.au
Type Description Weight Due Length
Final exam (Record+) Type B final exam Final exam
Written exam
50% Formal exam period 2 hours
Outcomes assessed: LO1 LO2
Assignment Homework (2 sets)
Math-derivation and programming (week 4 and 9 respectively)
20% Multiple weeks TBD
Outcomes assessed: LO1 LO2 LO6
Assignment group assignment Group Project
Data analysis and report
30% Week 13 TBD
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6
group assignment = group assignment ?
Type B final exam = Type B final exam ?

Assessment summary

  • Group Project: In groups of 3, students are required to work on a business-related problem that can be solved via the use of real data and the analytical methods taught in this unit, conduct the process of forming and refining the problem, exploratory analysis of data, choosing the appropriate statistical model, refining it, analysing the data with it, conducting associated estimation and inference analysis, prepare a report that presents a summary of their problem, analysis, model, statistical findings and conclusions, as well as discusses any limitations.
  • Homeworks: This assessment consists of two problem sets. This assessment is designed to assess and improve students' analytical and programming skills.
  • Final exam: This exam will examine all unit content from weeks 1-13 inclusive.

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 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.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

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 and preparation Lecture and tutorial (3 hr)  
Week 02 Theory of maximum likelihood estimation Lecture and tutorial (3 hr)  
Week 03 Foundation of statistical hypothesis testing Lecture and tutorial (3 hr)  
Week 04 Introduction to Bayesian statistics Lecture and tutorial (3 hr)  
Week 05 Monte Carlo methods Lecture and tutorial (3 hr)  
Week 06 Monte Carlo methods Lecture and tutorial (3 hr)  
Week 07 Monte Carlo methods Lecture and tutorial (3 hr)  
Week 08 Bayesian computation with Variational Bayes Lecture and tutorial (3 hr)  
Week 09 Bayesian computation with Variational Bayes Lecture and tutorial (3 hr)  
Week 10 Generalised linear models (GLM) Lecture and tutorial (3 hr)  
Week 11 Generalised linear models (GLM) Lecture and tutorial (3 hr)  
Week 12 Overview of neural networks and deep learning Lecture and tutorial (3 hr)  
Week 13 Revision Lecture and tutorial (3 hr)  

Attendance and class requirements

Lecture recordings: All lectures and tutorials are recorded and will be available on Canvas for student use.

 

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

There are no required textbooks. Comprehensive lecture slides will be given weekly.


Recommended readings (instructions on which/when to read will be given along the course):

  1. For mathematical foundation of statistics (Weeks 1-3): Robert Hogg, Joeseph McKean and Allen Craig. Introduction to Mathematical Statistics, 7th Edition, Pearson, 2015
  2. For Bayesian statistics and Monte Carlo methods (Weeks 4-9): Andrew Gelman, John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Donald Rubin. Bayesian Data Analysis, Chapman & Hall/CRC
  3. For GLM modelling (Weeks 10-11): John Fox. Applied regression analysis and generalized linear models, 3rd edition, Sage Publications, 2015
  4. For statistical learning (Weeks 12): Hastie, Tibshirani and Friedman (2009). The Elements of Statistical Learning, Springer-Verlag. Freely available at http://statweb.stanford.edu/~tibs/ElemStatLearn/

 

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. demonstrate the understanding of the underlying theory for advanced analysis of data arising in business contexts
  • LO2. choose with success the most appropriate and relevant statistical tools for solving the business analytic problem of interest
  • LO3. identify with accuracy and communicate the positives as well as the limitations of a range of analytical methods
  • LO4. demonstrate an ability to extract relevant information from large volumes of business-related data available online
  • LO5. demonstrate a high level of competence in statistical literacy and communicating the results of your analyses
  • LO6. demonstrate proficiency in the use of at least one statistical software package: Matlab, R or Python.

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

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

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

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.