Outline

Overview

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 DATA2902 or ECMT2110
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Marcel Scharth, marcel.scharth@sydney.edu.au

Assessment

The census date for this unit availability is 31 August 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written exam	Final exam Written exam	50%	Formal exam period	2 hours	AI prohibited
Outcomes assessed:
Practical skill	Homework 1 Problem set	10%	Week 05 Due date: 04 Sep 2026 at 23:59	TBD	AI allowed
Outcomes assessed:
Practical skill	Homework 2 Problem set	10%	Week 09 Due date: 09 Oct 2026 at 23:59	TBD	AI allowed
Outcomes assessed:
Data analysis	Group Project Data analysis and report	30%	Week 13 Due date: 06 Nov 2026 at 23:59 Closing date: 16 Nov 2026	TBD	AI allowed
Outcomes assessed:
= hurdle task ? = group assignment ?

Assessment summary

Group Project: In groups of three, students undertake an investigation of a business problem using real data and the inferential methods developed in this unit. The task comprises problem formulation; exploratory data analysis; the selection and justification of appropriate inferential methods; the fitting, checking, and refinement of models; the conduct and interpretation of statistical inference; and the preparation of a written report. The report presents the problem, the data, the analysis, the model, the statistical findings, and the conclusions, and provides a considered discussion of the limitations of the approach.
Homeworks: This assessment consists of two problem sets. This assessment is designed to assess and improve students' analytical 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 (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.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

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 University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Learning support

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.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	The Inference Stack	Lecture (2 hr)
Week 01	From Predictions to Business Actions	Tutorial (2 hr)
Week 02	Probability and Information	Lecture (2 hr)
Week 02	Probability and Information	Tutorial (2 hr)
Week 03	Maximum Likelihood	Lecture (2 hr)
Week 03	Maximum Likelihood	Tutorial (2 hr)
Week 04	Bayesian Inference	Lecture (2 hr)
Week 04	Bayesian Inference	Tutorial (2 hr)
Week 05	Monte Carlo Methods	Lecture (2 hr)
Week 05	Monte Carlo Methods	Tutorial (2 hr)
Week 07	Markov Chain Monte Carlo	Lecture (2 hr)
Week 07	Markov Chain Monte Carlo	Tutorial (2 hr)
Week 08	Causal Inference I: Causal Effects	Lecture (2 hr)
Week 08	Causal Inference I: Causal Effects	Tutorial (2 hr)
Week 09	Experiments I: A/B Testing as Business Experimentation	Lecture (2 hr)
Week 09	Experiments I: A/B Testing as Business Experimentation	Tutorial (2 hr)
Week 10	Experiments II: Adaptive Experimentation	Lecture (2 hr)
Week 10	Experiments II: Adaptive Experimentation	Tutorial (2 hr)
Week 11	Causal Inference II: Observational Studies	Lecture (2 hr)
Week 11	Causal Inference II: Observational Studies	Tutorial (2 hr)
Week 12	Causal Inference III: Heterogeneous Treatment Effects	Lecture (2 hr)
Week 12	Causal Inference III: Heterogeneous Treatment Effects	Tutorial (2 hr)
Week 13	Amortised Inference and Foundation Models	Lecture (2 hr)
Week 13	Amortised Inference and Foundation Models	Tutorial (2 hr)

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

There are no required textbooks. Comprehensive materials are provided weekly and are self-contained: everything examinable is covered in these materials. The readings below are optional, for students who wish to go further.

Start here. These are readable and well-matched to the unit's approach.

Richard McElreath (2020), Statistical Rethinking, 2nd edition, CRC Press (Weeks 4–6).
Nick Huntington-Klein (2025), The Effect: An Introduction to Research Design and Causality, 2nd edition, CRC Press. Free online at https://theeffectbook.net (Weeks 7, 10–11).
Ron Kohavi, Diane Tang and Ya Xu (2020), Trustworthy Online Controlled Experiments, Cambridge University Press (Weeks 8–9).

Technical. More demanding references for students considering honours or seeking deeper knowledge.

Larry Wasserman (2004), All of Statistics, Springer (Weeks 2–3).
Bradley Efron and Trevor Hastie (2016), Computer Age Statistical Inference, Cambridge University Press. Free at https://hastie.su.domains/CASI/ (Weeks 3–6).
Andrew Gelman et al. (2013), Bayesian Data Analysis, 3rd edition, CRC Press (Weeks 4-6).
Miguel Hernán and James Robins (2020), Causal Inference: What If, Chapman & Hall/CRC. Free at https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/ (Weeks 7, 10–11).
Aleksandrs Slivkins (2019), Introduction to Multi-Armed Bandits. Available at https://arxiv.org/abs/1904.07272 (Week 9).
Selected papers, introduced in the relevant lectures.

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. 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
Learning outcomes

Responding to student feedback

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

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

Additional information

Disclaimer

Important: the University of Sydney regularly reviews units of study and reserves the right to change the units of study available annually. To stay up to date on available study options, including unit of study details and availability, refer to the relevant handbook.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

QBUS3830: Advanced Analytics

Semester 2, 2026 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links