Outline

Overview

This unit highlights the importance of statistical methods and tools for today's managers and analysts and demonstrates how to apply these methods to business problems using real-world data. The quantitative skills that students learn in this unit are useful in all areas of business. Through taking this unit students learn how to model and analyse the relationships within business data; how to identify the appropriate statistical technique in different business environments; how to compute statistics by hand and using special purpose software; how to interpret results in the context of the business problem; and how to forecast using business data. The unit is taught through data-driven examples, exercises and business case studies.

Unit details and rules

Academic unit	Business Analytics
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	ECMT5001 or QBUS5002
Assumed knowledge ?	Students should be capable of reading data in tabulated form and working with Microsoft EXCEL and doing High School level of mathematics
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Tony Shang, ce.shang@sydney.edu.au
Lecturer(s)	Erick Li, erick.li@sydney.edu.au
Lecturer(s)	Tony Shang, ce.shang@sydney.edu.au
Tutor(s)	Yves Tam, yves.tam@sydney.edu.au

Assessment

The census date for this unit availability is 1 September 2025

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written exam ?	Final exam Closed book and pen-and-paper exam.	50%	Formal exam period	2 hours	AI prohibited
Outcomes assessed:
Out-of-class quiz	In-semester Quizes See descriptions on Canvas	10%	Multiple weeks	30 minutes	AI allowed
Outcomes assessed:
Written test ?	Mid-semester exam Closed book and pen-and-paper exam.	25%	Week 07 Due date: 21 Sep 2025 at 15:40	1.5 hours	AI prohibited
Outcomes assessed:
Practical skill	Group Assignment See descriptions on Canvas	15%	Week 12	20 pages / 2 weeks	AI allowed
Outcomes assessed:
= hurdle task ? = group assignment ?

Assessment summary

Small Continuous Quizzes: This assessment consists of multiple online quizzes, contributing a total of 10% to the final mark. Microsoft Excel may be required for data analysis. Replacement quizzes will not be provided. Detailed arrangements will be announced on Canvas.

Group Assignment: This task is to be completed by groups of 3-4 students. A detailed report is to be submitted in Week 12.

Mid-Semester Exam: This is a closed-book, pen-and-paper test scheduled for Week 7, to be completed within 90 minutes. It covers topics from Modules 1 to 6. A formula sheet will be provided.

Final Exam: This is a closed-book exam scheduled during the final exam period, to be completed in 120 minutes. It covers all topics taught in the unit. A formula sheet will be provided.

The final exam is a hurdle task, requiring students to meet a minimum standard to pass. Students must achieve at least 40% on the final exam. Failing to meet this standard, even with an overall aggregate mark above 50%, will result in a Fail grade for the unit. The academic transcript will display a Fail grade and the exact mark attained if it is within the 0-49 range. For all other marks, the transcript will show a Fail grade with a capped moderated mark of 49.

Details for each assessment task are available on Canvas.

All quizzes and exams are individual assessments. Students are strongly encouraged to engage in collaborative learning within small study groups to enhance their learning outcomes.

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.

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:

Any assessment submitted after the due time and date (or extended due time and date) will incur a late penalty of 5% of the total marks per 24-hour period, or part thereof, late. Note that this penalty is applied to the mark gained after the submitted work is marked.

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	Data and Statistics	Lecture (2 hr)
Week 01	Data and Statistics	Workshop (2 hr)
Week 02	Probability and Random Variables	Lecture (2 hr)
Week 02	Probability and Random Variables	Workshop (2 hr)
Week 03	Discrete Probability Distributions	Lecture (2 hr)
Week 03	Discrete Probability Distributions	Workshop (2 hr)
Week 04	Continuous Probability Distributions	Lecture (2 hr)
Week 04	Continuous Probability Distributions	Workshop (2 hr)
Week 05	Sampling distribution	Lecture (2 hr)
Week 05	Sampling distribution	Workshop (2 hr)
Week 06	Confidence interval estimation	Lecture (2 hr)
Week 06	Confidence interval estimation	Workshop (2 hr)
Week 07	Practice for Mid-semester Exam	Workshop (2 hr)
Week 07	Revision for Mid-semester Exam	Lecture (2 hr)
Week 08	Hypothesis testing	Lecture (2 hr)
Week 08	Hypothesis testing	Workshop (2 hr)
Week 09	Two population inference	Lecture (2 hr)
Week 09	Two population inference	Workshop (2 hr)
Week 10	Linear Regression Analysis I	Lecture (2 hr)
Week 10	Linear Regression Analysis I	Workshop (2 hr)
Week 11	Linear Regression Analysis II	Lecture (2 hr)
Week 11	Linear Regression Analysis II	Workshop (2 hr)
Week 12	Linear Regression Analysis III	Lecture (2 hr)
Week 12	Linear Regression Analysis III	Workshop (2 hr)
Week 13	Revision for Final Exam	Lecture (2 hr)
Week 13	Revision for Final Exam	Workshop (2 hr)

Attendance and class requirements

There are three parts to the unit:

Self-paced study modules on Canvas website
Zoom or in-person Q&A (see Canvas announcement)
Tutorials (see your schedule)

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

Selvanathan, E. A., Selvanathan, S and Keller, G. (2021) Business Statistics, Australia and New Zealand 8th Edition. Cengage Learning, Australia

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. build a strong quantitative skill set for business decision making; create statistical models for studying relationship amongst business variables; demonstrate proficiency in the use of statistical software for quantitative modelling
LO2. evaluate underlying theories, concepts, assumptions and arguments in business related fields
LO3. identify problems within real-world constraints and collect data for decision making; manage, analyse, evaluate and use information efficiently and effectively; demonstrate coherent arguments when recommending solutions
LO4. communicate confidently and coherently to a professional standard both orally and in writing
LO5. defend data integrity; analyse data and report results professionally and ethically.

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.

There is a lot of content to cover in an introductory-level statistics unit, and it can sometimes feel overwhelming for students when it's all packed into one semester. For 2025 S2, the main goal is to strike a balance between providing detailed content and making it digestible. To enhance the learning experience, we have made several changes: Key Points Only: Focus on the most crucial concepts and key points in the lecture slides. This approach will help avoid overwhelming students with too much information at once. Supplementary Notes: Provide detailed lecture notes or handouts separately, which students can review at their own pace. This allows the slides to remain clear and focused while still offering comprehensive information. Practice Questions: Create a repository of practice questions that students can access. These questions will cover a range of difficulty levels to help students build their skills progressively. Interactive Elements: Incorporate interactive elements such as polls, quizzes, and group discussions during lectures and Q&A sessions to keep students engaged and ensure they are following along. These changes aim to make the course more manageable and engaging for students, helping them to better understand and retain the material.

Additional information

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

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

QBUS5001: Foundation in Data Analytics for Business

Semester 2, 2025 [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