Skip to main content
Unit outline_

QBUS5001: Foundation in Data Analytics for Business

Semester 2, 2024 [Normal day] - Camperdown/Darlington, Sydney

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) Firouzeh Taghikhah, rosa.taghikhah@sydney.edu.au
Tony Shang, ce.shang@sydney.edu.au
Tutor(s) Yves Tam, yves.tam@sydney.edu.au
The census date for this unit availability is 2 September 2024
Type Description Weight Due Length
Supervised exam
? 
hurdle task
Final exam
Closed book and pen-and-paper exam.
50% Formal exam period 2 hours
Outcomes assessed: LO1 LO2 LO3 LO5
Small continuous assessment In-semester Quizes
See descriptions on Canvas
10% Multiple weeks 30 minutes
Outcomes assessed: LO1 LO5 LO3 LO2
Supervised test
? 
Mid-semester exam
Closed book and pen-and-paper exam.
25% Week 07
Due date: 14 Sep 2024 at 12:10
1.5 hours
Outcomes assessed: LO1 LO5 LO3 LO2
Assignment group assignment Group Assignment
See descriptions on Canvas
15% Week 12
Due date: 27 Oct 2024 at 23:59

Closing date: 03 Nov 2024
n/a
Outcomes assessed: LO1 LO5 LO4 LO3 LO2
hurdle task = hurdle task ?
group assignment = 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.

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 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.

Support for students

The Support for Students Policy 2023 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 2023. 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

WK Topic Learning activity Learning outcomes
Week 01 Theme 1 (All about data): Data fundamentals; Exploratory analysis - Summary Statistics Lecture (2 hr)  
Welcome and have fun with data Workshop (2 hr)  
Week 02 Theme 1 (All about data): Data fundamentals; Exploratory analysis - Summary Statistics Workshop (2 hr)  
Theme 2 (All about probability): Probability interpretation; Probability definitions Lecture (2 hr)  
Week 03 Theme 2 (All about probability): Probability interpretation; Probability definitions Workshop (2 hr)  
Theme 2 (All about probability): Probability and decisions; Monte Carlo example Lecture (2 hr)  
Week 04 Theme 2 (All about probability): Probability and decisions; Monte Carlo example Workshop (2 hr)  
Theme 2 (All about probability): Statistics - the bridge between probability and data Lecture (2 hr)  
Week 05 Theme 2 (All about probability): Statistics - the bridge between probability and data Workshop (2 hr)  
Theme 3 (All about inferences): Estimation and confidence interval Lecture (2 hr)  
Week 06 Theme 3 (All about inferences): Estimation and confidence interval Workshop (2 hr)  
Theme 3 (All about inferences): Hypothesis testing for single population Lecture (2 hr)  
Week 07 Theme 3 (All about inferences): Hypothesis testing for single population Workshop (2 hr)  
Theme 3 (All about inferences): Hypothesis testing for multiple populations Lecture (2 hr)  
Week 08 Theme 3 (All about inferences): Hypothesis testing for multiple populations Workshop (2 hr)  
Theme 4 (All about models): Simple regression- motivation and details Lecture (2 hr)  
Week 09 Theme 4 (All about models): Simple regression- motivation and details Workshop (2 hr)  
Theme 4 (All about models): Simple regression - using logs, dummy variables and model comparison Lecture (2 hr)  
Week 10 Theme 4 (All about models): Simple regression - using logs, dummy variables and model comparison Workshop (2 hr)  
Theme 4 (All about models): Multiple regression I Lecture (2 hr)  
Week 11 Theme 4 (All about models): Multiple regression I Workshop (2 hr)  
Theme 4 (All about models): Multiple regression II Lecture (2 hr)  
Week 12 Theme 4 (All about models): Multiple regression II Workshop (2 hr)  
Theme 4 (All about models): Hypothesis testing in regression Lecture (2 hr)  
Week 13 Theme 4 (All about models): Hypothesis testing in regression Workshop (2 hr)  
Final Exam Review Lecture (2 hr)  

Attendance and class requirements

There are three parts to the unit:

  1. Self-paced study modules on Canvas website
  2. Zoom or in-person Q&A (see Canvas announcement)
  3. 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 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. 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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

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 2024 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.

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.