Outline

Overview

The expanding reservoir of data and the accompanying computational capabilities for its analysis are increasingly acknowledged as indispensable business assets. At its core, this unit delves into the foundational computational techniques that underpin modern data-driven decision-making processes. At the end of this course, students will be equipped with the ability to formulate practical problems in data analytics as probabilistic models, estimate the parameters of such models by solving basic optimisation problems, and implement the associated computational algorithms in Python, yielding software tailored for practical application with real datasets.

Unit details and rules

Academic unit	Business Analytics
Credit points	6
Prerequisites ?	None
Corequisites ?	QBUS5001 or QBUS5002
Prohibitions ?	None
Assumed knowledge ?	Basic mathematical knowledge, e.g., probability, linear algebra, and calculus.
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Wilson Chen, ye.chen@sydney.edu.au
Lecturer(s)	Wilson Chen, ye.chen@sydney.edu.au
	Manoj Thomas, manoj.thomas@sydney.edu.au
	Jiang Qian, jiang.qian@sydney.edu.au

Assessment

The census date for this unit availability is 31 March 2025

Details
Criteria

Type	Description	Weight	Due	Length
Supervised exam ?	Final exam Closed-book examination.	45%	Formal exam period	2 hours
Outcomes assessed:
Supervised test ?	Mid-semester exam Closed-book examination.	25%	Week 07 Due date: 12 Apr 2025 at 18:10	1 hour
Outcomes assessed:
Assignment	Individual Assignment Written and programming task.	30%	Week 12 Due date: 23 May 2025 at 23:59	3000 words (indicative only).
Outcomes assessed:
= AI allowed ?

Assessment summary

Mid-semester exam: This exam will assess all aspects of the unit from approximately the first half of the semester.

Individual Assignment: The assignment will require you to implement an applied data science project, which typically includes exploratory data analysis, model building, and model evaluation. The assignment will contain both written and programming components.

Final Exam: The final exam will assess all aspects of the unit from the entire semester.

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) and automated writing tools

Except for supervised exams or in-semester tests, you may use generative AI and automated writing tools in assessments unless expressly prohibited by your unit coordinator.

For exams and in-semester tests, the use of AI and automated writing tools is not allowed unless expressly permitted in the assessment instructions.

The icons in the assessment table above indicate whether AI is allowed – whether full AI, or only some AI (the latter is referred to as “AI restricted”). If no icon is shown, AI use is not permitted at all for the task. Refer to Canvas for full instructions on assessment tasks for this unit.

Your final submission must be your own, original work. You must acknowledge any use of automated writing tools or generative AI, and any material generated that you include in your final submission must be properly referenced. You may be required to submit generative AI inputs and outputs that you used during your assessment process, or drafts of your original work. Inappropriate use of generative AI is considered a breach of the Academic Integrity Policy and penalties may apply.

The Current Students website provides information on artificial intelligence in assessments. For help on how to correctly acknowledge the use of AI, please refer to the  AI in Education Canvas site.

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.

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 Science Capabilities	Lecture (2 hr)
Week 01	Python Fundamentals 1	Workshop (2 hr)
Week 02	Data Lifecycle	Lecture (2 hr)
Week 02	Python Fundamentals 2	Workshop (2 hr)
Week 03	Exploratory Analysis	Lecture (2 hr)
Week 03	Data Wrangling 1	Workshop (2 hr)
Week 04	Mathematical Foundations and Scientific Computing	Lecture (2 hr)
Week 04	Data Wrangling 2 and Visualisation 1	Workshop (2 hr)
Week 05	Cluster Analysis	Lecture (2 hr)
Week 05	Visualisation 2 and Clustering	Workshop (2 hr)
Week 06	Regression	Lecture (2 hr)
Week 06	Exploratory Analysis Reflection	Workshop (2 hr)
Week 07	Feature Engineering	Lecture (2 hr)
Week 07	Regression	Workshop (2 hr)
Week 08	Classification	Lecture (2 hr)
Week 08	Feature Engineering	Workshop (2 hr)
Week 09	Model Evaluation	Lecture (2 hr)
Week 09	Classification	Workshop (2 hr)
Week 10	Optimisation	Lecture (2 hr)
Week 10	Model Evaluation	Workshop (2 hr)
Week 11	Big Data Solutions	Lecture (2 hr)
Week 11	Optimisation	Workshop (2 hr)
Week 12	Assignment Focus Session	Lecture (2 hr)
Week 12	Optimisation	Workshop (2 hr)
Week 13	Revision	Lecture (2 hr)
Week 13	Revision	Workshop (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

All readings for this unit can be accessed through Library eReserve, available on Canvas.

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. Identify types and sources of data, data quality issues and interact with data storage systems.
LO2. Explain and apply foundational techniques of data analysis to business problems.
LO3. Categorise business problems in order to select appropriate data analysis techniques and tools.
LO4. Interpret and evaluate the outputs of data analysis techniques and tools.
LO5. Evaluate data science capabilities of businesses and apply data science process models.
LO6. Identify and assess ethical issues associated with data driven decision making.
LO7. Communicate effectively with technical and non-technical audiences.

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.

Minor sessional updates.

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) and automated writing tools

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

BUSS6002: Data Science in Business

Semester 1, 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) and automated writing tools

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