Outline

Overview

In our ever-changing world, we are facing a new data-driven era where the capability to efficiently combine and analyse large data collections is essential for informed decision making in business and government, and for scientific research. Data science is an emerging interdisciplinary field with its focus on high performance computation and quantitative expression of the confidence in conclusions, and the clear communication of those conclusions in different discipline context. This unit is our capstone project that presents the opportunity to create a public data product that can illustrate the concepts and skills you have learnt in this discipline. In this unit, you will have an opportunity to explore deeper disciplinary knowledge; while also meeting and collaborating through project-based learning. The capstone project in this unit will allow you to identify and place the data-driven problem into an analytical framework, solve the problem through computational means, interpret the results and communicate your findings to a diverse audience. All such skills are highly valued by employers. This unit will foster the ability to work in an interdisciplinary team, to translate problem between two or more disciplines and this is essential for both professional and research pathways in the future.

Unit details and rules

Academic unit	Mathematics and Statistics Academic Operations
Credit points	6
Prerequisites ?	DATA2001 or DATA2901 or DATA2002 or DATA2902 or STAT2912 or STAT2012
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Jean Yang, jean.yang@sydney.edu.au

Type	Description	Weight	Due	Length
Assignment	Reflection tasks Submitted via Canvas (Final week)	10%	Multiple weeks	Final reflection
Outcomes assessed:
Assignment	Teamwork self- and peer-evaluation Attendance, meeting notes and peer-evaluation to be submitted on CANVAS.	15%	Multiple weeks	Online survey
Outcomes assessed:
Assignment	Joint project report Written task	30%	STUVAC	10 pages
Outcomes assessed:
Assignment	Computer assignment Submitted via Canvas	15%	Week -05	5 pages
Outcomes assessed:
Tutorial quiz	Discipline quiz (computing) In-class test completed during lecture time	15%	Week 08	60 minutes
Outcomes assessed:
Presentation	Interdisciplinary presentation and demonstration Oral presentation during computer lab class	15%	Week 13	5 - 8 minutes including demonstration
Outcomes assessed:
= group assignment ?

Assessment summary

Below are brief assessment details. Further information can be found in the Canvas site for this unit.

Disciplinary component

Discipline assignment: A take-home Rmarkdown assignment.
Discipline quiz: In this quiz, the student will demonstrate their ability to code and perform data analysis in R.

Interdisciplinary component

Interdisciplinary presentation and demonstration: Students must present and demonstrate their project as a group in a mini-conference format.
Project report: This will summarise the outcomes from the group interdisciplinary project.
Reflection tasks: There will be three quizzes (formative) and a final reflection to assess the student’s interaction and insight into interdisciplinary project work.
Teamwork self- and peer-evaluation: This includes attendance for group work, satisfactory weekly progress and completion of two peer-evaluation surveys.

Assessment criteria

Assessment grading

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	At HD level, you achieve the unit learning outcomes to an exceptional standard. A ‘High Distinction’ reflects your exceptional ability to apply comprehensive knowledge, analytical, communication, and collaborative skills in diverse contexts to synthesise multiple advanced insights and produce original solutions for highly complex problems.
Distinction	75 - 84	At D level, you achieve the unit learning outcomes to an excellent standard. A ‘Distinction’ reflects your excellent ability to apply well-developed knowledge, analytical, communication, and collaborative skills in diverse contexts to synthesise multiple insights to produce original solutions for complex problems.
Credit	65 - 74	At CR level, you achieve the unit learning outcomes to a good standard. A ‘Credit’ reflects your ability to apply broad knowledge, analytical, communication, and collaborative skills in a variety of contexts to synthesise insights and produce adequate solutions for routine problems.
Pass	50 - 64	At PS level, you achieve the unit learning outcomes to a proficient standard. A ‘Pass’ reflects your ability to apply threshold knowledge, analytical, communication, and collaborative skills in some but not all contexts to combine insights and produce basic solutions for routine problems.
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.

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.

Weekly schedule

WK	Topic	Learning activity
Week 01	Principals of data science	Lecture (2 hr)
Week 01	Interdisciplinary projects overview and revision of data analytics	Computer laboratory (3 hr)
Week 02	Week 2 workshop - Evaluation strategies in machine learning	Lecture and tutorial (2 hr)
Week 02	Discipline computer labs	Computer laboratory (3 hr)
Week 03	Week 3 workshop	Lecture and tutorial (2 hr)
Week 03	Discipline computer labs	Computer laboratory (3 hr)
Week 04	Week 4 workshop	Lecture and tutorial (2 hr)
Week 04	Discipline computer labs	Computer laboratory (3 hr)
Week 05	Week 5 workshop	Lecture and tutorial (2 hr)
Week 05	Team formation	Computer laboratory (3 hr)
Week 06	Week 6 workshop	Seminar (2 hr)
Week 06	Develop an integrated experimental–statistical solution to the interdisciplinary problem.	Computer laboratory (3 hr)
Week 07	Develop an integrated experimental–statistical solution to the interdisciplinary problem	Computer laboratory (3 hr)
Week 08	Develop an integrated experimental–statistical solution to the interdisciplinary problem	Computer laboratory (3 hr)
Week 09	Develop an integrated experimental–statistical solution to the interdisciplinary problem	Computer laboratory (3 hr)
Week 10	Develop an integrated experimental–statistical solution to the interdisciplinary problem	Computer laboratory (3 hr)
Week 11	Develop an integrated experimental–statistical solution to the interdisciplinary problem	Computer laboratory (3 hr)
Week 12	Develop an integrated experimental–statistical solution to the interdisciplinary problem	Computer laboratory (3 hr)

Attendance and class requirements

This is an interdisciplinary project unit and attendance at all “computer laboratory” sessions involving group work (wks 5 – 13) are required.

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 is no prescribed textbook. Some of the reading materials from previous units may be of use

R Markdown: The Definitive Guide by Yihui Xie, J.J. Allaire, Garrett Grolemund.
Modern Data Science with R by Baumer, Kaplan and Horton.
Data Science in R: A Case Studies Approach to Computational Reasoning and Problem Solving by Deborah Nolan and Duncan Temple Lang.
Happy Git and GitHub for the useR by Jenny Bryan.

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.

At the completion of this unit, you should be able to:

LO1. study the interdisciplinary data-driven problem and formulate it into an analytical framework
LO2. apply disciplinary knowledge to solve problems in an interdisciplinary context
LO3. create an investigation strategy, explore solutions, discuss approaches and predict outcomes
LO4. analyse data using modern information technology and digital skills
LO5. demonstrate integrity, confidence, personal resilience and the capacity to manage challenges, both individually and in teams
LO6. collaborate with diverse groups and across cultural boundaries to develop solution(s) to the project problems
LO7. communicate project outcomes effectively to an interdisciplinary audience.

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.

We received thoughtful and constructive feedback from the students who took DATA3888 in Semester 1, 2021. In general, most students appreciate the support from the teaching team and the chance to apply their learning in a real-world context. Students prefer to like more guidance with the project unit and we have shifted to increase the use of individual group-specific CANVAS pages to ensure more transparent communication between the students and the teaching team. Based on the feedback, we will continue to offer exciting real-world examples with different partners.

Additional information

Work, health and safety

We are governed by the Work Health and Safety Act 2011, Work Health and Safety Regulation 2011 and Codes of Practice. Penalties for non compliance have increased. Everyone has a responsibility for health and safety at work. The University’s Work Health and Safety policy explains the responsibilities and expectations of workers and others, and the procedures for managing WHS risks associated with University activities.

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

Assessment grading

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

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

Work, health and safety

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect

Current students

DATA3888: Data Science Capstone

Semester 1, 2022 [Normal day] - Remote

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Assessment grading

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

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

Work, health and safety

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect