DATA4103: Semester 2, 2023

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Units DATA4103 Semester 2 2023 [Normal day]

Unit outline_

DATA4103: Data Science Honours Project A

Semester 2, 2023 [Normal day] - Camperdown/Darlington, Sydney

Overview

Independent research can be a life changing experience. In this unit you will complete a research project in the discipline of Data Science. Together with your supervisor, you will identify a suitable research problem and develop a strategy to address it. This may include synthesising and generalising results from the statistical literature, developing novel methodologies or attacking a problem in applied statistics in an innovative way. In terms of assessment, you will communicate the research plan and findings via an oral presentation and a 40 to 60 page honours thesis. Successful completion of your Honours will clearly demonstrate that you have mastered significant research and professional skills for either undertaking a PhD or any variety of future careers.

Unit details and rules

Academic unit	Mathematics and Statistics Academic Operations
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Clara Grazian, clara.grazian@sydney.edu.au

Type	Description	Weight	Due	Length
Honours thesis	Oral Presentation Presentation of research from honours thesis.	10%	Week 10 Due date: 13 Oct 2023 at 14:00	25 minutes (+5 minutes questions)
Outcomes assessed:
Honours thesis	Honours Thesis Submission of an honours thesis supervised over two semesters.	90%	Week 13 Due date: 30 Oct 2023 at 17:00	2 semesters ~60 pages
Outcomes assessed:

Assessment summary

Preparation of an honours thesis.

Assessment criteria

Result Name	Mark Range	Description
First class – Outstanding	95-100	Outstanding First-Class quality of clear Medal standard
First class – Very high standard	90-94	Very high standard of work similar to above but overall performance is borderline for award of a Medal.
First class	80-89	Clear First Class quality, showing a command of the field both broad and deep, with the presentation of some novel insights.
Second class, first division	75-79	Second class Honours, first division – student will have shown a command of the theory and practice of the discipline.
Second class, second division	70-74	Second class Honours, second division – student is proficient in the theory and practice of their discipline but has not developed complete independence of thought, practical mastery or clarity of presentation.
Third class	65-69	Third class Honours – performance indicates that the student has successfully completed the work, but at a standard barely meeting Honours criteria.
Not pass	0-64	The student's performance in fourth year is not such as to justify the award of Honours.

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:

1 mark reduced per day.

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	Learning outcomes

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.

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. LO1. study and work independently and in teams
LO2. LO2. manage schedules and resources
LO3. LO3. propose research that will increase knowledge in the area of interest
LO4. LO4. know the ethical principles of research and adhere to them
LO5. LO5. understand quantitative and qualitative approaches to research
LO6. LO6. conduct a literature review and write it up
LO7. LO7. investigate a topic under supervision, including data collection and analysis
LO8. LO8. write a research article that compiles all aspects of the study
LO9. LO9. demonstrate the ability to orally present ideas and research findings and respond to questions.

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

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

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

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