DATA3888: Data Science Capstone Semester 1, 2024

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DATA3888: Data Science Capstone

Semester 1, 2024

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

Unit code DATA3888

Academic unit Mathematics and Statistics Academic Operations

Prerequisites DATA2001 or DATA2901 or DATA2002 or DATA2902 or STAT2912 or STAT2012

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.

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.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

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.

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.

Outcomes

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.