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Unit of study_

DATA3888: Data Science Capstone

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.

Details

Academic unit Mathematics and Statistics Academic Operations
Unit code DATA3888
Unit name Data Science Capstone
Session, year
? 
Semester 1, 2021
Attendance mode Normal day
Location Camperdown/Darlington, Sydney
Credit points 6

Enrolment rules

Prohibitions
? 
None
Prerequisites
? 
DATA2001 or DATA2901 or DATA2002 or DATA2902 or STAT2912 or STAT2012
Corequisites
? 
None
Available to study abroad and exchange students

Yes

Teaching staff and contact details

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: LO5 LO6
Assignment Teamwork self- and peer-evaluation
Attendance, meeting notes and peer-evaluation to be submitted on CANVAS.
15% Multiple weeks Online survey
Outcomes assessed: LO5 LO7 LO6
Assignment group assignment Joint project report
Written task
30% STUVAC 10 pages
Outcomes assessed: LO3 LO2 LO1 LO7 LO6 LO5 LO4
Assignment Computer assignment
Submitted via Canvas
15% Week -05 5 pages
Outcomes assessed: LO1 LO2 LO3 LO7 LO4 LO5
Tutorial quiz Discipline quiz (computing)
In-class test completed during lecture time
15% Week 08 60 minutes
Outcomes assessed: LO3 LO4
Presentation group assignment Interdisciplinary presentation and demonstration
Oral presentation during computer lab class
15% Week 13 5 - 8 minutes including demonstration
Outcomes assessed: LO3 LO7 LO6 LO5 LO4
group assignment = group assignment ?

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 sydney.edu.au/students/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.

Special consideration

If you experience short-term circumstances beyond your control, such as illness, injury or misadventure or if you have essential commitments which impact your preparation or performance in an assessment, you may be eligible for special consideration or special arrangements.

Academic integrity

The Current Student website provides information on academic honesty, academic dishonesty, 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 dishonesty or plagiarism seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic dishonesty. If such matches indicate evidence of plagiarism or other forms of dishonesty, your teacher is required to report your work for further investigation.

WK Topic Learning activity Learning outcomes
Week 01 Principals of data science Lecture (2 hr) LO1 LO2
Interdisciplinary projects overview and revision of data analytics Computer laboratory (3 hr) LO1 LO6
Week 02 Week 2 workshop - Evaluation strategies in machine learning Lecture and tutorial (2 hr) LO2 LO3 LO4
Discipline computer labs Computer laboratory (3 hr) LO2 LO3 LO4
Week 03 Week 3 workshop Lecture and tutorial (2 hr) LO2 LO3 LO4
Discipline computer labs Computer laboratory (3 hr) LO2 LO3 LO4 LO7
Week 04 Week 4 workshop Lecture and tutorial (2 hr) LO2 LO3 LO4
Discipline computer labs Computer laboratory (3 hr) LO2 LO3 LO4
Week 05 Week 5 workshop Lecture and tutorial (2 hr) LO2 LO3 LO4
Team formation Computer laboratory (3 hr) LO6
Week 06 Week 6 workshop Seminar (2 hr) LO1 LO2 LO3 LO5
Develop an integrated experimental–statistical solution to the interdisciplinary problem. Computer laboratory (3 hr) LO2 LO3 LO4 LO5 LO6
Week 07 Develop an integrated experimental–statistical solution to the interdisciplinary problem Computer laboratory (3 hr) LO2 LO3 LO4 LO5 LO6
Week 08 Develop an integrated experimental–statistical solution to the interdisciplinary problem Computer laboratory (3 hr) LO2 LO3 LO4 LO5 LO6
Week 09 Develop an integrated experimental–statistical solution to the interdisciplinary problem Computer laboratory (3 hr) LO2 LO3 LO4 LO5 LO6
Week 10 Develop an integrated experimental–statistical solution to the interdisciplinary problem Computer laboratory (3 hr) LO2 LO3 LO5 LO6 LO7
Week 11 Develop an integrated experimental–statistical solution to the interdisciplinary problem Computer laboratory (3 hr) LO2 LO3 LO5 LO6 LO7
Week 12 Develop an integrated experimental–statistical solution to the interdisciplinary problem Computer laboratory (3 hr) LO2 LO3 LO5 LO6 LO7

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 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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9
LO1         
LO2         
LO3         
LO4         
LO5         
LO6         
LO7         
We received thoughtful and constructive feedback from the students who took DATA3888 in Semester 1, 2020. In general, most students appreciate the support from the teaching team and the chance to apply their learning in a real-world context. Students also feel the workload is very high and like more guidance and clarity on expectation on the assessment tasks. Based on the feedback, we will continue to offer exciting real-world examples with different partners. We have reduced the amount of assessment for 2021 especially towards the second half of the semesters and we will increase more support and focus on the interdisciplinary component.

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.