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Data Science Project Pt1 (MAST90106)
Graduate courseworkPoints: 12.5On Campus (Parkville)
Overview
| Availability | Semester 1 |
|---|---|
| Fees | Look up fees |
This capstone project will provide the culmination of the Master of Data Science degree. It will apply the skills developed during the degree to a practical problem of relevance to science, industry, commerce or society in general. Students will work in teams under only general guidance from staff members. Students will complete diaries to log their work on the project so that the extent of their contribution to group projects can be determined. In the first part of the project students will complete a literature review and a plan for their project.
Intended learning outcomes
On completion of this project, students should be able to:
- apply contemporary data science techniques to a practical problem.
- project manage as part of a team in order to design the program of work, complete the analysis of project results and compile the project report
- present results at a career-ready level
Generic skills
In addition to learning specific skills that will assist students in their future careers in data science, they should progressively acquire generic skills from this subject that will assist them in any future career path. These include:
- problem-solving skills: the ability to engage with unfamiliar problems and identify relevant solution strategies;
- analytical skills: the ability to construct and express logical arguments and to work in abstract or general terms to increase the clarity and efficiency of analysis;
- collaborative skills: the ability to work in a team;
- time management skills: the ability to meet regular deadlines while balancing competing commitments.
- the ability to work in a team environment.
Last updated: 31 January 2024
Eligibility and requirements
Prerequisites
25 credit points from
| Code | Name | Teaching period | Credit Points |
|---|---|---|---|
| COMP20008 | Elements of Data Processing |
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
|
12.5 |
| COMP90049 | Introduction to Machine Learning |
Semester 2 (On Campus - Parkville)
Semester 1 (On Campus - Parkville)
|
12.5 |
| MAST90104 | A First Course In Statistical Learning | Semester 2 (On Campus - Parkville) |
25 |
| INFO90002 | Database Systems & Information Modelling |
Semester 2 (On Campus - Parkville)
Semester 1 (On Campus - Parkville)
|
12.5 |
OR
Admission into:
• Master of Data Science (MC-DATASC) - Data Science Background Stream
Corequisites
Non-allowed subjects
Inherent requirements (core participation requirements)
The University of Melbourne is committed to providing students with reasonable adjustments to assessment and participation under the Disability Standards for Education (2005), and the Assessment and Results Policy (MPF1326). Students are expected to meet the core participation requirements for their course. These can be viewed under Entry and Participation Requirements for the course outlines in the Handbook.
Further details on how to seek academic adjustments can be found on the Student Equity and Disability Support website: http://services.unimelb.edu.au/student-equity/home
Last updated: 31 January 2024
Assessment
| Description | Timing | Percentage |
|---|---|---|
A literature review and project plan completed by the research group
| End of first semester | N/A |
A written report totaling 6,000 words, or equivalent completed by the research group
| End of second semester | 70% |
Group oral presentation
| End of second semester | 30% |
Additional details
The assessment requirements are applicable to the entire 25 point Research Project.
Individual's contribution to the project measured by a peer assessment factor. (e.g. 0.5 for ½ contribution, 1 for full contribution). To justify the factor students will be expected to compile an individual portfolio including a journal, meeting summaries/minutes, their assigned role, and evidence of their contribution through draft reports and an assessment of the roles of others in the group.
The individual student's mark comes from the group mark multiplied by the peer assessment factor.
Last updated: 31 January 2024
Dates & times
- Semester 1
Coordinators Michael Kirley, Feng Liu, and Joyce Zhang Mode of delivery On Campus (Parkville) Contact hours Up to 10 hours comprising one initial 2 hour workshop to form groups and discuss the form of the project, one 1 hour workshop on research ethics, one 2 hour workshop towards the end of the first semester to discuss the project’s progress and up to 5 hours informal contact with the subject coordinator. Total time commitment 170 hours Teaching period 26 February 2024 to 26 May 2024 Last self-enrol date 8 March 2024 Census date 3 April 2024 Last date to withdraw without fail 3 May 2024 Assessment period ends 21 June 2024 Semester 1 contact information
Time commitment details
170 hours
What do these dates mean
Visit this webpage to find out about these key dates, including how they impact on:
- Your tuition fees, academic transcript and statements.
- And for Commonwealth Supported students, your:
- Student Learning Entitlement. This applies to all students enrolled in a Commonwealth Supported Place (CSP).
Subjects withdrawn after the census date (including up to the ‘last day to withdraw without fail’) count toward the Student Learning Entitlement.
Last updated: 31 January 2024
Further information
- Texts
Prescribed texts
There are no specifically prescribed or recommended texts for this subject.
- Related Handbook entries
This subject contributes to the following:
Type Name Course Master of Data Science - Available to Study Abroad and/or Study Exchange Students
This subject is available to students studying at the University from eligible overseas institutions on exchange and study abroad. Students are required to satisfy any listed requirements, such as pre- and co-requisites, for enrolment in the subject.
Last updated: 31 January 2024